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
- 1Institute of the History, Philosophy and Ethics of Medicine, Ulm University, Ulm, Germany
- 2Institute of Neural Information Processing, Ulm University, Ulm, Germany
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. Author summary: The use of Artificial Intelligence (AI) in diagnostics based on medical images is increasing. AI can automatically analyze images, quickly detect patterns, and support physicians. However, using this technology in patient care raises ethical challenges and requires ethical and regulatory protection. In April and May 2025, we screened official documents to identify ethical challenges of the ethical and regulatory framework of the diagnostic technology’s behavior on the international and European Union level. The documents were thematically and ethically analyzed by using the ethical principles of autonomy, beneficence, non-maleficence, and (social) justice. We found that the framework consists of five broad themes: (i) data basis, (ii) reliability, (iii) comprehensibility, (iv) disclosure, and (v) usability. All ethical principles are supported, but they are limited by a wide range of technical methods. Insufficient security measures for protecting data, and the race between developing attack and defense mechanisms, hinder maintaining safety. Thereby, all ethical principles could be harmed. In conclusion, we found that image-based diagnostic AI systems are protected by similar ethical guidance and regulations as medical AI systems for general purposes. We recommend establishing a regulatory framework that addresses the system’s specific challenges in order to sufficiently protect data and its target functions.
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BibTeX
@article{parchmann2026ethical,
title = {{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}},
author = {Nina Parchmann and Marcin Orzechowski and Daniel Braun and Florian Steger},
journal = {PLOS Digital Health},
year = {2026},
month = oct,
doi = {10.1371/journal.pdig.0001781},
url = {https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001781},
note = {CC BY},
}
RIS
TY - JOUR
TI - 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
AU - Nina Parchmann
AU - Marcin Orzechowski
AU - Daniel Braun
AU - Florian Steger
PY - 2026
DA - 2026/10/01
JO - PLOS Digital Health
DO - 10.1371/journal.pdig.0001781
UR - https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001781
AB - 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. Author summary: The use of Artificial Intelligence (AI) in diagnostics based on medical images is increasing. AI can automatically analyze images, quickly detect patterns, and support physicians. However, using this technology in patient care raises ethical challenges and requires ethical and regulatory protection. In April and May 2025, we screened official documents to identify ethical challenges of the ethical and regulatory framework of the diagnostic technology’s behavior on the international and European Union level. The documents were thematically and ethically analyzed by using the ethical principles of autonomy, beneficence, non-maleficence, and (social) justice. We found that the framework consists of five broad themes: (i) data basis, (ii) reliability, (iii) comprehensibility, (iv) disclosure, and (v) usability. All ethical principles are supported, but they are limited by a wide range of technical methods. Insufficient security measures for protecting data, and the race between developing attack and defense mechanisms, hinder maintaining safety. Thereby, all ethical principles could be harmed. In conclusion, we found that image-based diagnostic AI systems are protected by similar ethical guidance and regulations as medical AI systems for general purposes. We recommend establishing a regulatory framework that addresses the system’s specific challenges in order to sufficiently protect data and its target functions.
SN - 2767-3170
ER -