In May 2024, OpenAI introduced HealthBench, a new benchmarking system aimed at evaluating the clinical capabilities of large language models (LLMs) like ChatGPT. While on the surface this seemed like a routine tech update, for those in medicine it was a defining moment—a quiet but powerful admission that the way we’ve been measuring AI in medicine is flawed at its core.

For several years now, headlines have celebrated how AI tools are acing medical exams, scoring higher than doctors in competitive tests and being framed as more efficient, accurate, and even safer. These claims have generated excitement and concern in equal measure. But beneath the hype lies a deeper tension: Medicine is not about getting answers right. It is about getting people right.

Medicine Is Not Just Facts—It’s Judgement, Nuance, and Humanity

Doctors are trained not only to know facts but also to handle doubt, tolerate uncertainty, respond to exceptions, and recognise unspoken cultural and emotional cues. Medical education focuses not only on what’s in the textbook but on how to read a patient, how to decide when not to act, or when to follow instinct over data.

By contrast, AI tools are only as good as the data they are trained on, and questions they have seen. They are pattern-matching systems, trained to optimise for statistical correctness. They may retrieve accurate information, but they lack context and often struggle with ambiguity.

What Is HealthBench, and Why Does It Matter?

HealthBench by OpenAI attempts to create a new standard for testing how well LLMs perform in clinical scenarios. But it also indirectly shows how previous benchmarks have relied on testing tools designed for humans, not machines.

Most benchmarks till now were based on medical licensing exams. These exams test knowledge recall, memorisation of diagnostic criteria, drug names, standard treatments—things doctors must know early in their careers. But this is not how real doctors work in practice.

In real clinical settings, doctors look at incomplete data, ask follow-up questions, weigh trade-offs, and use judgment shaped by years of patient interaction. AI, in contrast, responds to fixed queries and often has no feedback loop or sense of real-world context.

The Mismatch in Yardsticks

Comparing AI and doctors using current benchmarks is like testing a chef and a calculator on how fast they can list ingredients of a recipe. The calculator will win—but would you trust it to cook your dinner?

This is exactly what’s happening in the medical AI space. The yardsticks are mismatched. We are testing AI on memory and speed, but claiming it’s ready for humanlike decision-making.

HealthBench is a step forward—it aims to create tests that better reflect the types of reasoning, data synthesis, and clinical logic that occur in real-world healthcare. But experts warn that even this tool has limitations, because true clinical competence involves intuition, empathy, communication, and risk assessment, none of which are easy to encode in benchmarks.

AI as a Tool, Not a Replacement

There is growing consensus among medical practitioners that AI should be viewed as a tool, not a replacement for doctors. Used wisely, AI can help with:

• Sorting through large amounts of data

• Flagging patterns or anomalies

• Suggesting diagnoses for rare conditions

• Providing medical summaries or translation in telemedicine

But real clinical work is messy. It involves patients who don’t follow textbook symptoms, multiple conditions, and deeply human variables like anxiety, fear, non-compliance, or mistrust. No AI tool is ready to handle all this at scale, in real-time.

The Dangers of Overhyping AI in Medicine

By continuing to overstate AI’s clinical competence based on exam-style benchmarks, we risk:

• Creating false expectations among patients

• Pushing doctors to rely on tools without accountability

• Misallocating funds to tech that doesn’t deliver real outcomes

• Ignoring the importance of human presence and care

Medical training takes years, and part of its value lies in building judgment, learning to accept failure, and balancing science with ethics. These elements are not replicable in current LLMs.

Cautionary Lessons for Regulators and Hospitals

Policymakers and hospital administrators must resist the temptation to fast-track AI systems purely based on benchmark results. Instead, they should ask:

• Does this tool improve patient outcomes?

• Can it explain its recommendations clearly and reliably?

• Is it fair, inclusive, and safe for diverse populations?

• How is accountability maintained if something goes wrong?

The pitfalls of benchmarking AI incorrectly are many: from overreliance to ethical lapses, to systemic bias and erosion of public trust. We need transparent, clinician-led oversight before AI can be deployed meaningfully in healthcare.

Looking Ahead: A Better Framework for Medical AI

As we move forward, the medical community must push for richer, multi-dimensional benchmarks that test AI on:

• Case reasoning over fact recall

• Understanding uncertainty and risk

• Cultural and social nuances in patient behaviour

• Communication skills and empathy emulation

Collaboration between technologists, ethicists, medical educators, and patient advocacy groups is vital. HealthBench is a step in the right direction, but the journey towards trustworthy, safe, and effective AI in medicine is just beginning.

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