A Comprehensive Survey on Evaluating Large Language Model Applications in the Medical Industry
Yining Huang Keke Tang Meilian Chen

A self-consistent model for dust settling and the vertical shear instability in protoplanetary disks
    Satoshi Okuzumi1
    
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Since the inception of the Transformer architecture in 2017, Large Language Models (LLMs) such as GPT and BERT have evolved significantly, impacting various industries with their advanced capabilities in language understanding and generation. These models have shown potential to transform the medical field, highlighting the necessity for specialized evaluation frameworks to ensure their effective and ethical deployment. This comprehensive survey delineates the extensive application and requisite evaluation of LLMs within healthcare, emphasizing the critical need for empirical validation to fully exploit their capabilities in enhancing healthcare outcomes.

Our survey is structured to provide an in-depth analysis of LLM applications across clinical settings, medical text data processing, research, education, and public health awareness. We begin by exploring the roles of LLMs in different medical applications, detailing how they are evaluated based on their performance in tasks such as clinical application, medical text data processing, information retrieval, data analysis, medical scientific writing, educational content generation etc. The subsequent sections delve into the methodologies employed in these evaluations, discussing the benchmarks and metrics used to assess the models' effectiveness, accuracy, and ethical alignment.

Through this survey, we aim to equip healthcare professionals, researchers, and policymakers with a comprehensive understanding of the potential strengths and limitations of LLMs in medical applications. By providing detailed insights into the evaluation processes and the challenges faced in integrating LLMs into healthcare, this survey seeks to guide the responsible development and deployment of these powerful models, ensuring they are harnessed to their full potential while maintaining stringent ethical standards.









§ INTRODUCTION & BACKGROUND

Since the introduction of the Transformer architecture by Google's team in 2017<cit.>, the field of natural language processing has entered a new era. The innovation of the Transformer lies in its use of self-attention mechanisms, which significantly improved the model's ability to handle long-range dependencies, setting the foundation for numerous subsequent language models. Following this, OpenAI released GPT (Generative Pre-trained Transformer)<cit.> in 2018, which utilized a pre-training and fine-tuning approach. By undergoing unsupervised learning on vast amounts of text data and then fine-tuning on specific tasks, GPT significantly enhanced performance across a variety of natural language processing tasks. Google's BERT (Bidirectional Encoder Representations from Transformers)<cit.> model further refined pre-training methods by training in a bidirectional manner, enhancing the contextual understanding of text. The release of GPT-2<cit.> and GPT-3<cit.> marked significant increases in model size and generative capabilities. Particularly, GPT-3, with its 175 billion parameters, became known for producing text nearly indistinguishable from human writing at the time. Following this, InstructGPT<cit.> and ChatGPT<cit.> were optimized for following user instructions, further improving interaction quality and practicality with humans. In 2023, OpenAI launched GPT-4<cit.>, an even larger and smarter model capable of handling more complex language understanding and generation tasks, demonstrating superior performance on multiple dimensions. Additionally, innovations in models continued to evolve, such as Google's Gemini<cit.> model, which is optimized for specific information retrieval tasks. In the open-source domain, models like LLaMA<cit.>, OPT<cit.>, and others provide the research community and industry with more flexibility and accessibility. These models aim to offer performance competitive with large proprietary models while lowering the barriers to usage and research. Notably, models like Gemma<cit.> and Meta’s OPT (Open Pre-trained Transformer) have gained popularity for their open accessibility and adaptability to various languages and tasks, fostering broader experimentation and development in the field. Through these developments, LLMs have not only demonstrated powerful capabilities in understanding, content generation, reasoning, and tool usage but have also opened up new possibilities for AI applications, especially in domains requiring deep semantic understanding and interaction.

Following the foundational technologies like Transformer, GPT, and BERT, Large Language Models (LLMs) have found extensive applications across various industries, demonstrating their adaptability and transformative potential. The educational sector is witnessing the emerging role of LLMs as teacher assistants and feedback providers. For instance, ChatGPT has been tested as an automated coach, analyzing classroom interactions to provide feedback, though it sometimes lacks novelty in its suggestions<cit.>. Additionally, LLMs have proven capable of providing detailed and coherent feedback to students, outperforming human instructors in clarity and detail<cit.>. In the legal field, LLMs like GPT-3 have been tailored to perform tasks requiring legal reasoning, showing improvements in tasks like the Japanese Bar exam when using specialized prompting techniques<cit.>. Moreover, GPT-4 has been evaluated for its ability to generate accurate explanations of legal terms, further enhanced by integrating contextual data from case law<cit.>. LLMs have also significantly impacted software development. They are used in detecting vulnerabilities in software, showing superior performance in identifying issues in source code over traditional models<cit.>. Furthermore, models like the Programmer's Assistant allow for conversational interactions, improving software development processes by integrating contextually aware dialogues<cit.>. In the financial sector, the XuanYuan 2.0 model demonstrates how LLMs can be specialized for financial discussions in Chinese, providing domain-specific responses informed by hybrid-tuning methods<cit.>. Research also shows that LLMs can perform sophisticated financial reasoning, with capabilities emerging significantly at certain model sizes and improving with instruction-tuning<cit.>. These examples underline the versatility of LLMs in adapting to diverse professional and academic needs, setting the stage for deeper integration into domain-specific applications. Applications of LLMs in the medical field, which are a significant focus of this survey, will be discussed in detail later in this survey and are not elaborated here.

Large Language Models (LLMs) have been extensively deployed across various industries, yet they come with inherent challenges that could hinder their effectiveness and ethical deployment. Issues such as the lack of transparency in deep learning, probabilistic rather than deterministic outputs, frequent hallucinations, limited reasoning capabilities, and potential biases in knowledge coverage necessitate a rigorous evaluation of LLMs in practical settings to ensure their reliability, safety, efficiency, and ethical integrity. Several recent studies illustrate the depth and diversity of evaluations needed to address these challenges. <cit.> evaluates ChatGPT's performance on logical reasoning, non-textual reasoning, and commonsense reasoning tasks. It demonstrates that while ChatGPT excels in multitask and multilingual capabilities, it struggles with reliability, often producing hallucinations and showing varying success in different reasoning categories. <cit.> evaluates the ability of language models to identify toxic content in text using a generative zero-shot prompt-based method. It explores the model's self-diagnosing capabilities and discusses the ethical implications of such methods, highlighting both quantitative and qualitative strengths in toxicity detection across social media datasets. In <cit.>, the authors developed a framework to evaluate superhuman machine learning models by focusing on logical consistency in decision-making, rather than direct correctness. They applied this method to tasks like chess evaluation, forecasting, and legal judgments, revealing logical inconsistencies in models, including GPT-4, even in scenarios where traditional ground truth is absent. <cit.> evaluates the capability of large language models (LMs) to memorize factual knowledge by conducting extensive knowledge probing experiments using 10 models and 4 augmentation methods on a new open-domain QA dataset named PopQA. The findings reveal that while LMs struggle with less popular factual knowledge and scaling does not substantially improve long-tail memorization, retrieval-augmented LMs significantly outperform larger, non-augmented models in terms of efficiency and factual accuracy. <cit.> evaluates ChatGPT's cultural adaptation by analyzing its responses to culturally specific prompts. The study reveals that while ChatGPT aligns well with American cultural norms, it demonstrates limited effectiveness in adapting to other cultural contexts, often flattening out cultural differences when prompted in English. <cit.> evaluates the inherent biases in large-scale language models like ChatGPT, discussing their origins in training data, model design, and algorithmic constraints. It emphasizes the ethical challenges posed by biased outputs and reviews current methods for identifying, quantifying, and mitigating these biases to foster the development of more responsible and ethical AI systems.

