Evidence map›Paper›PMID 41986592›Full record

ArticleNPJ digital medicine2026

Seven deadly sins in artificial intelligence for digital medicine.

Heimo Müller, Vimla L Patel, Edward H Shortliffe, Igor Jurisica, Andreas Holzinger

Abstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Heimo MüllerMachine Learning and Information Science Group, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, Graz, Austria.
Vimla L PatelThe New York Academy of Medicine, New York, NY, USA.
Edward H ShortliffeDepartment of Biomedical Informatics, Vagelos College of Physicians and Surgeons, Columbia University, New York, NY, USA.
Igor JurisicaSchroeder Arthritis Institute and Krembil Research Institute, University Health Network; Departments of Medical Biophysics and Computer Science, and Faculty of Dentistry, University of Toronto; Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia; School of Digital Public Health, Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE, Toronto, ON, Canada.
Andreas HolzingerMachine Learning and Information Science Group, Diagnostic and Research Center for Molecular BioMedicine, Medical University of Graz, Graz, Austria. andreas.holzinger@human-centered.ai.

Funding

Austrian Science Fund P-32554
6 · The paper itself

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

PMID41986592
PMCPMC13369183

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

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.