A new position paper argues that medical AI is neglecting crucial real-world treatment outcomes. Current AI models are primarily trained and evaluated on human opinions and synthesized texts, rather than actual data from treatments. This oversight limits the potential of medical AI and introduces deficiencies in both advanced models and major benchmarks. The paper advocates for incorporating real treatment outcomes from sources like observational databases and randomized experiments into AI training and evaluation to better achieve the goal of improving patient health. AI
IMPACT This research highlights a critical gap in medical AI development, potentially redirecting future efforts towards more outcome-focused evaluation and training.
RANK_REASON The cluster contains a single academic paper discussing a specific research methodology and its limitations. [lever_c_demoted from research: ic=1 ai=1.0]
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