Recent reviews in the domain of LLM evaluation have shed light on diverse approaches and methodologies. <cit.> highlights the categorization of LLM evaluations into knowledge and capability, alignment, and safety evaluations, emphasizing the construction of comprehensive evaluation platforms to guide responsible development and maximize societal benefits. <cit.> focuses on the critical task of aligning LLMs with human intentions, covering dimensions such as reliability, safety, and fairness. It presents a detailed analysis of trustworthiness across multiple sub-categories to address alignment challenges in real-world applications. <cit.> discusses the extensive evaluation needs of LLMs across various domains, including healthcare and education. It advocates for a broad evaluation approach that addresses the societal impacts and practical integration of LLMs, promoting ongoing assessment to refine these technologies. Despite these insightful reviews, there remains a notable gap in the depth and breadth of evaluations specifically in the healthcare domain. Although existing reviews<cit.> touch on medical applications, they lack a deep dive into critical areas such as clinical applications, medical data governance, research (both basic and clinical), medical education, and public health education. The complexity and sensitivity of these areas demand specialized evaluation frameworks that can comprehensively assess the performance of LLMs in healthcare. Thus, there is a pressing need for a specialized review that focuses on the evaluation of LLMs within the healthcare vertical. Such a review should aim to provide healthcare practitioners, researchers, and policymakers with detailed insights into the application and assessment of LLMs, ensuring they are equipped with the knowledge to implement and evaluate these technologies effectively in various medical settings. This would not only aid in harnessing the potential of LLMs to improve healthcare outcomes but also ensure that their deployment is governed by rigorous ethical standards and practical efficacy.

This review aims to provide a comprehensive evaluation of Large Language Models (LLMs) within the medical sector from the perspectives of healthcare professionals, researchers, and patients. It is designed to serve as a reference for integrating LLM technology into various medical applications, facilitating informed decision-making regarding their implementation and evaluation. We will delve into the use of LLMs in clinical applications, data processing, research support, education, and public health awareness. The review will cover detailed evaluation methodologies used in the medical field, including benchmarks, assessment entities, evaluation subjects, comparative experiments, and detailed procedural insights etc. This structured approach will aid stakeholders in understanding the spectrum of evaluating LLM applications within healthcare, ensuring that these innovations are effectively integrated.





§ TAXONOMY AND STRUCTURE OF THE SURVEY

Following the introduction and background that highlighted the pressing need for specialized evaluation of LLMs in the medical sector, this chapter outlines the structure of our comprehensive review. This review is designed to navigate through the multifaceted applications and evaluations of LLMs within healthcare, shedding light on both their potential and the intricacies of their implementation.

Section 3 breaks down into specific application fields and scenarios where LLMs are making a mark in healthcare.



  * In 3.1 Evaluations Based on Application Fields and Scenarios, we focus on bringing introduction of evaluation of LLMs' application on different aspects of medical field, including clinical application, medical text data processing, medical researches, medical education and public awareness. 




  * In 3.1.1 Evaluations of Department and Specific Disease’s Clinical Application, we begin by dissecting evaluations across a spectrum of clinical applications. 1) For general integrative Applications, we assess how LLMs perform in generic clinical settings, providing a foundational understanding of their effectiveness across various medical departments without focusing on specific diseases. 2) For specialized departments and disease applications, we explore the use of LLMs in specialized medical fields such as endocrinology and ophthalmology, detailing how these technologies meet the unique demands of specific health conditions. 3) For supportive departments applications, the focus then shifts to supportive departments such as radiology and emergency care, where LLMs assist in critical diagnostic and procedural tasks.


  * 3.1.2 Medical Text Data Processing Application Evaluations delves into the realm of LLM applications in processing medical text data, illustrating how these models are evaluated across a variety of data processing tasks. The evaluations focus on several critical aspects such as relation extraction (RE), named entity recognition (NER), and question answering (QA), which are pivotal for enhancing the utility of medical text data like clinical notes and electronic health records (EHRs). Researches in this subsection demonstrates LLMs' capacity to improve biomedical NLP tasks. These advancements highlight the models' potential in transforming how medical data is processed, making it more accessible and actionable for healthcare providers. Moreover, comprehensive benchmark studies assess the performance of models like ChatGPT across different types of medical text, including clinical trial descriptions and biomedical corpus. These studies use a range of metrics such as entity-level F1 scores for NER and accuracy for QA tasks to evaluate the models. The results indicate effective, though sometimes limited, capabilities in biomedical text understanding, suggesting areas for further improvement and adaptation. The evaluations discussed provide practitioners and researchers with examples of how LLMs can be applied in data processing scenarios within the healthcare sector. They offer insights into application patterns and how to evaluate these applications, helping users understand the operational effectiveness and practical limitations of LLMs in real-world settings. These insights are crucial for guiding further development and refinement of LLM technologies to better serve the needs of the medical community.


  * In 3.1.3 Medical Research Application Evaluations, we examine the impact of Large Language Models (LLMs) in medical research, highlighting their utility in three key areas: 1) Retrieval & Screening: LLMs enhance biomedical information retrieval and article screening, crucial for systematic reviews. Evaluations focus on the models' accuracy, sensitivity, and specificity in extracting relevant information efficiently. 2) Modeling & Analysis: LLMs are employed in modeling biological processes and analyzing complex datasets, aiding in hypothesis generation and disease modeling. Their effectiveness is measured through precision, recall, and field-specific metrics to ensure accuracy and applicability. 3) Writing & Reference Generation: LLMs assist in drafting scientific papers and generating references, automating routine tasks like citation formatting and literature summarization. Performance is evaluated based on accuracy, coherence, and the appropriateness of content and references.


  * 3.1.4 Medical Education & Public Awareness Application Evaluations highlights the role of LLMs in medical education to medical students and professionals and enhancing public health awareness.
1) Medical Education: LLMs are increasingly used to augment medical education by providing dynamic learning tools that enhance understanding and retention. They support various educational activities, from generating interactive content and case studies to facilitating exam preparation and continuous professional development. Evaluations focus on the models' ability to deliver accurate, relevant, and pedagogically sound content, assessing their impact on learning outcomes and educational efficacy. 2) Public Awareness Application: In the realm of public health awareness, LLMs help disseminate vital health information, aiding in disease prevention and health promotion. They are employed to evaluate the quality and reliability of medical information available to the public, ensuring that health communications are both accessible and accurate. The effectiveness of these applications is measured by their ability to enhance public understanding of complex health issues and support informed decision-making.







  * In 3.2 Comprehensive Discussion on Evaluation Methods and Indicators, we will discuss the methodologies employed in evaluating LLMs, addressing questions about the nature of LLMs used (commercial vs. self-developed), the types of inputs (public datasets or proprietary data), the specifics of the prompts and tasks, and the benchmarks against which outputs are measured. This section aims to illuminate the depth and breadth of evaluation practices, highlighting the diversity of criteria from technical performance to ethical considerations.


  * The 3.3 Benchmarks will provide an overview of the benchmarks used in the studies we review, offering a categorized description of these benchmarks and discussing their relevance and applicability in medical settings.



Section 4 will introduce the broader challenges and specific issues arising from the evaluation of LLMs in healthcare. Specifically, we will analyze the technical, ethical, and legal challenges encountered in these evaluations, emphasizing the need for more rigorous and detailed frameworks to ensure the efficacy of LLM applications in healthcare. This section will also discuss the potential strategies for improving evaluation frameworks, methods, and metrics to better address these challenges, ensuring that LLMs can be effectively integrated into medical practice.




