ArticlePLOS digital health2026
The ethical and regulatory framework of medical artificial intelligence (AI) system's behavioral performance for image-based diagnostics: An ethical evaluation of the International and European Union Scope.
Article in PLOS digital health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
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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.
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.
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0 citing papers in PubMed.
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Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The use of Artificial Intelligence (AI) for image-based diagnostics is rising rapidly and enables new opportunities for improving clinical diagnostics through high precision and a quick identification of anomalies. However, a technology characterized by a high variability of image data and the opaqueness of AI models within patient care raises ethical challenges and requires protection by an appropriate ethical and regulatory framework. To identify ethical challenges of the ethical and regulatory framework of the behavioral performance of medical AI systems for image-based diagnostics on the international and European Union level, we searched and screened documents in April and May 2025. The documents were thematically evaluated before conducting an ethical analysis. We identified regulations and ethical recommendations that could be divided into five thematic categories: The (i) data basis is regulated by data standardization, data assessment, and data quality. (ii) Reliability considers assessments, the intended performance, robustness, accuracy, and compliance, whereas the (iii) comprehensibility centers on explainability, understandability, and traceability. The (iv) disclosure of the system considers transparency, operational disclosure, and collaborations, and the (v) usability is regulated on resources and competency. This framework could support the autonomy, beneficence, non-maleficence, and (social) justice, however allows a wide range of compliant technical measures that vary in effectiveness. The insufficient technical feasibility to provide security measures against malicious data alteration as well as the dynamic development of attack and defense mechanisms severely challenge the maintenance of a compliant safe and secure diagnostic AI system. Thereby, the autonomy, beneficence, non-maleficence, and (social) justice could be jeopardized. In conclusion, AI image-based diagnostic systems are regulated by a similar ethical and regulatory framework as general medical AI systems. To protect data and the intended performance, sufficient protection through a regulatory framework is necessary that responds to the challenges of image-based AI diagnostic systems.
Identifiers
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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.