ReviewBMC medical informatics and decision making2025
On the practical, ethical, and legal necessity of clinical Artificial Intelligence explainability: an examination of key arguments.
Review in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers, 3 of them syntheses that pooled 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.
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
18 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Ethical challenges to patient autonomy in the era of artificial intelligence: a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Analysis of postpartum and other depression detection using machine learning and deep learning techniques: a systematic review.Frontiers in artificial intelligence · 2026Pooled it
- Artificial intelligence-oriented predictive model for the risk of postpartum depression: a systematic review.Frontiers in public health · 2025Pooled it
- Responsible Clinical AI in Dentistry: Trust, Professional Autonomy, and Perceptions of Accountability Across Stakeholders in Romania-A Multidisciplinary Cross-Sectional Survey.Healthcare (Basel, Switzerland) · 2026Article
- Ethical Aspects of Artificial Intelligence in Precision Medicine for Obesity Management.JMA journal · 2026Review
- Artificial Intelligence in Implant Dentistry: Clinical Validity, Diagnostic Performance, Surgical Planning, and Medico-Legal Implications-A Narrative Review.Dentistry journal · 2026Review
- Integration of Federated Learning and Blockchain in Health Care: Tutorial on Medical Data, Architectures, Privacy, Security, and Regulatory Compliance.Journal of medical Internet research · 2026Article
- Ethical Responsibility in Medical AI: A Semi-Systematic Thematic Review and Multilevel Governance Model.Healthcare (Basel, Switzerland) · 2026Review
- Review
- Artificial intelligence and robotic technologies redefining precision and personalization in orthopedic surgery: a narrative review.Frontiers in bioengineering and biotechnology · 2026Review
- Empowering photodynamic therapy with artificial intelligence: current trends and future directions.Frontiers in oncology · 2026Review
- Real-world application of large language models for automated TNM staging using unstructured gynecologic oncology reports.NPJ precision oncology · 2025Article
- Artificial intelligence-based tools for precision diagnosis and treatment of neurofibromatosis type 1 associated peripheral and central glial tumors.Orphanet journal of rare diseases · 2025Review
- Artificial Intelligence in Orthopedic Surgery: Current Applications, Challenges, and Future Directions.MedComm · 2025Review
- Nanomaterials reshape the pulmonary mechanical microenvironment: novel therapeutic strategies for respiratory diseases.Frontiers in bioengineering and biotechnology · 2025Review
- AI agents in Alzheimer's disease management: challenges and future directions.Frontiers in aging neuroscience · 2025Article
- Application of artificial intelligence in gynecologic cancers: A bibliometric analysis.Digital healthArticle
- Interpretable machine learning prediction of in-hospital mortality in people with HIV: A cohort study in southeastern China.Digital healthArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The necessity for explainability of artificial intelligence technologies in medical applications has been widely discussed and heavily debated within the literature. This paper comprises a systematized review of the arguments supporting and opposing this purported necessity. Both sides of the debate within the literature are quoted to synthesize discourse on common recurring themes and subsequently critically analyze and respond to it. While the use of autonomous black box algorithms is compellingly discouraged, the same cannot be said for the whole of medical artificial intelligence technologies that lack explainability. We contribute novel comparisons of unexplainable clinical artificial intelligence tools, diagnosis of idiopathy, and diagnoses by exclusion, to analyze implications on patient autonomy and informed consent. Applying a novel approach using comparisons with clinical practice guidelines, we contest the claim that lack of explainability compromises clinician due diligence and undermines epistemological responsibility. We find it problematic that many arguments in favour of the practical, ethical, or legal necessity of clinical artificial intelligence explainability conflate the use of unexplainable AI with automated decision making, or equate the use of clinical artificial intelligence with the exclusive use of clinical artificial intelligence.
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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.