AI Clinical Decision Support Software: The New Regulatory Reality for Digital Health and Life Sciences, Pt 2

By Darya Lucas

Addressing the Challenges of AI-Enabled Clinical Decision Support Software

In Part One of this series, we examined the evolving regulatory landscape for AI-enabled clinical decision support (CDS) software and the challenges organizations face as products become more sophisticated. Once companies recognize these challenges, the next step is developing strategies that support innovation while meeting regulatory expectations and commercial objectives.

Successfully bringing AI-enabled healthcare solutions to market requires more than technical innovation. It demands thoughtful planning across the entire product lifecycle.

AI Requires a Lifecycle Approach

For AI-enabled Software as a Medical Device (SaMD), model performance is only one part of the regulatory and commercial equation. The FDA’s Artificial Intelligence and Machine Learning (AI/ML) resources recognize that AI/ML-enabled medical devices raise unique considerations related to performance, transparency, modifications, and lifecycle management.

Unlike many traditional healthcare products, AI-enabled software may continue to evolve after launch through software updates, new data inputs, or model refinements. Stakeholders increasingly expect companies to demonstrate how models are developed, validated, monitored, and updated over time.

Issues such as transparency, bias, cybersecurity, human factors, and change management are becoming central to both FDA review and market adoption. Together with international regulatory partners, the FDA has also emphasized responsible AI development practices through its Good Machine Learning Practice (GMLP) Principles for Medical Device Development.

Companies that establish governance processes early are better positioned to manage these changes while maintaining regulatory and customer confidence. For products regulated as medical devices, these considerations also intersect with the FDA’s Quality Management System Regulation (21 CFR Part 820), which establishes requirements for manufacturers’ quality systems and processes supporting product safety and compliance.

Clinical Evidence Should Be a Business Strategy

Clinical evidence is not simply a regulatory requirement; it is also a critical driver of adoption. For AI-enabled healthcare software, the appropriate evidence strategy depends on the software’s intended use, clinical risk, regulatory pathway, and commercial objectives. A workflow support tool may require a different approach from the approach used for software that provides patient-specific recommendations used to inform diagnosis or treatment decisions.

Companies often face difficult decisions regarding the appropriate level of evidence, when evidence should be generated, and how that evidence should support both regulatory and commercial goals. Those decisions are best addressed early in development when companies have the greatest flexibility. A well-designed evidence strategy can support regulatory objectives while also building confidence among healthcare providers, health systems, investors, and strategic partners.

Real-World Data Is Reshaping AI Development

AI-enabled software depends on data throughout its lifecycle. Real-world data (RWD) may support algorithm development, external validation, post-market monitoring, and future product enhancements. Real-world evidence (RWE) may also help demonstrate how software performs across different clinical settings and patient populations.

The FDA’s Real-World Evidence Program recognizes the potential role of real-world data in supporting regulatory decision-making while emphasizing the importance of data quality, relevance, and fitness for purpose. The value of RWD, however, depends on careful evaluation. Companies must consider factors such as data provenance, representativeness, completeness, and governance before relying on real-world evidence to support regulatory or commercial objectives.

For organizations developing AI-enabled healthcare solutions, including digital health platforms, health data companies, and clinical technology providers, data and regulatory strategies are increasingly connected.

Governance Has Become a Market Differentiator

Healthcare organizations evaluating AI software are asking more sophisticated questions. Beyond regulatory status, customers want to understand how algorithms are developed, how performance is monitored, how updates are managed, and how risks such as bias and cybersecurity are addressed.

These questions extend beyond traditional medical device companies. Healthcare technology platforms, clinical research organizations, pharmaceutical companies, biotechnology companies, and healthcare data companies are increasingly evaluating similar issues as they incorporate AI into products and services.

Companies that integrate regulatory, clinical, quality, engineering, cybersecurity, and legal perspectives early in development are often better prepared to address these expectations. While remaining essential to compliance, strong governance is now a competitive advantage.

Questions Digital Health and Life Sciences Leaders Should Be Asking

As AI-enabled CDS continues to evolve, digital health and life sciences leaders should consider:

• Has the product changed in a way that affects FDA jurisdiction or regulatory classification?
• Does the software remain within the scope of non-device CDS, or should it be evaluated as SaMD?
• Could future features, intended uses, or claims change the regulatory analysis?
• Does the clinical evidence strategy support both regulatory objectives and commercial adoption?
• Can real-world data appropriately support clinical validation, post-market monitoring, and ongoing lifecycle management?
• Are governance processes sufficient to manage AI performance and software changes over time?

How companies answer these questions increasingly influences product strategy, investment decisions, partnerships, and long-term market positioning.

Looking Ahead

AI is transforming clinical decision support, but successful products will require more than sophisticated algorithms. Companies that integrate regulatory strategy, clinical evidence, data governance, and lifecycle planning from the beginning will be better positioned to bring innovative technologies to market.

Darya Lucas, Associate Attorney

As the FDA’s approach to AI-enabled healthcare software and digital health technologies continues to develop, thoughtful planning around classification, evidence generation, and ongoing oversight will become increasingly important for digital health, healthcare technology, and life sciences companies seeking to compete effectively.

How Gardner Law Can Help

Gardner Law advises medical device, digital health, healthcare technology, and life sciences companies on regulatory strategies involving clinical decision support software, AI-enabled Software as a Medical Device (SaMD), AI/ML technologies, and digital health innovation. We help clients evaluate FDA jurisdiction, assess whether software functions may qualify for statutory exclusions or FDA enforcement discretion policies, develop clinical evidence strategies, address real-world data considerations, and align regulatory strategy with commercialization objectives throughout the product lifecycle.