The Benefits and Concerns of AI in Medical Diagnosis

Welcome to another episode of The Words Matter Podcast.

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So we are into episode four of the clinical reasoning series, and I continue my conversation with philosopher of medicine Prof. Bjørn Hofmann where we develop our discussion which started on the ethical implications of disease in the previous episode to now moves on to overdiagnosis and medicalisation.  

And for reference we speak around Bjørn’s 2016 paper titled "Medicalization and overdiagnosis: different but alike." Published in the journal Medicine, Health Care and Philosophy (see paper here and see Bjørn’s other work on the topic here)

In this episode we speak about: 

• Distinguishing between the concepts of medicalisation and overdiagnosis and discuss their main drivers.
• How medicine, health care,  and health professionals have become ever more diligent in defining, detecting, preventing, and treating disease – covering more ground than ever and how this can lead to the adverse situation of overdiagnosis.
• The positive and adverse effects of giving someone diagnosis
• What Bjørn terms the ‘asymmetry of aversion’ meaning that for many health professionals is worse to overlook something than to over do something which may facilitate over diagnosis.
• The role of AI and machine learning to address the crudeness and imprecision in some our diagnostic labelling and processes.
• High and low-value care and the role of healthcare economics on how readily we dip into the diagnostic toolkit and medical testing.
• How the expansion in the concept of disease (and diagnosis) has lead to over diagnosis and medicalization
• And finally we discuss what can we do to reduce the detrimental expansion of disease and subsequent over diagnosis.

So this was another wonderful conversation with Bjørn. He is able to transfer incredibly thought provoking yet fundamental questions to clinical practice and our care of people, and I have immensely grateful to him for giving up so much of his time.