ArticleBMC medical informatics and decision making2020
Explainability for artificial intelligence in healthcare: a multidisciplinary perspective.
Article in BMC medical informatics and decision making, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 729 papers, 16 of them syntheses that pooled it.
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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.
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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.
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Who cites it
729 citing papers in PubMed, 16 syntheses or guidelines pooled it.
- Machine Learning and Deep Learning for the Diagnosis of Cervical Degenerative Diseases: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
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- Explainable Artificial Intelligence in Dentistry: A Systematic Review of Its Trust and Translation.International dental journal · 2026Pooled it
- Large Language Models in Colorectal Cancer Care and Clinical Decision Support: Systematic Review.Journal of medical Internet research · 2026Pooled it
- Artificial intelligence models in cerebral infarction: performance and applications in diagnosis, classification, grading, treatment, and disease course prediction-a systematic review.BMC medical informatics and decision making · 2026Pooled it
- Responsible artificial intelligence integration framework for psychiatric guidelines.The international journal of neuropsychopharmacology · 2026Guideline
- Why aren't they used? Systematic review of barriers to implementation of clinical decision support systems for early cancer detection in primary care.The British journal of general practice : the journal of the Royal College of General Practitioners · 2026Pooled it
- Machine Learning in Assessing Intraoperative Blood Loss: A Systematic Review and Meta-Analysis.International nursing review · 2026Pooled it
- Integrating aqli (rationale) and naqli (revealed) knowledge in AI-driven clinical decision support systems to enhance healthcare delivery for guiding termination of pregnancy among Muslim patients: a systematic review (1).BMC medical informatics and decision making · 2026Pooled it
- Artificial Intelligence-based detection of neuropsychiatric lupus: an exploratory meta-analysis of neuroimaging and multimodal biomarker models.Clinical and experimental medicine · 2026Pooled it
- Effectiveness of Machine Learning in Detecting Vessels Encapsulating Tumor Clusters in Hepatocellular Carcinoma: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- "First, do no harm" in the digital era: examining the practicality of the European Health Data Space proposal and ethical implications of artificial intelligence: A systematic literature review.BMC medical ethics · 2026Pooled it
- Explainable artificial intelligence (XAI) in medical imaging: a systematic review of techniques, applications, and challenges.BMC medical imaging · 2026Pooled it
- AI-enabled eye-movement and emerging multimodal frameworks for precision dyslexia screening and reading pattern analysis.Frontiers in medicine · 2026Pooled it
- Pooled it
- Explainable artificial intelligence in medical ultrasound: a WoSCC-based bibliometric analysis, evidence map, and Scopus concordance assessment.Frontiers in medicine · 2026Pooled it
- Trust in Generative AI for Health Information Consumption and the Effect of Learned Dependency: Randomized Controlled Experimental Study.Journal of medical Internet research · 2026Trial
- The VALUE of AI-Guided Communication: Enhancing Shared Decision-Making in Metabolic Bariatric Surgery Consultations Through a Metacognitive Framework.Obesity surgery · 2026Trial
- Are asynchronous or synchronous clinical decision support more likely to change provider behavior? A case study in dementia.Journal of the American Medical Informatics Association : JAMIA · 2026Trial
- Artificial intelligence-guided nanozyme engineering for chronic wound healing: from rational design to precision therapeutics.Bioactive materials · 2027Review
669 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
backgroundExplainability is one of the most heavily debated topics when it comes to the application of artificial intelligence (AI) in healthcare. Even though AI-driven systems have been shown to outperform humans in certain analytical tasks, the lack of explainability continues to spark criticism. Yet, explainability is not a purely technological issue, instead it invokes a host of medical, legal, ethical, and societal questions that require thorough exploration. This paper provides a comprehensive assessment of the role of explainability in medical AI and makes an ethical evaluation of what explainability means for the adoption of AI-driven tools into clinical practice.
methodsTaking AI-based clinical decision support systems as a case in point, we adopted a multidisciplinary approach to analyze the relevance of explainability for medical AI from the technological, legal, medical, and patient perspectives. Drawing on the findings of this conceptual analysis, we then conducted an ethical assessment using the "Principles of Biomedical Ethics" by Beauchamp and Childress (autonomy, beneficence, nonmaleficence, and justice) as an analytical framework to determine the need for explainability in medical AI.
resultsEach of the domains highlights a different set of core considerations and values that are relevant for understanding the role of explainability in clinical practice. From the technological point of view, explainability has to be considered both in terms how it can be achieved and what is beneficial from a development perspective. When looking at the legal perspective we identified informed consent, certification and approval as medical devices, and liability as core touchpoints for explainability. Both the medical and patient perspectives emphasize the importance of considering the interplay between human actors and medical AI. We conclude that omitting explainability in clinical decision support systems poses a threat to core ethical values in medicine and may have detrimental consequences for individual and public health.
conclusionsTo ensure that medical AI lives up to its promises, there is a need to sensitize developers, healthcare professionals, and legislators to the challenges and limitations of opaque algorithms in medical AI and to foster multidisciplinary collaboration moving forward.
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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.