Legal and Regulatory Considerations for Artificial Intelligence Use in Medical and Radiology Research

Abstract

Background

Artificial intelligence (AI) is increasingly integrated into medical imaging and radiology research, improving image acquisition, interpretation, workflow efficiency, and decision support. However, its rapid adoption has introduced complex legal and regulatory challenges involving patient privacy, data governance, algorithm validation, liability, intellectual property, transparency, cybersecurity, and ethical oversight. A comprehensive understanding of the evolving legal landscape is essential for the safe, effective, and legally compliant implementation of AI in radiology.

Methods

A narrative review of the published literature and current regulatory guidance was conducted. Relevant international legal and regulatory frameworks governing AI in medical imaging were evaluated, including the European Union Artificial Intelligence Act, General Data Protection Regulation, U.S. Food and Drug Administration guidance on Software as a Medical Device (SaMD), Health Insurance Portability and Accountability Act, World Health Organization recommendations, International Medical Device Regulators Forum principles, and selected national regulations. Key topics reviewed included data protection, informed consent, AI validation, regulatory approval pathways, post-market surveillance, cybersecurity, algorithmic bias, explainability, and medico-legal responsibility.

Results

Current regulatory frameworks increasingly classify AI-enabled radiology software as SaMD, requiring risk-based evaluation, clinical validation, quality management systems, and continuous post-market performance monitoring. Regulatory authorities emphasize transparency, human oversight, data security, and lifecycle governance of AI systems. Nevertheless, important legal challenges remain, including liability for AI-assisted diagnostic errors, ownership of AI-generated outputs, secondary use of imaging data, cross-border data sharing, algorithmic bias, and regulation of adaptive or continuously learning AI models.

Conclusions

The legal framework governing AI in radiology is evolving in parallel with technological innovation. Responsible integration of AI into clinical practice and research requires harmonized international regulations, robust data governance, standardized validation protocols, transparent algorithm development, and clearly defined legal accountability. Continued collaboration among radiologists, researchers, regulators, policymakers, industry, and legal experts will be essential to ensure that AI technologies remain safe, ethical, equitable, and trustworthy while maximizing their potential to improve patient care.

Overview

Using artificial intelligence (AI) in radiology and medical imaging research is increasingly permissible worldwide, but it sits at the intersection of four distinct regulatory domains: patient data privacy law, human-subjects research ethics, medical device/software regulation, and publication/authorship ethics. A radiologist conducting subspecialty research with AI tools (image analysis models, large language models [LLMs] for literature synthesis, or generative AI for manuscript drafting) must satisfy requirements across all four domains simultaneously, since compliance in one area (e.g., de-identification) does not automatically satisfy another (e.g., institutional review board [IRB] approval or Food and Drug Administration [FDA] clearance). ^{1 2} The interconnected legal domains that collectively determine regulatory compliance for AI research in radiology are depicted in Fig. 1 .

Fig. 1.

Overlapping regulatory domains governing AI use in radiology research. Four-set diagram showing that legally compliant AI research sits at the intersection of patient data privacy law (HIPAA, GDPR, DPDP Act), IRB/ethics-committee oversight, medical device/SaMD regulation (FDA, CE, CDSCO), and publication/authorship ethics. AI, artificial intelligence; CDSCO, Central Drugs Standard Control Organization; CE, Conformité Européenne; DPDP Act, Digital Personal Data Protection Act; FDA, Food and Drug Administration; GDPR, General Data Protection Regulation; HIPAA, Health Insurance Portability and Accountability Act; IRB, institutional review board; SaMD, Software as a Medical Device.

