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Artificial intelligence and medicine have a longstanding and proficuousrelationship. In the present work we develop a brief assessment of thisrelationship with specific focus on machine learning, in which we highlightsome critical points which may hinder the use of machine learning techniquesfor clinical diagnosis and therapy advice in practice. We then suggest aconceptual framework to build successful systems to aid clinical diagnosis andtherapy advice, grounded on a novel concept we have coined drifting domains. Wefocus on oncology to build our arguments, as this area of medicine furnishesstrong evidence for the critical points we take into account here.

On the practice of classification learning for clinical diagnosis and therapy advice in oncology