§ CURRENT STATE OF LLM APPLICATION EVALUATIONS IN THE MEDICAL FIELD



 §.§ Evaluations Based on Application Fields and Scenarios



  §.§.§ Evaluations of Department and Specific Disease's Clinical Application


General Integrative Applications: 

This part reviews studies evaluating Large Language Models (LLMs) in various clinical processes without distinguishing between different departments or specific diseases, offering a general perspective on their application in common clinical scenarios. Through these evaluations, we explore LLMs' performance on accuracy, bias, and applicability across the medical field. The selected studies provide a broad overview of LLMs' capabilities and areas for refinement in healthcare.

<cit.> evaluated the ChatGPT(GPT-3.5) across various clinical tasks, including differential diagnoses, diagnostic testing, final diagnosis, and clinical management. The evaluation encompassed 36 published clinical vignettes from the Merck Sharpe & Dohme (MSD) Clinical Manual, focusing on accuracy measures derived from the proportion of correct responses. ChatGPT achieved an overall accuracy of 71.7%, with the highest accuracy in making a final diagnosis (76.9%) and the lowest in generating an initial differential diagnosis (60.3%). Performance was compared across patient demographics and case acuity but showed no significant variance, indicating ChatGPT's consistent applicability across different clinical scenarios without bias towards patient age or gender.

In a comprehensive study assessing the integration of Large Language Models (LLMs) in the medical industry, researchers<cit.> focused on evaluating GPT-4's application across various clinical tasks. The evaluation encompassed medical education, diagnostic reasoning, clinical plan generation, and subjective patient assessments. Utilizing the Azure OpenAI interface for experimentation, the study specifically tested GPT-4's ability to encode racial and gender biases and the implications of such biases on clinical care. Results indicated that GPT-4 did not accurately model the demographic diversity of medical conditions, often reinforcing stereotypes in clinical vignettes. Differential diagnoses and treatment plans exhibited significant biases, associating demographic attributes with stereotypical diseases and recommendations for more expensive procedures. These findings underline the necessity for thorough bias assessments and mitigation strategies before deploying LLMs like GPT-4 in clinical settings, ensuring equitable healthcare delivery.

<cit.> evaluates the Pathways Language Model (PaLM) and its instruction-tuned variant, Flan-PaLM, across multiple medical question-answering tasks. The clinical tasks assessed include professional medicine, medical research, and consumer medical queries, encompassing areas like diagnostic reasoning and clinical knowledge application. Evaluation metrics spanned factuality, comprehension, reasoning, potential harm, and bias, alongside accuracy on multiple-choice datasets such as MedQA, MedMCQA, PubMedQA, and MMLU clinical topics. Flan-PaLM achieved state-of-the-art accuracy, notably 67.6% on MedQA, surpassing previous benchmarks by over 17%. Human evaluations, however, highlighted areas for improvement, particularly in aligning model responses with clinical expertise and minimizing potential misinformation or harm.

<cit.> includes a study that evaluated the application of ChatGPT, in the medical field. The study focused on assessing chatbot-generated responses to physician-developed medical queries across 17 specialties, encompassing both binary (yes/no) and descriptive questions that relate to clinical tasks such as disease identification and management. The evaluation metrics involved accuracy and completeness of responses, using a 6-point Likert scale for accuracy (1 being completely incorrect to 6 being completely correct) and a 3-point Likert scale for completeness (1 being incomplete to 3 being complete with additional context). The results revealed median accuracy scores ranging from 5.0 to 6.0 across different question difficulties, indicating a level of performance that was between mostly correct to completely correct, and a median completeness score of 3.0, reflecting comprehensive answers. This study underscores the potential of ChatGPT in providing largely accurate information for a range of medical queries, yet highlights the necessity for further improvements and validation for clinical use.

Specialized Departments and Disease Applications: 


In the following part, we delve into the evaluations of LLMs across various specialized medical departments and specific disease applications. The research spans a diverse range of medical fields, from endocrinology, addressing diabetes management, to ophthalmology, with a focus on ocular conditions, and extends into orthopedics, mental health, and reproductive medicine among others. Each study offers a unique perspective on the potential and challenges of implementing LLMs in these distinct medical domains.

<cit.> evaluates ChatGPT and GPT 4.0 in specialized medical fields, focusing on nutritional management for patients with type 2 diabetes mellitus (T2DM) in endocrinology. The clinical tasks assessed include providing evidence-based dietary advice, responding to common nutritional therapy questions, and making food recommendations. Evaluation metrics encompassed passing the Chinese Registered Dietitian Examination, alignment of food recommendations with expert advice, and expert reviews of ChatGPT’s responses to nutritional queries. Results indicated both ChatGPT and GPT 4.0 passed the dietitian examination. ChatGPT’s food recommendations and responses were mostly in line with best practices, receiving favorable reviews from professional dietitians, showcasing its potential in dietary management and patient education within the medical nutrition therapy domain for diabetes management.

<cit.> utilized ChatGPT-3.5 and GPT-4.0, as well as Google's Bard, to evaluate their performance in the ophthalmology department, focusing on addressing queries related to ocular symptoms. The research assessed the clinical applications of these large language models (LLMs) in identifying and managing various ocular conditions. Evaluation metrics included accuracy (graded as poor, borderline, good) and comprehensiveness of responses, alongside the models' self-awareness in terms of their ability to self-check and self-correct. ChatGPT-4.0 emerged as the most accurate, with 89.2% of its responses rated as 'good', outperforming ChatGPT-3.5 (59.5%) and Google Bard (40.5%). All LLMs demonstrated high comprehensiveness scores (4.6 to 4.7 out of 5) but showed subpar to moderate self-awareness capabilities.

<cit.> evaluates the ChatGPT(GPT-3.5) model in the ophthalmology field. Focused on the specialized department of ophthalmology, it addresses the clinical task of providing patient care advice through an online medical forum. Evaluation metrics included the ability to distinguish AI-generated responses from those written by ophthalmologists, presence of incorrect information, alignment with medical consensus, likelihood and extent of harm. The results showed that ChatGPT responses were largely indistinguishable from human-written ones, with similar rates of accuracy regarding correct information, safety, and consensus alignment. The study underlines the potential of LLMs like ChatGPT to generate appropriate ophthalmic advice, comparable to board-certified ophthalmologists.

In evaluating the applications of Large Language Models (LLMs) in the medical industry, <cit.> focused on myopia-related inquiries utilized ChatGPT-3.5, ChatGPT-4.0, and Google's Bard. This research targeted the ophthalmology department, specifically addressing the disease of myopia. The evaluation encompassed various clinical tasks, including pathogenesis, risk factors, clinical presentation, diagnosis, treatment and prevention, and prognosis of myopia. The LLMs' responses were assessed based on accuracy and comprehensiveness by three consultant-level paediatric ophthalmologists using a three-point accuracy scale (poor, borderline, good) and a five-point comprehensiveness scale. ChatGPT-4.0 demonstrated superior performance, with 80.6% of responses rated as ‘good’ for accuracy, and showed high mean comprehensiveness scores, highlighting its potential in delivering precise and detailed information for myopia care.

In the study<cit.> evaluating Claude-instant-v1.0, GPT-3.5-Turbo, Command-xlarge-nightly, and Bloomz. This research specifically targeted the clinical specialties of ophthalmology, orthopedics, and dermatology, encompassing a diverse set of 60 diseases. The evaluation assessed LLMs' ability to generate therapeutic recommendations, focusing on clinical tasks such as the accuracy and safety of treatment advice. The evaluation criteria included the mDISCERN score, correctness, and potential harmfulness of the recommendations. Results showed significant differences among the models in terms of quality and safety across the examined medical fields, with Claude-instant-v1.0 achieving the highest mean mDISCERN score and GPT-3.5-Turbo being noted for the lowest harmfulness rating, indicating the nuanced performance of LLMs in generating clinical recommendations for specific diseases and departments.

