On the practice of classification learning for clinical diagnosis and
  therapy advice in oncology
  Artificial intelligence and medicine have a longstanding and proficuous
relationship. In the present work we develop a brief assessment of this
relationship with specific focus on machine learning, in which we highlight
some critical points which may hinder the use of machine learning techniques
for clinical diagnosis and therapy advice in practice. We then suggest a
conceptual framework to build successful systems to aid clinical diagnosis and
therapy advice, grounded on a novel concept we have coined drifting domains. We
focus on oncology to build our arguments, as this area of medicine furnishes
strong evidence for the critical points we take into account here.
