ArticleBioData mining2026
A crisis of overconfidence: Why confidence, not accuracy, is the real risk in clinical AI.
Article in BioData mining, 2026. 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.
- Accuracy, Self-Reported Confidence, and Overconfidence of Large Language Models in Endodontics: An Evaluation Using National Specialty Examination Questions.Medical science monitor : international medical journal of experimental and clinical research · 2026Article
- Artificial Intelligence-Assisted Colonoscopy for Colorectal Lesion Detection: Current Evidence, Challenges, and Future Directions.Journal of clinical medicine · 2026Review
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
Language models today are trained to convey confidence in their outputs, regardless of whether those outputs are correct. The alignment methods we use to make them helpful also push them toward unwarranted certainty, rewarding decisive answers over appropriate hedging. As these foundation models enter high-stakes domains such as science and medicine, this disconnect between how sure they sound and how accurate they are can become dangerous. Here, we examine why post-training degrades a model’s sense of uncertainty, and we review techniques that can bring expressed confidence back in line with actual reliability. Through this, we argue that trustworthy AI means treating calibration as a core design goal.
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.