<cit.> assessed ChatGPT(GPT-3.5) focusing on its application within orthopedics, specifically evaluating its ability to self-diagnose common diseases such as carpal tunnel syndrome, cervical myelopathy, lumbar spinal stenosis, knee osteoarthritis, and hip osteoarthritis. The research aimed to measure the model's accuracy and precision in providing diagnoses and recommending medical consultations. Evaluation metrics included correct answer ratios, reproducibility between days and raters using the Fleiss K coefficient, and the extent of medical consultation recommendations. Results indicated variable accuracy with correct answer ratios ranging from 4% for cervical myelopathy to 100% for carpal tunnel syndrome. Reproducibility also varied, suggesting inconsistencies in ChatGPT's diagnostic capabilities within the orthopedic domain.

<cit.> evaluated the February 2023 version of ChatGPT in the specialized department of reproductive medicine, focusing on fertility-related clinical queries. The clinical tasks assessed included providing information on infertility FAQs from the CDC, completing validated fertility knowledge surveys (Cardiff Fertility Knowledge Scale and the Fertility and Infertility Treatment Knowledge Score), and reproducing the American Society for Reproductive Medicine's committee opinion on "optimizing natural fertility." Evaluations were based on response length, factual content, sentiment analysis, and the accuracy of reproducing key facts. ChatGPT demonstrated comparable performance to established sources in terms of response length, factual content, and sentiment, achieving high percentiles in fertility knowledge surveys and accurately reproducing all key facts from the committee opinion on optimizing natural fertility. This highlights ChatGPT's potential as a relevant and meaningful tool for fertility-related clinical information, though it noted limitations in citing sources and the risk of fabricating information.

<cit.> explores the application of ChatGPT in assessing suicide risk within the mental health department. The experiment utilized ChatGPT versions 3.5 and 4, updated on May 24, 2023, to evaluate their capabilities in a psychiatric context. The clinical task addressed was the assessment of suicide risk, incorporating factors like perceived burdensomeness and thwarted belongingness. Evaluations were conducted based on various indicators such as psychache, suicidal ideation, risk of suicide attempts, and resilience. The results revealed that ChatGPT-4's assessments of suicide risk and suicidal ideation were comparable to those of mental health professionals, highlighting its potential utility in clinical settings. However, it also showed a tendency to overestimate psychache, suggesting areas for further research and improvement.

<cit.> introduces the Medical Multimodal Large Language Model (Med-MLLM), leveraging large-scale pre-training on multimodal medical data. Focused on radiology, specifically chest X-rays (CXR) and Computed Tomography (CT) images, Med-MLLM targets diseases like COVID-19 and its variants, including Delta and Omicron. Evaluations span across clinical tasks such as medical reporting, disease diagnosis, and patient prognosis. Performance metrics used include natural language generation metrics (BLEU, ROUGE-L, CIDEr) for reporting, and Accuracy and AUC for diagnosis and prognosis tasks. Remarkably, with just 1% of labeled data, Med-MLLM demonstrated competitive or superior performance compared to fully-supervised models on various tasks, showcasing its robustness and efficiency in handling rare diseases with limited data.

<cit.> explores the utilization of LLMs in clinical tasks such as differential diagnosis and clinical management for diseases specific to these fields. Although not directly assessing performance through metrics, the authors discuss the potential of LLMs to enhance patient engagement, improve diagnostic accuracy, and offer personalized treatment plans. They underscore the necessity for LLMs like ChatGPT 4.0 to address challenges such as data privacy, ethical considerations, and the verification of AI-generated information to ensure their effective and safe use in clinical settings.

Supportive Departments Applications: 

In the following section, we delve into the application and evaluation of Large Language Models (LLMs) within supportive departments of the medical field, specifically focusing on radiology and emergency department scenarios. The studies presented explore the utility of models like ChatGPT in radiologic decision-making, diagnostic quizzes in neuroradiology, automatic generation of radiology reports from chest X-rays, and diagnosis and triage in emergency settings. Each research piece offers insights into the capabilities and performance of LLMs across these distinct yet crucial areas of medical practice.

<cit.> utilized ChatGPT-3.5 and GPT-4, to explore their capability in clinical decision support within radiology. The research focused on evaluating the models' performance in identifying appropriate imaging services for breast cancer screening and breast pain, crucial areas in radiologic decision-making. The evaluation was conducted against the American College of Radiology (ACR) Appropriateness Criteria, using both open-ended and select-all-that-apply prompt formats. ChatGPT's suggestions were compared with ACR guidelines to assess concordance. Results showed that ChatGPT-3.5 and GPT-4 performed well, with GPT-4 exhibiting a higher average percentage correct in selecting appropriate imaging modalities for breast cancer screening (98.4%) compared to ChatGPT-3.5 (88.9%). For breast pain, GPT-4 also outperformed ChatGPT-3.5 in accuracy, demonstrating the potential feasibility of using large language models for radiologic decision-making and indicating a positive trend in model performance with newer versions.

In their study<cit.>, the authors evaluated the application of the GPT-4-based ChatGPT (July 20, 2023 version) in the field of neuroradiology, focusing specifically on the radiology department and related diseases of the brain, head and neck, and spine. The research aimed at assessing the model's capability in solving diagnostic quizzes from the "Case of the Month" section of the American Journal of Neuroradiology (AJNR), thereby evaluating its utility in clinical tasks such as differential diagnosis and clinical management. The evaluation criteria included diagnostic accuracy and the validity of differential diagnoses, which were quantified using a five-point Likert scale. The results showcased an overall diagnostic accuracy of 57.86% across 140 cases, with performance varying by subgroup: 54.65% for brain, 67.65% for head and neck, and 55.0% for spine. This study highlights ChatGPT 4.0's potential as a supportive tool in radiological diagnostics within specific medical domains.

<cit.> focuses on the application of LLMs in generating radiology reports from chest X-rays (CXRs), a task critical to diagnostic radiology, a supportive department in healthcare. The research leverages the Convolutional vision Transformer (CvT) ImageNet-21K checkpoint for image encoding and the Distilled Generative Pre-trained Transformer 2 (DistilGPT2) checkpoint for text decoding, aiming to automate report generation in the medical domain of radiology, particularly for diagnosing various conditions depicted in CXRs. The clinical task evaluated is the automatic generation of radiology reports, a pivotal component of clinical management and diagnosis. Evaluation metrics include traditional natural language generation (NLG) metrics (BLEU-4, ROUGE-L, METEOR) and the Clinical Efficacy (CE) metric, focusing on diagnostic accuracy. The model, CvT2DistilGPT2, demonstrates notable improvements over the state-of-the-art, with an 8.3% increase in CE F-1 score, indicating higher diagnostic accuracy and report quality akin to that of radiologist-generated reports.

<cit.> evaluates the performance of ChatGPT versions 3.5 and 4.0 focusing on the Emergency Department (ED) setting. It addresses the clinical tasks of diagnosis and triage for patients with urgent or emergent problems, comparing the LLMs' performance against the symptom checker applications from WebMD and Ada Health, as well as diagnoses and triage recommendations from board-certified ED physicians. Evaluation metrics included diagnostic accuracy—measured by the proportion of matches between the LLMs' diagnoses and final ED diagnoses—and triage accuracy, gauged by the agreement with physician recommendations. ChatGPT 3.5 demonstrated high diagnostic accuracy but a higher unsafe triage rate, whereas ChatGPT 4.0 showed improved triage agreement with physicians but lower diagnostic accuracy. The study underscores the potential and limitations of applying LLMs like ChatGPT in critical clinical settings, highlighting the need for improvements in triage accuracy and thorough clinical evaluation before unsupervised patient use.




