How India is reshaping its regulatory approach to AI-enabled medical devices ahead of 2026
India is steadily emerging as one of the most important growth markets for AI-enabled medical technologies. As adoption accelerates across both public and private healthcare systems, the regulatory framework governing these technologies is also becoming more structured, particularly under the Medical Device Rules (MDR) 2017.
For manufacturers planning market entry in 2026–2027, understanding this evolving regulatory environment is essential. The way AI-based medical devices are defined, validated, and maintained throughout their lifecycle is now a central element of regulatory strategy—not a secondary consideration.
In India, AI-based medical technologies are not assigned a fixed regulatory category based solely on their technological characteristics. Instead, classification is determined by intended use and associated risk.
Software may be regulated as Software as a Medical Device (SaMD) when functioning independently, or as Software in a Medical Device (SiMD) when integrated into hardware systems.
Under the MDR 2017 framework, devices are classified into Classes A, B, C, or D depending on risk level, with higher scrutiny applied to applications in areas such as radiology, diagnostics, and oncology.
In this context, the intended use statement becomes one of the most critical regulatory elements, as it directly influences classification, evidence requirements, and approval pathways.
A distinctive aspect of India’s emerging approach to AI regulation is the recognition that machine learning systems are inherently dynamic.
To address this, the concept of an Algorithm Change Protocol (ACP) has become increasingly relevant. Rather than requiring re-evaluation for every algorithmic modification, the ACP allows manufacturers to define the conditions under which an algorithm may evolve after initial approval.
A well-structured ACP typically establishes the boundaries for algorithm updates, the validation methodology for changes, and the criteria that determine whether updates can be implemented within the existing approval framework.
From a regulatory perspective, this introduces a structured mechanism for managing innovation while maintaining oversight, particularly for adaptive AI systems.
For higher-risk devices, particularly those classified as Class C and D, there is an increasing expectation that performance data reflects the local population.
India’s healthcare diversity has driven regulators to place greater emphasis on validating AI performance in relevant clinical and demographic contexts.
In practice, this has led to the use of local benchmarking environments, including platforms such as BODH, which are designed to evaluate algorithm performance within Indian datasets.
This requirement reflects a broader regulatory objective: ensuring that AI systems demonstrate consistent clinical reliability across population-specific conditions, rather than relying solely on global validation data.
While ISO 13485 remains a foundational requirement for quality management systems, AI-based medical devices are subject to additional scrutiny regarding software transparency and system architecture.
Regulatory expectations now commonly extend to detailed documentation of software design, version control processes, and traceability across development stages.
Cybersecurity considerations are also integrated into the regulatory assessment, with emphasis on encryption standards, access control mechanisms, and system resilience.
In this context, regulators are not only evaluating device functionality, but also the integrity and controllability of the underlying software infrastructure.
Foreign manufacturers seeking to enter the Indian market are required to appoint an Indian Authorized Agent (IAA). This entity acts as the official regulatory interface and is responsible for managing submissions through the SUGAM portal, maintaining communication with regulatory authorities, and overseeing post-market compliance obligations.
This requirement forms a structural component of the regulatory system and is essential for market access.
Software updates are assessed based on their impact on device performance and intended use.
Minor updates may be addressed through notification procedures, while more substantial modifications—particularly those affecting output or clinical functionality—may require formal regulatory review or new submissions.
The classification of these updates is closely linked to the initial structure of the Algorithm Change Protocol, reinforcing the importance of defining software evolution pathways early in the regulatory planning process.
Cybersecurity has become an integral component of regulatory evaluation for AI-based medical devices.
Alignment with internationally recognized standards such as ISO/IEC 27001 and IEC 62443 is increasingly expected, alongside demonstrated capability for ongoing vulnerability management and timely system updates.
This reflects a broader regulatory shift toward continuous risk management rather than static compliance at the point of approval.
Regulatory approval in India does not conclude the manufacturer’s responsibility. Instead, it initiates an ongoing obligation to monitor real-world device performance.
Manufacturers are expected to track safety signals, monitor algorithm drift, and submit periodic safety update reports (PSURs) as part of post-market surveillance requirements.
This reinforces the concept that AI-based medical devices operate within a continuous regulatory lifecycle, rather than a fixed approval model.
The evolving regulatory framework in India reflects a broader shift toward lifecycle-based governance of AI technologies in healthcare.
For manufacturers, this requires a more integrated approach to regulatory planning, where classification strategy, algorithm design, validation methodology, and post-market processes are aligned from the outset.
Companies that anticipate these requirements early in development are better positioned to navigate regulatory review efficiently and avoid unnecessary delays during submission or post-approval phases.
India’s approach to regulating AI-based medical devices is evolving toward a structured, risk-based, and lifecycle-oriented model. While the framework continues to mature, its direction is increasingly clear: regulatory oversight will focus not only on initial compliance, but on the controlled evolution and sustained performance of AI systems over time.
For global manufacturers, this represents both a challenge and an opportunity. Success in this market will depend less on adapting at the point of submission, and more on designing regulatory strategies that anticipate change from the beginning.