ArticleMedicine, health care, and philosophy2025
The need for epistemic humility in AI-assisted pain assessment.
Article in Medicine, health care, and philosophy, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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
3 citing papers in PubMed.
- Engineering framework for curiosity-driven and humble AI in clinical decision support.BMJ health & care informatics · 2026Article
- Beyond overconfidence: Embedding curiosity and humility for ethical medical AI.PLOS digital health · 2026Article
- Digital Psychiatry with Chatbot: Recent Advances and Limitations.Clinical psychopharmacology and neuroscience : the official scientific journal of the Korean College of Neuropsychopharmacology · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
It has been difficult historically for physicians, patients, and philosophers alike to quantify pain given that pain is commonly understood as an individual and subjective experience. The process of measuring and diagnosing pain is often a fraught and complicated process. New developments in diagnostic technologies assisted by artificial intelligence promise more accurate and efficient diagnosis for patients, but these tools are known to reproduce and further entrench existing issues within the healthcare system, such as poor patient treatment and the replication of systemic biases. In this paper we present the argument that there are several ethical-epistemic issues with the potential implementation of these technologies in pain management settings. We draw on literature about self-trust and epistemic and testimonial injustice to make these claims. We conclude with a proposal that the adoption of epistemic humility on the part of both AI tool developers and clinicians can contribute to a climate of trust in and beyond the pain management context and lead to a more just approach to the implementation of AI in pain diagnosis and management.
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What Socratic 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 Socratic graph.