Patient Data Privacy: HIPAA, GDPR, and India's DPDP Act

In the United States, the Health Insurance Portability and Accountability Act (HIPAA) governs any AI tool that touches Protected Health Information (PHI). A Business Associate Agreement (BAA) is legally mandatory before transmitting patient DICOM (Digital Imaging and Communications in Medicine) studies to any AI vendor or cloud LLM; without a signed BAA, sending PHI to a service is a HIPAA violation regardless of the vendor's security claims. ^{3 4} Two lawful de-identification pathways exist: Safe Harbor (removing 18 specified identifiers) and Expert Determination (a qualified statistician certifies re-identification risk is very small). ⁴ Notably, medical images themselves are not on HIPAA's 18-identifier list, so burned-in PHI, facial reconstructions from computed tomography/magnetic resonance imaging, and metadata in DICOM headers require separate scrutiny and dedicated de-identification pipelines (e.g., pixel redaction plus DICOM tag scrubbing). ^{2 5} HIPAA's scope is also limited, as it does not directly apply to non-covered entities such as pure technology or AI companies that process appropriately de-identified imaging data. Consequently, many AI-in-radiology research pipelines emphasize robust de-identification practices to safeguard patient privacy, rather than depending solely on institutional coverage. ¹

In the European Union (EU), GDPR (General Data Protection Regulation) treats health data as “special category” data requiring an additional lawful basis beyond simple consent. For AI/machine learning research specifically, GDPR provides an alternative to prior explicit consent: processing that is “necessary for scientific research purposes,” provided it rests on EU/national law and includes safeguards such as pseudonymization and encryption. ⁶ If imaging data are genuinely anonymized such that no individual is identifiable, it falls outside GDPR's definition of “personal data” altogether, freeing researchers from consent requirements—though achieving true anonymization (vs. reversible pseudonymization) is technically demanding for imaging data. ⁶

In India, the Digital Personal Data Protection (DPDP) Act, 2023 is now the controlling data law. It generally requires explicit, informed, and revocable consent for processing personal data; however, it provides a specific exemption for research, archiving, or statistical purposes. Under this provision, personal data may be processed without consent if it is not used to make decisions affecting specific individuals and the processing complies with the required safeguards and standards. ^{7 8} Hospitals and AI platforms handling sensitive health data are classified as “Significant Data Fiduciaries,” triggering obligations to appoint a Data Protection Officer and conduct periodic audits. ⁸ The comparative regulatory landscape governing AI applications in medical imaging research across different jurisdictions is summarized in Table 1 .

Table 1. Comparative overview of regulatory frameworks governing artificial intelligence use in medical imaging research across major jurisdictions.

Jurisdiction	Primary law	Key legal pathway for AI research	Consent requirement
United States	HIPAA	Safe harbor or expert determination de-identification; BAA with vendors 3 4	Waivable via IRB if de-identified/minimal risk 2
European Union	GDPR	“Scientific research” lawful basis with pseudonymization/encryption 6	Not required if data are truly anonymized 6
India	DPDP Act, 2023	Research/statistical processing may be permitted under applicable exemptions and safeguards 7	Generally required; exempted for aggregate research use 8

Abbreviations: AI, artificial intelligence; BAA, Business Associate Agreement; DPDP Act, Digital Personal Data Protection Act; GDPR, General Data Protection Regulation; HIPAA, Health Insurance Portability and Accountability Act; IRB, institutional review board.

Institutional Review Board/Ethics Committee Approval

Nearly all AI-based retrospective imaging research involving identifiable patient data requires either informed consent or an IRB-approved waiver of consent. U.S. federal regulations (45 CFR 46.116) permit a full or partial waiver when four conditions are jointly met: the research poses no more than minimal risk, the study could not practicably be conducted without the waiver, the waiver does not adversely affect subjects' rights and welfare, and any use of identifiable data could not practicably be done otherwise. ⁹ Retrospective chart and image reviews—the most common radiology-AI research design—routinely qualify for this waiver, which is why most published radiology-AI validation studies note IRB approval “with waiver of informed consent” rather than obtaining prospective consent from thousands of patients. The U.S. Department of Health and Human Services Office for Human Research Protections (SACHRP) has explicitly noted that AI research, even where it technically falls under human-subjects oversight, will “very likely” qualify for this waiver because such studies typically cannot be practicably conducted with individual consent from every patient in a large training/validation dataset.

