ReviewFrontiers in artificial intelligence2025
In the search for the perfect prompt in medical AI queries.
Review in Frontiers in artificial intelligence, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
2 citing papers in PubMed.
- Risk of Guideline Overselling and Certainty Inflation in GPT-5 Responses: A Statement-Based Analysis of Four ESGE Colorectal Guidelines.Endoscopy international open · 2026Article
- Assessment of brachial plexus and upper-extremity peripheral nerve injuries at the anatomical level using multimodal generative models.Frontiers in surgery · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The evaluation of medical Artificial Intelligence (AI) systems presents significant challenges, with performance often varying drastically across studies. This narrative review identifies prompt quality-the way questions are formulated for the AI-as a critical yet under-recognized variable influencing these outcomes. The analysis explores scientific literature published between January 2018 and August 2025 to investigate the impact of prompt engineering on the perceived accuracy and reliability of conversational AI in medicine. Results reveal a "performance paradox," where AI sometimes surpasses human experts in controlled settings yet underperforms in broader meta-analyses. This inconsistency is strongly linked to the type of prompt used. Critical concerns are highlighted, such as "prompting bias," which may invalidate study conclusions, and AI "hallucinations" that generate dangerously incorrect information. Furthermore, a significant gap exists between the optimal prompts formulated by experts and the natural queries of the general public, raising issues of safety and health equity. In the end we were interested in finding out what the optimal balance existed between the complexity of a prompt and the value of the generated response, and, in this context, whether we could attempt to define a path toward identifying the best possible prompt.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.