  §.§.§ Medical Text Data Processing Application Evaluations


In a groundbreaking study<cit.>, researchers developed GatorTronGPT, a generative large language model (LLM) based on the GPT-3 architecture. This model was trained using a diverse dataset comprising 82 billion words of de-identified clinical text from the University of Florida Health, spanning 126 clinical departments and approximately 2 million patients, as well as 195 billion words of general English text. The evaluation focused on biomedical natural language processing (NLP) tasks, specifically relation extraction and question answering, using six benchmark datasets. GatorTronGPT demonstrated state-of-the-art performance, outperforming existing models with the highest F1 scores in relation extraction for drug-drug interaction, chemical-disease relation, and drug-target interaction, as well as achieving competitive accuracy in biomedical question answering. This highlights GatorTronGPT's potential in processing medical text data, such as clinical notes and electronic health records (EHRs), for advanced NLP applications in the medical industry.

In evaluating the applications of large language models in the medical industry, researchers<cit.> focused on ChatGPT versions GPT-3.5 and GPT-4. Their comprehensive benchmark study assessed ChatGPT's performance across various medical text data types, including biomedical corpus like article abstracts, clinical trial descriptions, and biomedical questions. The evaluation encompassed a range of data processing tasks such as Named Entity Recognition (NER), Relation Extraction (RE), and Question Answering (QA), among others. Performance metrics included entity-level F1 scores for NER, Micro F1 for RE, and accuracy for QA tasks. Results highlighted that ChatGPT achieved a BLURB score of 58.50, indicating effective yet limited capabilities in biomedical text understanding and generation compared to the state-of-the-art model's score of 84.30.

In a comprehensive evaluation, researchers<cit.> assessed the performance of four popular LLMs - GPT-3.5, PaLM-2, Claude-2, and LLaMA-2 - across diverse medical text data processing tasks. These tasks, crucial for biomedical text analysis, included Named Entity Recognition (NER), Relation Extraction (RE), Entity Linking, Text Classification, Question Answering, and Text Summarization. Their performance was measured across 26 datasets encompassing various types of medical data such as clinical notes, patient electronic health records (EHRs), and medical research articles. The evaluation focused on metrics like Precision, Recall, F1 scores for NER and RE tasks; Recall@1 for Entity Linking; F1 and Accuracy for Text Classification and Question Answering; alongside ROUGE and BERTScore for Text Summarization. The findings revealed LLMs' strong zero-shot capabilities, particularly in tasks with smaller training datasets, sometimes even outperforming state-of-the-art models that were fine-tuned specifically for those tasks. However, their performance varied across different tasks and datasets, indicating no single LLM consistently outperformed others in all evaluations.

In a retrospective cohort study<cit.> focused on processing patient-initiated electronic health record (EHR) messages, a natural language processing (NLP) framework was developed based on the distilBERT model, a lighter version of BERT optimized for faster computation without significant performance loss. This NLP model was utilized to classify and triage patient communications within the EHR system, specifically targeting messages related to COVID-19. The model's primary tasks involved accurately identifying messages reporting positive COVID-19 test results from a dataset comprising various patient-initiated EHR messages, including test reports and health inquiries. Evaluation metrics centered on classification accuracy, sensitivity, and the macro F1 score. The model demonstrated high effectiveness with a macro F1 score of 94%, and sensitivities of 85% for COVID-19 related messages (not reporting a positive test), 96% for COVID-19 positive test reports, and 100% for non-COVID-19 communications.

In <cit.>, the researchers evaluated the application of the Flan-T5, a publicly available Large Language Model (LLM), for phenotyping patients with Postpartum Hemorrhage (PPH) from discharge notes within electronic health records (EHR). The dataset consisted of 271,081 discharge summaries. The model's performance was assessed on 24 granular concepts related to PPH, demonstrating its ability to enhance phenotyping accuracy and patient identification for clinical and research purposes. Evaluation metrics included sensitivity, specificity, Positive Predictive Value (PPV), Binary F1 score, and accuracy, with the model achieving strong results, such as a PPV of 0.95 in phenotyping PPH. This signifies the model's high fidelity in identifying relevant medical concepts from unstructured clinical text, showcasing its potential in medical text data processing applications without the need for substantial annotated training data.

In <cit.>, researchers focused on leveraging RoBERTa for processing clinical notes from patients with chronic low back pain (cLBP). The medical data category explored includes clinical notes, encompassing progress notes and emergency department provider notes. The evaluation targeted Named Entity Recognition (NER) and Natural Language Inference (NLI) tasks. Performance metrics such as F1 scores were employed to assess the models, with RoBERTa achieving an F1 score of 84.4% for NER tasks. Additionally, an entailment model based on RoBERTa demonstrated its capability in identifying social determinants of health (SDoH) through NLI, showcasing notable performance despite the complexity of clinical narratives. This exploration underscores the potential of large language models in processing and extracting valuable information from unstructured medical texts.

In <cit.>, the authors explored the adaptation of BERT and its variant BioBERT, to the Italian language for processing biomedical textual data. They experimented with medical data including machine-translated biomedical abstracts and native Italian medical textbooks, focusing on domain-specific adaptation. The evaluation covered various data processing tasks, such as Named Entity Recognition (NER), Extractive Question Answering (QA), and Relation Extraction (RE), across different biomedical datasets translated into Italian. Performance was measured in terms of Mean Reciprocal Rank (MRR) for language modeling and F1 scores for downstream tasks. The BioBIT model, derived from machine-translated PubMed abstracts, demonstrated significant improvements over the baseline, particularly in NER and RE tasks, with F1 score improvements as high as 5.9% and 9.4%, respectively. These results underline the model's effectiveness in biomedical language understanding and processing for the Italian language, addressing the gap in resources for less-resourced languages.

In a study<cit.> on leveraging transformer-based models for structuring free-text radiological reports, the authors experiment with BERT, focusing on German chest X-ray reports from intensive care unit (ICU) patients. They evaluate the model's performance on text classification tasks, specifically identifying six radiological findings mentioned in the reports. The evaluation metrics include macro-averaged F1-scores (MAF1) and confidence intervals (CIs). The study compares the efficacy of different pre-training and labeling strategies: an on-site pre-trained model using masked language modeling (Tmlm) versus a medically pre-trained model (Tmed), both fine-tuned with 'silver' (rule-based) and 'gold' (manually annotated) labels. The highest performance was observed for Tmlm,gold, achieving a MAF1 of 95.5% (CI: 94.5–96.3), highlighting the efficiency of custom pre-training combined with manual annotations for medical text data processing applications.

In the research<cit.> presented, a deep learning system was developed for the task of section identification in clinical narratives, specifically unstructured clinical records such as progress notes written in Spanish. The foundation of this system is a transformer-based pre-trained language model, specifically the BSC's bsc-bio-ehr-es model, which has been fine-tuned on a curated corpus consisting of 1038 annotated clinical documents. The evaluation of the system's performance was conducted using a newly designed B2 metric, focusing on section identification accuracy. This metric allowed for a nuanced assessment of the system's ability to identify and classify sections within the clinical records accurately. The system achieved an average B2 score of 71.3 on the open-source dataset provided for community use, and 67.0 in data scarcity scenarios, showcasing its effectiveness and potential for application in processing clinical narratives within the medical industry.