India's Indian Council of Medical Research 2023 Ethical Guidelines for AI in Biomedical Research and Healthcare mirror this structure: written informed consent is the default for human-subject AI research, but a consent waiver is explicitly permitted “in case of anonymized data for retrospective studies,” while institutional ethics committee (IEC) approval remains mandatory for any use of retrospective data to develop or validate an AI algorithm. These guidelines also mandate that ethics committees include members trained in AI/big-data concepts and require public disclosure of algorithm audit results. ¹⁰

Software/Device Regulation: When Research Tools Cross into SaMD

A critical legal distinction is between AI used purely as a research/analysis tool versus AI intended for clinical diagnosis, which the FDA and equivalent regulators classify as “Software as a Medical Device” (SaMD). Under the FD&C Act, software intended to “treat, diagnose, cure, mitigate, or prevent disease” is a regulated medical device requiring premarket clearance—typically through pathways such as 510(k) clearance, De Novo classification, or Premarket Approval depending on device risk and intended use, or the more rigorous Premarket Approval pathway for high-risk tools. ¹¹ Purely investigational or academic use of an AI algorithm on de-identified retrospective data for research purposes generally does not trigger this device-regulation requirement, but if a researcher intends to deploy the same tool for real-time clinical decision support, separate regulatory clearance becomes necessary. ¹² The FDA's January 2025 draft guidance on AI-enabled device lifecycle management, alongside its Good Machine Learning Practice principles (covering data quality, bias mitigation, transparency, and continuous monitoring), sets the evolving compliance bar for any AI tool moving from research into clinical use. ¹²

Using LLMs and Generative AI in the Research and Writing Process

For literature review, data analysis assistance, and manuscript drafting, radiology journals have converged on a consistent set of rules, established first by Radiological Society of North America's Radiology and echoed by the Korean Journal of Radiology, Frontiers in Radiology, and Elsevier's imaging journals:

• AI tools (ChatGPT, LLMs, chatbots) can never be listed as an author or co-author, since they cannot take accountability for accuracy, integrity, or originality—a core ICMJE (International Committee of Medical Journal Editors) authorship requirement. ¹⁴

• Authors must disclose AI use at submission, specifying the tool name, version, manufacturer, and date of use, typically in the Methods or Acknowledgments section. ¹⁴

• Human authors bear full responsibility for verifying accuracy and absence of plagiarism in any AI-assisted text, including checking citations, since “referencing AI-generated material as the primary source is not acceptable” per ICMJE. ¹⁴

• Generative AI must not be used to create or alter scientific images/figures unless the AI use is itself part of the formal research design (e.g., studying generative image models), in which case it must be described reproducibly in Methods. ¹⁵

• Reviewers and editors are generally barred from feeding manuscripts under review into public AI tools, since this breaches confidentiality of unpublished research.

• A comparative review of imaging journal policies found that journals with explicit AI-use policies had measurably higher citation and impact metrics, suggesting policy clarity itself correlates with research quality signaling. ¹⁵

Practically, this means a subspecialist radiologist can legally and ethically use LLMs such as ChatGPT, Claude, or Perplexity to summarize literature, refine phrasing, or structure a manuscript, provided the use is disclosed, the output is independently verified against primary sources, and no fabricated citations or AI-authored conclusions are presented as original findings. ¹⁷

Practical Compliance Checklist for AI-Assisted Radiology Research

• Before starting: obtain IEC/IRB approval (or documented waiver) covering the specific dataset and AI methodology; do not treat de-identification alone as a substitute for ethics approval. ¹¹

• Data handling: use validated DICOM de-identification pipelines (tag scrubbing plus pixel-level burned-in PHI redaction) before any image leaves institutional infrastructure or before uploading to a cloud AI tool. ^{2 5}

• Vendor contracts: for any cloud-based AI/LLM service touching patient data, confirm a signed BAA (United States) or Data Processing Agreement under GDPR Article 28 (EU), and verify DPDP Act compliance documentation for Indian data. ^{3 8}

• Public/consumer LLMs: never input real, identifiable patient images or reports into public-facing chatbots without an enterprise/BAA-covered agreement; use synthetic or fully de-identified case examples for general exploration.

