"Sounds Impressive... But for Whom?" Why AI's Overconfident Medical Summaries Could Be Dangerous

Title: "Sounds Impressive... But for Whom?" Why AI's Overconfident Medical Summaries Could Be Dangerous

Content: Medical research thrives on precision — but humans and AIs both love to overgeneralise with AI-generated medical summaries. New research shows large language models routinely turn cautious medical claims into sweeping, misleading statements. Even the best models aren’t immune — and the problem could quietly distort how science is understood and applied.

Why AI-Generated Medical Summaries Could Be Misleading

“In a randomised trial of 498 European patients with relapsed or refractory multiple myeloma, the treatment increased median progression-free survival by 4.6 months, with grade three to four adverse events in 60 per cent of patients and modest improvements in quality-of-life scores, though the findings may not generalise to older or less fit populations.”

From nuance to nonsense: how ‘generics’ mislead

Enter AI. And it’s making the problem worse.

Dropped qualifiers

Flattened nuance

Turned cautious claims into confident-sounding generics

Why is this happening?

Partly, it’s in the training data. If scientific papers, press releases and past summaries already overgeneralise, the AI inherits that tendency. And through reinforcement learning — where human approval influences model behaviour — AIs learn to prioritise sounding confident over being correct. After all, users often reward answers that feel clear and decisive.

The stakes? Huge.

Nearly half already use AI to summarise scientific work. 58% believe AI outperforms humans in this task.

What needs to change?

Editorial guidelines need to explicitly discourage generics without justification. Researchers using AI summaries should double-check outputs, especially in critical fields like medicine. This issue highlights the ongoing challenge of ensuring responsible AI development, a topic gaining traction globally, including efforts in places like Taiwan’s AI Law Is Quietly Redefining What “Responsible Innovation” Means.

Models should be fine-tuned to favour caution over confidence. Built-in prompts should steer summaries away from overgeneralisation. This is crucial for developing ProSocial AI that benefits society responsibly, rather than generating misleading content. The broader discussion around AI and ethics is becoming increasingly important as AI integrates into more sensitive areas.

Tools that benchmark overgeneralisation — like the methodology in our study — should become part of AI model evaluation before deployment in high-stakes domains. This is especially true for applications in healthcare, where precision is paramount, as discussed in detail by researchers in publications such as Nature Medicine[^1].

So… next time your chatbot says “The drug is effective,” will you ask: for whom, exactly?

[^1]: Nature Medicine: Large language models in medicine