Article
Legal and Regulatory Challenges of Artificial Intelligence in Healthcare Startups: A Narrative Review
Artificial intelligence (AI) startups are developing tools for diagnosis, triage, clinical decision support, remote monitoring and health-system administration. Their translation is governed by overlapping requirements for medical devices, data protection, biomedical research, professional practice, cybersecurity, consumer protection and intellectual property. This narrative review examines the principal legal and regulatory challenges faced by healthcare AI startups, with India as the primary jurisdiction and the European Union and United States as comparators. Openly accessible peer-reviewed literature and official regulatory materials published from January 2018 to August 2026 were reviewed, together with earlier foundational instruments. The analysis identifies six recurrent pressure points: uncertain product classification; evidence requirements that may not fit rapidly changing software; lawful access to representative health data; bias, transparency and human oversight; fragmented liability across developers, clinicians and institutions; and inconsistent rules across export markets. India now has a more defined policy environment through the Medical Devices Rules, the 2026 guidance on medical device software, the Digital Personal Data Protection framework, the Indian Council of Medical Research ethical guidelines and national AI-governance guidance. These instruments are complementary, but their scopes, commencement schedules and enforcement routes are not identical. Startups should therefore treat compliance as a product-lifecycle function: define intended use before development, establish data provenance and contractual rights, generate population-relevant clinical evidence, document human oversight, control software changes, allocate responsibility contractually and monitor performance after deployment. Proportionate regulatory support, common technical standards, regulatory sandboxes and clearer coordination among health, data and AI authorities could reduce uncertainty without lowering patient-safety safeguards.