ArticleNPJ digital medicine2026
Seven deadly sins in artificial intelligence for digital medicine.
Article in NPJ digital medicine, 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.
- The precision ceiling: information-theoretic accuracy bounds for responsible AI health screening.Frontiers in digital health · 2026Article
- How Implementation Context Shapes Clinician Acceptance and Engagement with Guideline-Based Clinical Decision Support Systems in Primary Care: A Qualitative Study.Journal of healthcare leadership · 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
Abstract
Artificial intelligence (AI) is increasingly embedded in clinical environments, raising questions of trust, fairness, empathy, and governance. The ethical terrain surrounding AI in medicine remains unstable despite its rapid adoption. We introduce the "Seven Deadly Sins of AI in Medicine", a conceptual framework of recurring systemic failure modes: (i) Blind Trust, (ii) Overregulation, (iii) Dehumanization, (iv) Misaligned Optimization, (v) Overinforming and False Forecasting, (vi) Misapplied Statistics, and (vii) Self-Referential Evaluation. The framework was developed through systematic synthesis of scientific literature, clinical guidelines, and regulatory frameworks prior to any empirical data collection. To validate this pre-established framework, we conducted a global, cross-professional opinion poll of 914 stakeholders from 143 countries between July 2024 and March 2025. Results confirmed broad agreement with each pre-identified risk, revealing cross-cultural convergence in ethical concern alongside persistent divides in attitudes toward regulation-particularly between technologically advanced nations and emerging economies. We further propose an inversion of the framework into seven cardinal virtues for AI in medicine, offering actionable principles to guide responsible development and governance. The goal is to move beyond scattered ethical guidelines toward a unified diagnostic tool for trustworthy, human-centered medical AI.
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.