In a comprehensive analysis<cit.> focused on evaluating LLMs utilizing a NLP algorithm for the extraction and categorization of pulmonary nodules from chest CT radiology reports. This investigation specifically processed imaging reports, a vital category of patient electronic health records (EHRs), to evaluate the performance of LLMs in medical text data processing tasks. The NLP algorithm developed for this purpose was assessed for its ability to accurately identify reports containing pulmonary nodules and measure the diameters of these nodules. Evaluation metrics concentrated on sensitivity, specificity, and accuracy, with the algorithm achieving notable results: sensitivity of 94% for identifying nodule-containing reports, and an accuracy of 93% in determining the largest reported nodule diameter. This study underscores the efficacy of LLMs in processing and analyzing clinical imaging reports, thereby providing a critical foundation for the early detection and management of lung cancer within the medical field.

In the study <cit.>, a Transformer-based fusion model, ARMOUR, was explored for multimodal data processing in clinical settings. This model was specifically designed to handle structured and unstructured medical data, such as physiological measurements and clinical notes, reflecting its application on diverse medical data types. The evaluation focused on six clinical prediction tasks, including risk prediction and Diagnostic Related Groups (DRG) predictions, to assess the model's performance in handling and processing medical text data alongside structured measurements. Performance evaluations were conducted using metrics like Area Under the Receiver Operating Curve (AUROC) and Area Under the Precision–Recall Curve (AUPRC) for binary classification tasks, and accuracy and Macro F1 for multiclass classification tasks. Results highlighted ARMOUR's superior performance in comparison to unimodal and other multimodal baseline methods, showcasing its effectiveness in integrating structured and unstructured medical data for improved clinical predictions.

In the study <cit.>, researchers utilized the Savana Manager 3.0 artificial intelligence platform, based on natural language processing (NLP) techniques, for data extraction and analysis. This AI application was deployed on clinical diagnosis data derived from electronic health records (EHRs) provided by the Castilla-La Mancha Regional Healthcare Service (SESCAM) in Spain, covering a period from January 2012 to December 2020. The study focused on processing unstructured clinical information within EHRs, specifically targeting idiopathic pulmonary fibrosis (IPF) diagnosis, to identify patient profiles, diagnostic test utilization, and therapeutic management. Evaluation metrics included precision (P), recall (R), and the F-score, with results indicating high accuracy in identifying IPF cases within the medical records: P=1.0, R=0.862, and F-score=0.926. These findings underscore the effectiveness of applying large language models in medical data processing, particularly in accurately extracting and analyzing patient information from EHRs.





  §.§.§ Medical Research Application Evaluations

The rapid proliferation of LLMs like ChatGPT in medical research underscores a transformative phase in the intersection of artificial intelligence and healthcare. Although not conducting an empirical evaluation themselves, <cit.> discussing the use of ChatGPT in the medical field provide essential insights that guide researchers in this domain. These insights, while highlighting the potential enhancements in research methodologies facilitated by LLMs, also underscore the urgent need for empirical studies to validate these technologies in real-world settings. Key points raised in this paper include the urgent call for more empirical research to substantiate the claims of efficiency and effectiveness of LLMs in medical applications. The authors point out that while the citation rate of ChatGPT in PubMed suggests a rapid acceptance within the medical community, a substantial portion of these references are from editorials and commentaries rather than rigorous research studies. This gap indicates a critical need for structured and well-documented empirical research to evaluate the actual impact of LLMs on medical research and practice. Building on the insights highlighted about the potential applications of LLMs in medical research, it is crucial to delve deeper into how these technologies are being empirically evaluated across different facets of scientific inquiry. In this part, we aim to offer overview of evaluations of LLM applied in medical scientific researches, specifically, in three key areas: 1. Biomedical information retrieval and scientific article screening; 2. Modeling and analysis; 3. Writing of scientific article and reference generation. 

Retrieval & Screening: 

MedCPT<cit.> stands out by utilizing a Transformer-based model, specifically designed for biomedical information retrieval (IR). MedCPT was experimentally tested on various biomedical IR tasks, demonstrating its capability through zero-shot settings. Performance was assessed on multiple metrics, including document retrieval, article representation, and sentence representation. Notably, MedCPT achieved state-of-the-art results on six different IR tasks, setting new benchmarks in the Benchmarking-IR (BEIR) suite, surpassing traditional sparse retrievers and even larger models such as OpenAI's cpt-text-XL. This highlights its efficacy in generating superior biomedical article and sentence representations, thus underscoring its utility in medical research applications.

<cit.> utilized the ChatGPT and GPT-4 APIs to automate the screening of titles and abstracts for clinical reviews. The performance of these large language models (LLMs) was assessed based on accuracy, macro F1-score, sensitivity, and interrater reliability, with k and prevalence-adjusted and bias-adjusted k (PABAK). The results indicated an overall accuracy of 0.91, a macro F1-score of 0.60, a sensitivity for included papers at 0.76, and for excluded papers at 0.91. Additionally, the model demonstrated a high level of agreement with human reviewers (k=0.96 PABAK), highlighting its efficiency and reliability in identifying relevant clinical studies.


Modeling & Analysis: 

In the study<cit.>, a Generative Pre-trained Transformer-inspired model, named cMolGPT, was utilized to experiment with de novo molecular design targeting specific proteins. This application of a LLMs in medical research was assessed using the generation of SMILES strings for novel compounds. The evaluation metrics included the fraction of valid and unique molecules, fragment similarity, and similarity to the nearest neighbor, among others. Results showed that cMolGPT outperformed baseline models in generating valid and unique molecules, indicating its effectiveness in designing drug-like compounds specific to given targets. These findings highlight the model's potential to contribute significantly to the molecular optimization cycle in drug discovery.

In the study presented in "BioSeq-Diabolo,"<cit.> researchers utilized NLP-derived semantics analysis methods to evaluate biological sequence similarities, a task analogous to semantics similarity analysis in natural languages. They applied these techniques to assess protein remote homology detection, circRNA-disease associations, and protein function annotation. The evaluation metrics included the Receiver Operating Characteristic Curve (ROC), Precision-Recall Curve (PRC), and Learning to Rank (LTR) integration method. Experimental results demonstrated that the NLP-based methods in BioSeq-Diabolo outperformed other state-of-the-art predictors in these tasks, highlighting the potential of leveraging large language model techniques for complex biological sequence analysis.

RNA-MSM<cit.>, an RNA MSA-transformer language model, stands out by leveraging homologous sequence data generated via RNAcmap3. This model is specifically evaluated on tasks related to RNA secondary structure prediction and solvent accessibility prediction. Evaluation metrics include precision-recall curves, F1 scores, and Matthews correlation coefficient (MCC) for secondary structure, along with Pearson correlation coefficient (PCC) and mean absolute error (MAE) for solvent accessibility. The RNA-MSM model demonstrated significant improvements over existing techniques such as SPOT-RNA2, showcasing the robustness and precision of LLMs in deciphering complex biological data and enhancing predictive performance in medical research applications.

In the research<cit.>, the authors developed and evaluated a LLM named NYUTron, based on a BERT-like architecture. NYUTron was specifically tested in the medical domain, focusing on a series of clinical and operational tasks within the NYU Langone Health System. These tasks included predicting 30-day all-cause readmission, in-hospital mortality, comorbidity index, length of hospital stay, and insurance denial predictions. The model's performance was assessed using the Area Under the Curve (AUC) as a primary metric. NYUTron demonstrated AUCs ranging from 78.7% to 94.9%, outperforming traditional models by improvements of 5.36% to 14.7% across different tasks, showing its effectiveness in leveraging unstructured clinical notes for predictive healthcare analytics.