• Publication: disclose all generative-AI tool usage with name/version/manufacturer in the manuscript, retain full responsibility for verifying AI-assisted content, and never list AI as an author. ¹⁴

• Clinical translation: if research findings lead to deploying an AI tool for actual diagnostic use (vs. academic validation), separately evaluate FDA/CE/Central Drugs Standard Control Organization device-regulation requirements before clinical implementation. ^{11 12}

The proposed legally aligned workflow outlining the progression of AI research in radiology from initial conception through validation and potential clinical implementation is illustrated in Fig. 2 .

Fig. 2.

Legally aligned workflow for conducting AI-based radiology research from conception to potential clinical deployment. Ten-step flowchart with two decision points: IRB/IEC consent-waiver determination and the clinical-deployment decision that triggers the device-regulation pathway. AI, artificial intelligence; IEC, institutional ethics committee; IRB, institutional review board.

Across the United States, EU, and India, the overarching legal principle is consistent even where specific mechanisms differ: identifiable patient imaging data can be legally used in AI research when de-identification, ethics-committee oversight, and appropriate lawful-basis documentation are in place, while any AI output feeding into publications or clinical decisions must remain fully attributable to and verified by a human researcher or clinician. ^{14 15} The jurisdiction-specific differences in regulatory pathways, data governance requirements, consent considerations, and safeguards for AI-based radiology research are compared in Fig. 3 .

Fig. 3.

Comparative overview of legal pathways for AI-based radiology research across major jurisdictions. Matrix summarizing the core data law, typical legal basis, consent status for retrospective imaging AI studies, and key safeguards in the United States, European Union, and India. AI, artificial intelligence.

Summary Addendum: AI-Enhanced Legal and Methodological Counsel

Deep, source-grounded research workflows are now central to safely navigating the rapidly evolving legal landscape surrounding AI in medicine and radiology. Tools such as Perplexity's Deep Research mode can systematically decompose a complex question (for example, “regulation of AI-enabled SaMD in imaging across the United States, EU, and India”), retrieve and cross-verify legislation, guidelines, and recent case studies, and synthesize them into a structured report with traceable citations and explicit uncertainty notes. When used under appropriate institutional governance, this kind of automated multi-pass evidence gathering does not replace the radiologist's judgment; instead, it acts as a disciplined augmentation, ensuring that both the legal framing and methodological choices in an AI study are continuously updated to reflect the latest regulatory guidance and journal policies.

At the same time, multi-model comparison frameworks such as Perplexity's Model Council provide a practical way to triangulate expert opinions and reduce the chance of single-model bias when designing meta-analyses or policy-sensitive research in radiology. By running the same question across several frontier models and then synthesizing points of agreement, disagreement, and unique insights, the council-style workflow mirrors the logic of meta-analysis: multiple “studies” (models) are interrogated in parallel, concordance and heterogeneity are explicitly surfaced, and the radiologist-researcher remains responsible for adjudicating conflicts using primary literature and domain expertise. Used in this way—transparent, cited, and subordinate to human oversight—Deep Research and Model Council can act as an expert assistant and methodological counsel, helping subspecialty radiologists design stronger, more defensible AI studies and meta-analyses without diluting accountability. The potential role of AI-assisted deep-research and multi-model comparison workflows as supportive methodological and legal counsel in radiology research, while maintaining human oversight and accountability, is depicted in Fig. 4 .

Fig. 4.

Conceptual use of AI deep-research and model-council workflows as AI-assisted legal and methodological counsel in radiology research. Inputs flow through query decomposition, multi-source retrieval, cross-verification, and citation-rich reporting; parallel frontier models are compared and synthesized, with the radiologist retaining full oversight and final accountability. AI, artificial intelligence.

This manuscript is a narrative review providing a practical overview of current legal and regulatory considerations for AI use in radiology research, rather than a systematic review or a novel proposed legal framework.