ich is not a single stream of research components or methodologies, but rather a diverse amalgam- ation of medical devices, sensors, and mobile applications, on which several hybrid SaaS (software as a service)-enabled business solutions rely, by means of innova- tive startups leading the transformation of healthcare. Briefly, the core principle that IoMT relies on is multimodal monitoring for early threat assessment leading to the steps required for early diagnosis, easier treatment, and better prevention, all the 5G in Healthcare while keeping the stakeholders (patients, healthcare workers, and doctors) in a feed- back connected loop. A technical overview of IoMT would result in a generalized positive consensus on the approach given its primary reliance on interconnected devices. However, developers and experts in the field of Industry 4.0, 5G, and AI would prefer to refer to such a consensus as a layman's understanding of a rather complicated and perplexing field. Troubles emerge as we dig deep into this field, especially when taking into consideration how the reliance on interconnected devices requires a near-real-time analysis and insight generation on high-frequency long-term observational medical data collected from various data points [14]. Also, provided that the uses of IoMT are constantly on the rise, the question of ensuring a single-digit millisecond latency rate and massive bandwidth of conduction for tele surgery, and SO on., becomes a question worth addressing. Although various startups, businesses, and research institutions across the globe have proposed proprietary AI-enabled 5G architectures for smart healthcare, the above questions continue to exist that require focused and intensive collaborations between industry and academia in the long run. 1.3.1 INDUSTRY APPLICATIONS: 5G ENABLED TELEMEDICINE A real-case study of the existing impact of Al-enabled 5G solutions in smart healthcare is the rise of telemedicine. Given the lack of dexterity both theoretically and practically of AI-use cases, the