The Risks of Over-Generalization in Medicine and the Impact of AI

In the medical field, a fundamental principle is to avoid overstating findings beyond what the data support. Clinicians and researchers learn early on that precise communication is vital. Medical journals and peer reviewers demand careful, qualified conclusions, leading researchers to hedge their statements to prevent overreach. For example, a typical clinical trial report might state: "In a randomized study with 498 European patients suffering from relapsed multiple myeloma, the treatment extended median progression-free survival by 4.6 months, with serious adverse events in 60% of patients and modest improvements in quality of life; however, these results may not apply to older or less fit populations." Such detailed statements aim for accuracy but can be complex for audiences to interpret. Consequently, these cautious conclusions are often simplified into broad claims like: "The treatment improves survival and quality of life," or "The drug is safe and effective for patients with multiple myeloma." While clearer, these summaries frequently overstate the actual evidence, implying benefits are universal or guaranteed.

Researchers refer to these broad claims as "generics"—statements that lack explicit qualifiers about context, population, or conditions. Phrases such as "the treatment is effective" or "the drug is safe" sound authoritative but fail to specify the scope, potentially leading to misapplication of findings. This tendency to overgeneralize is not new. Past studies have shown that many published research articles extend findings beyond the studied populations, often without sufficient justification. These generalizations are partly driven by cognitive biases: humans tend to favor simple, sweeping explanations when faced with complex data, even unconsciously stretching conclusions beyond what the evidence supports.

The rise of artificial intelligence (AI), particularly large language models (LLMs) like ChatGPT, DeepSeek, LLaMA, and Claude, threatens to intensify this problem. Recent research tested these models' ability to summarize high-quality medical articles and abstracts. Results revealed a high prevalence—up to 73%—of overgeneralizations in AI-generated summaries. These models frequently stripped away qualifiers, simplified nuanced findings, and translated specific conclusions into broad, unwarranted claims. When compared to human experts, LLMs were nearly five times more likely to produce such sweeping generalizations. Interestingly, newer models such as ChatGPT-4 and DeepSeek tended to be even more prone to overgeneralize.

This behavior may stem from the training data, which often contain overgeneralized scientific texts, and from the reinforcement learning processes that favor confident, assertive responses. As a result, AI tools used to summarize medical literature risk distorting scientific understanding, especially in high-stakes fields like medicine where subtle differences in population, effect size, and uncertainty are critical.

To mitigate these issues, clear editorial guidelines for human researchers can promote more precise language. When employing AI for summarization, choosing models with better accuracy, such as Claude, and designing prompts that encourage cautious language can help. Moreover, benchmarking AI models' tendencies to overgeneralize before deploying them in clinical or research settings is essential.

Ultimately, in medicine, both data collection and communication demand rigor and precision. Recognizing the shared human and AI tendency to overgeneralize underscores the importance of scrutinizing how results are presented. Ensuring careful language and responsible AI usage is vital to delivering the right treatments to the right patients, backed by appropriately scoped evidence.