In the study<cit.>, experiments were conducted using the domain-specific Pre-trained Language Model known as CNBERT, which was trained on a large dataset of clinical notes to tailor its capabilities for medical applications. The evaluation was focused on medical text classification tasks. These tasks were assessed using accuracy as the primary performance metric. Results indicated that the CNBERT model outperformed standard NLP methods and even other domain-specific PLMs across various test settings. Specifically, the integration of medical knowledge through the MedKPL framework significantly improved classification accuracy, demonstrating robustness in both multi-classification and binary classification tasks across different medical departments.

In the study<cit.>, researchers utilized a novel ensemble-based approach for sentiment analysis on social media data to evaluate public sentiment toward COVID-19 vaccines in the United Kingdom. This ensemble model integrated a hybrid of lexicon rule–based and deep learning–based methods, specifically combining the Valence Aware Dictionary for Sentiment Reasoning (VADER), TextBlob, and the Bidirectional Encoder Representations from Transformers (BERT) model. Evaluation of this application involved monitoring the frequency and nature of adverse effects following immunization (AEFIs) mentioned in social media posts, as well as analyzing sentiment trends related to the vaccines and manufacturers. The study reported an overall positive public sentiment towards vaccines at 58%, with negative and neutral sentiments at 22% and 19% respectively. This ensemble approach demonstrated robust performance in capturing public opinion trends, providing complementary insights for pharmacovigilance and public health policy making. 

Writing & Reference Generation: 

<cit.> utilized the GPT-3 to explore its capability in generating fraudulent scientific articles in the medical field, specifically in neurosurgery. The evaluation focused on the model's ability to mimic the structure and content of authentic medical research papers. Key aspects assessed were linguistic coherence, technical accuracy, and the authenticity of citations and references. The results indicated that while the AI could produce text that superficially resembles a genuine scientific article, including correct formats and a coherent layout, it also generated several incorrect and nonexistent citations, revealing potential flaws in its output when scrutinized by experts.

<cit.> reviews the application of GPT-3 and GPT-4 in medical writing. It discusses their theoretical use in generating various medical documents, emphasizing the need for oversight by medical professionals to ensure the accuracy and ethical integrity of AI-generated content. Concerns such as bias, misinformation, and privacy are highlighted as significant issues that necessitate ongoing training and regulation of these models. While the discussion is rich in ethical and operational considerations, it does not include results from empirical evaluations but stresses the importance of critical review by experts in actual medical settings.

<cit.> investigates the capabilities of GPT-3.5 and ChatGPT in zero-shot medical evidence summarization. The evaluation was conducted on summarization tasks across six clinical domains, utilizing abstracts from Cochrane Reviews. Key aspects assessed were coherence, factual consistency, comprehensiveness, and harmfulness. The results indicated that while LLMs can effectively capture essential information from source documents, they sometimes generate factually inconsistent summaries or potentially harmful content. These findings suggest that while LLMs are promising tools for medical summarization, careful scrutiny is necessary to ensure the reliability and safety of their outputs in medical applications.

<cit.> discuss the application of the ChatGPT in the medical research field, highlighting its potential in streamlining the creation of scientific documents and bureaucratic texts. However, they caution against the uncritical use of this technology due to observed inaccuracies in content generation, specifically in the realm of scientific citation. The study identifies instances where ChatGPT provided incorrect or fabricated literature references, underscoring the risk of relying on AI-generated data without verification. This analysis suggests that while LLMs like ChatGPT can be useful tools in medical research, their output must be meticulously checked for accuracy and reliability, particularly when used in clinical decision-making or educational settings.

<cit.> utilized the GPT-3.5 to assess its effectiveness in identifying references for nephrology literature reviews. The evaluation focused on the accuracy and reliability of reference generation. Key metrics included the existence, completeness, and authenticity of references. Results indicated that 62% of the references provided by ChatGPT existed, but only 20% were authentic. The study highlighted a significant number of fabricated (31%) and incorrect (7% incomplete) references. Notably, errors were frequent in DOI and link accuracy, suggesting limited reliability of ChatGPT in medical research applications without further verification and cross-checking.

While LLMs show promising enhancements to various aspects of medical research, significant challenges remain that require further investigation. Issues such as data privacy, potential biases, misinformation, and ensuring diversity in data handling need comprehensive assessment. Addressing these concerns is crucial for advancing LLM applications in a manner that is ethically sound and scientifically robust. Future research must continue to evaluate these areas critically, ensuring that advancements in LLM technology align with the overarching goals of improving accuracy, fairness, and security in medical research.



  §.§.§ Medical Education & Public Awareness Application Evaluations


Medical Education: 

As the medical education landscape seeks innovative ways to enhance learning and knowledge dissemination, LLMs are emerging with significant potential to influence this field. The capabilities of LLMs to understand complex texts, reason through medical scenarios, and generate coherent and contextually relevant responses position them as promising tools in the realm of educational technologies. The application of LLMs in medical education is seen as particularly valuable for a number of reasons: they can act as supplementary resources that provide explanations and case studies, aid in keeping professionals updated with the latest research and evidence-based practices, and offer interactive platforms for learning and assessment. While the tangible impacts of LLMs on medical education are still being explored, their potential to revolutionize learning experiences in this domain is clear. These models can simplify the digestion of complex content through interactive methods, enhancing both the understanding and retention of medical knowledge. Additionally, LLMs offer promising avenues for evaluating academic content and augmenting traditional exam preparation through their advanced reasoning and response generation capabilities. This broad utility signals a shift towards more dynamic and responsive educational environments, where LLMs support a deeper engagement with the material and offer personalized learning experiences. The articles selected for discussion in this section will critically examine these potential applications, assessing the effectiveness and practicality of LLMs in fostering an advanced educational framework for medical students and professionals.

The quality of educational materials is crucial, and while LLMs have the capability to generate high-quality teaching content, it is essential to evaluate their output to prevent the dissemination of inaccuracies or biases to medical professionals. The study<cit.> utilized GPT-4 to evaluate its application in generating clinical vignettes. These vignettes are essential for training medical students and professionals by simulating patient cases that accurately represent demographic diversity. The evaluation focused on the model's ability to portray the correct demographic distribution of various medical conditions compared to actual US prevalence data. The findings indicated significant biases, as GPT-4 often stereotyped certain demographics in its clinical presentations. For example, it over-represented Black patients in vignettes about sarcoidosis and under-represented other demographics in various conditions. These biases highlight the need for careful implementation and monitoring of LLMs in educational tools to prevent the reinforcement of stereotypes in medical training.

About educational materials, there is a large amount of online medical content, large language models can also be leveraged to evaluate the quality of existing online content, because high-quality educational materials can significantly enhance learning efficiency. The study<cit.> accessed ChatGPT's ability on evaluating online medical content's quality and readability. Specifically, they centered on ChatGPT's proficiency using the DISCERN tool and readability assessments, focusing on shock wave therapy for erectile dysfunction. Evaluations were conducted based on the accuracy of the DISCERN score and various readability indices like the Flesch-Kincaid level and Gunning Fox Index. Results showed a significant discrepancy between ChatGPT's evaluations and those obtained from established human raters and readability tools, indicating that ChatGPT may not yet effectively assess medical content as accurately as human experts and established instruments.

