{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,11,1]],"date-time":"2026-11-01T00:06:38Z","timestamp":1793491598939,"version":"4.5.0"},"reference-count":77,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T00:00:00Z","timestamp":1778198400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Reprod. Health"],"abstract":"<jats:p>Artificial intelligence and algorithmic tools are increasingly integrated into reproductive healthcare, including fertility tracking applications, diagnostic systems, and clinical decision support tools. While these technologies may support improved access, personalisation, and decision making, their expansion is also associated with significant ethical and governance challenges in a domain shaped by legal risk, social norms, and deeply personal health decisions. These challenges include limited transparency, which may constrain informed consent and autonomy; risks of bias arising from unrepresentative data; heightened privacy concerns linked to sensitive reproductive information; and unclear accountability across developers, clinicians, and institutions. Importantly, these risks are not uniform, but are shaped by legal, social, and health system conditions, and may be particularly pronounced in low resource and legally restrictive settings. Despite growing attention to AI ethics, existing frameworks and guidance often remain high level and do not fully address how these challenges should be governed in context-sensitive reproductive health settings. This paper advances a governance-oriented perspective that integrates Healthcare 5.0 and reproductive justice frameworks to examine how these challenges emerge within adaptive socio-technical systems. It argues that existing governance approaches remain insufficiently responsive to structural and intersectional vulnerabilities and proposes a context aware approach that embeds human oversight, explainability, privacy protection, and accountability as core system level requirements. By positioning reproductive health as a critical test case for AI governance, this perspective highlights the need for enforceable, context-sensitive approaches that prioritise equity, autonomy, and the lived realities of affected populations.<\/jats:p>","DOI":"10.3389\/frph.2026.1771550","type":"journal-article","created":{"date-parts":[[2026,5,8]],"date-time":"2026-05-08T05:50:48Z","timestamp":1778219448000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":3,"title":["Regulating algorithmic tools in reproductive health: ethical and legal challenges"],"prefix":"10.3389","volume":"8","author":[{"given":"Collins Chibueze","family":"Anokwuru","sequence":"first","affiliation":[{"name":"Corona Management Systems","place":["Abuja, Nigeria"]},{"name":"Department of Public Health, Federal University of Technology","place":["Owerri, Nigeria"]},{"name":"Youth in Research Hub","place":["Enugu, Enugu, 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