A claim argued that artificial intelligence will make obtaining a medical degree pointless by the time graduates practice. Healthcare organizations deploy AI for administrative tasks and specialized assistance such as scanning medical imagery. Current AI suffers from widespread hallucinations and can cause clinician deskilling when over-relied upon. Frontier large language models fail when familiar medical exam formats are slightly altered, undermining real-world reliability and risking harmful, garbled advice. Models including GPT-4o and Claude 3.5 Sonnet performed poorly after minor wording changes, reflecting probabilistic next-word prediction rather than human-level medical understanding. Medical training and human clinicians remain essential for safe patient care.
Last week, Google AI pioneer Jad Tarifi sparked controversy when he told Business Insider that it no longer makes sense to get a medical degree - since, in his telling, artificial intelligence will render such an education obsolete by the time you're a practicing doctor. Companies have long touted the tech as a way to free up the time of overworked doctors and even aid them in specialized skills, including scanning medical imagery for tumors. Hospitals have already been rolling out AI tech to help with administrative work.
As detailed in a paper published in the journal JAMA Network Open, things quickly fell apart for models including OpenAI's GPT-4o and Anthropic's Claude 3.5 Sonnet when the wording of questions in a benchmark test was only slightly adjusted. The idea was to probe the nature of how large language models arrive at their answers: by predicting the probability of each subsequent word - and not through a human-level understanding of complex medical terms.
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