Beyond traditional educational materials, Large Language Models (LLMs) also facilitate the learning process as tools for quick knowledge retrieval and learning support. This study<cit.> utilized the ChatGPT(GPT-3) for educational scenarios within the field of oral and maxillofacial radiology. It assessed the model's capability to provide detailed information on radiographic anatomical landmarks and pathologies. Evaluation was based on a set of 80 questions that covered anatomical landmarks, specific pathologies, and their radiographic features. The model's responses were analyzed and rated using a modified 4-point Likert scale. Results indicated that ChatGPT-3 accurately described radiographic landmarks with a mean score of 3.94 and effectively communicated the characteristic features of pathologies, achieving mean scores of 3.85 for pathologies and 3.96 for their radiographic features. This study underscores the potential of ChatGPT-3 as a supplementary educational tool in medical settings, though it highlighted limitations in detail specificity and the handling of medical abbreviations.

Beyond information retrieval and learning support in educational processes, many researchers have empirically assessed the capabilities of LLMs in answering standardized public examination questions to gauge their understanding and knowledge mastery. These evaluations highlight whether LLMs have the potential to serve as learning assistants. <cit.> utilized versions GPT-3.5 and GPT-4 to answer questions from the 112th Japanese National Nursing Examination (JNNE). The models were assessed based on their ability to provide correct answers across multiple question types, including compulsory, general, scenario-based, and conversation questions. Performance indicators included accuracy rates for each question type. The results showed a significant improvement in accuracy from GPT-3.5 to GPT-4, demonstrating potential benefits of LLMs in medical education settings.

In the study<cit.>, ChatGPT(GPT-3) was tested in a mock objective structured clinical examination (OSCE) for the Royal College of Obstetricians and Gynaecologists. The experiment assessed ChatGPT's performance on structured discussion questions related to various obstetrics and gynecology topics. Evaluators judged ChatGPT on its factual accuracy, contextual relevance, communication, information gathering, patient safety, and applied clinical knowledge. The results demonstrated that ChatGPT outperformed average historical human candidate scores, exhibiting rapid and contextually apt responses within the constraints of the examination format.

In the study<cit.> ChatGPT and GPT-4 were evaluated on their performance in answering USMLE-style questions involving soft skills such as empathy and ethics. Specifically, the models were tasked with 80 multiple-choice questions designed to assess communication, professionalism, and ethical judgment. These questions were sourced from the USMLE website and the AMBOSS question bank. Performance metrics included accuracy and consistency, with GPT-4 demonstrating superior results by correctly answering 90% of the questions, compared to ChatGPT's 62.5%, and showing no revisions in its responses, highlighting its robustness and reliability in handling complex medical ethics scenarios.

In addition to standardized test questions, some researchers<cit.> have developed well-designed case vignettes to evaluate LLM capabilities. In a study involving models such as ChatGPT, Google Bard, and Microsoft Bing, 50 hematology-related cases were used to test their application in medical education. These models were assessed on their ability to answer questions across a range of hematology topics. Medical professionals rated the responses on a scale of one to five. Results showed significant performance differences among the models, with ChatGPT scoring the highest, demonstrating its potential effectiveness for educational uses in medicine.

In a comparative study<cit.>, ChatGPT, Google Bard, and Microsoft Bing were evaluated for their ability to solve case vignettes in physiology education. These models were tasked with responding to 77 validated physiological case vignettes. The evaluation focused on accuracy and appropriateness, using a scale from 0 to 4 structured around the observed learning outcomes. Results showed that ChatGPT outperformed the others, with overall higher accuracy scores, indicating its potential utility in medical education settings.

Public Awareness Application: 

In the post-COVID-19 era, with the surge in GPT technology applications, the public is inundated with information of varying quality. Researchers are applying LLMs to sift through this data, identifying authentic information, providing accurate medical insights, and supporting public self-diagnosis efforts. To fully understand the value LLMs bring to these applications, it is crucial to examine the evaluations of these use cases.

Building on the previous discussion of LLMs used to evaluate the quality and readability of online medical content<cit.>, the applicability of LLMs to discern the veracity of information also holds feasibility. The study<cit.> focuses on the detection of COVID-19 related fake news using transformer-based models like BERT and COVID-Twitter-BERT (CT-BERT). The researchers evaluated the effectiveness of these models in a public awareness scenario by discerning the authenticity of information spread on social media platforms. Evaluation metrics included accuracy, precision, recall, and F1 score, with the model CT-BERT coupled with a BiGRU layer achieving an F1 score of 98.5%, indicating high effectiveness.

If LLMs can accurately discern the veracity of content, it suggests their potential to provide trustworthy and reliable information to the public. Some researchers have explored this capability by evaluating how LLMs deliver medical knowledge, more importantly, assessing their effectiveness in communicating accurate health information. <cit.> utilized LLMs to address frequently asked questions about postpartum depression (PPD), derived from the American College of Obstetricians and Gynecologists (ACOG). Specifically, ChatGPT(GPT-4) and LaMDA (via Bard) were tested alongside Google Search. The evaluation focused on the clinical accuracy of LLM-generated responses to these PPD-related queries. Clinical accuracy was assessed by two board-certified physicians using a GRADE-informed scale, evaluating the appropriateness of responses in reflecting current medical knowledge. Results demonstrated that ChatGPT provided more clinically accurate responses compared to Bard and Google Search, indicating its potential utility in public education by delivering reliable medical information.

<cit.> utilized ChatGPT(GPT-3.5) to address fertility-related queries. This application was evaluated in a public awareness context where the AI was tasked to respond to FAQs and clinical queries from reputable sources like the CDC and American Society for Reproductive Medicine. Evaluation metrics included response accuracy, factual correctness, and the ability to replicate expert consensus opinions from medical documents. The results showed that ChatGPT could generate informative and relevant responses, with the model achieving high accuracy in replicating medical advice and facts. However, limitations were noted in source citation and occasional factual inaccuracies.

Building on the potential of LLMs to disseminate medical knowledge to the public, these models also extend their utility to assisting individuals in making preliminary assessments or interpretations of medical conditions based on provided data. In this study<cit.>, ChatGPT(GPT-3.5) was employed to evaluate their application in public awareness and education within the medical field. The primary task assigned to ChatGPT was to self-diagnose common orthopedic conditions based on symptom descriptions provided by researchers. This application was intended to assess the model’s utility in aiding public self-diagnosis, potentially increasing public awareness about these conditions. The evaluation metrics focused on the accuracy, precision, and the model’s consistency in recommending medical consultations. Results indicated varied correct answer ratios across different conditions, highlighting inconsistent performance. ChatGPT demonstrated high accuracy in diagnosing conditions with localized symptoms such as carpal tunnel syndrome but struggled with conditions having multifocal symptoms like cervical myelopathy. This evaluation showcases the potential and limitations of using LLMs for public education in recognizing symptoms and understanding when to seek professional medical advice.

The European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) Working Group on Artificial Intelligence (WG-AI) employed the ChatGPT to simulate patient scenarios<cit.>. ChatGPT was tasked with interpreting laboratory test results from fictional clinical cases. These interpretations were evaluated by WG-AI members for their relevance, correctness, helpfulness, and safety. The model's performance in interpreting laboratory results was systematically assessed. While ChatGPT recognized all laboratory tests and could detect deviations from the reference intervals, its interpretations were found to be superficial and not always accurate. The evaluations showed varied results across the quality dimensions, indicating that the model’s utility in providing meaningful medical education or public awareness is limited without further specialized training in medical data.



 §.§ Comprehensive Discussion on Evaluation Methods and Indicators

(To Be Updated)



 §.§ Benchmarks

(To Be Updated)



§ CHALLENGES AND ISSUES

(To Be Updated)



§ CONCLUSION & OUTLOOK

(To Be Updated)






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