BENCHMARKS IN MEDICINE: THE PROMISE AND PITFALLS OF EVALUATING AI TOOLS WITH MISMATCHED YARDSTICKS

The core tension is this: medicine is not just about getting answers right. It is about getting people right. Doctors are trained to deal with doubts, handle exceptions, and recognise cultural patterns not taught in books. AI, by contrast, is only as good as the data it has seen and the questions it has been trained on

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In May 2024, OpenAI released HealthBench, a new benchmarking system to test the clinical capabilities of large language models (LLMs) such as ChatGPT. On the surface, this may sound like yet another technical update. But for the medical world, it marked an important moment—a quiet acknowledgement that our current ways of evaluating medical AI are fundamentally wrong.

Headlines in the recent past have trumpeted that AI “outperforms doctors” or “aces medical exams.” The impression that’s coming through is these models are smarter, faster, and perhaps even safer. But this hype masks a deeper truth. To put it plainly, the benchmarks used to arrive at these claims are based on exams built for evaluating human memory retention from classroom teachings. They reward fact recall, not clinical judgment.

AI-driven innovations in medicine: devices, data, and diagnosis

A calculator problem

A calculator can multiply two six-digit numbers within seconds. Impressive, no doubt. But does this mean calculators are better than, and understand maths more than mathematics experts ? Or better even than an ordinary person who takes a few minutes to do the calculation with a pen and paper?

Language models are celebrated because they can churn out textbook-style answers to MCQs and fill in the blanks for medical facts and questions faster than medical professors. But the practice of medicine is not a quiz. Real doctors deal with ambiguity, emotion, and decision-making under uncertainty. They listen, observe, and adapt.

The irony is that while AI beats doctors in answering questions, it still struggles to generate the very case vignettes that form the basis of those questions. Writing a good clinical scenario from real patients in clinical practice requires understanding human suffering, filtering irrelevant details, and framing the diagnostic dilemma with context. So far, that remains a deeply human ability.