Healthcare AI: Navigating the Regulatory Challenges

Healthcare AI: Navigating the Regulatory Challenges

Failing fast is often used as a mantra in technology startups.  It embraces the entrepreneurial drive to develop a solution to a problem quickly, validate the solution under realistic scenarios, and leverage the feedback to update and repeat the process.  The core idea is that with each iteration the solution moves closer to solving the problem as deficiencies are gradually removed.  Companies that offer Software as a Service (SaaS) embrace this model since decisions to add new features are driven by hard metrics of customer acquisition and retention rates.  Commercial implementations of Artificial Intelligence (AI) were likewise rolled out against this backdrop.  A prime example is the Netflix recommendation engine that uses prior customer data to customize a “best” list of movies related to a current movie selection.  The company employed customers’ responses to sophisticated A/B testing to adapt the algorithms that personalize recommendations.  Data considerations for AI development for medical device manufacturers were detailed in my previous blog, Healthcare AI: Does the data tell a compelling story?  In this current blog, the regulatory pathway for AI medical devices will be discussed.

Failing fast is not a concept that resonates well within regulated environments, especially healthcare.  A medical error can have significant ramifications for the health and well-being of a patient.  Manufacturers of medical devices have systematic design processes built on thorough failure mode risk analysis, limiting both the number and negative effects of failures.  In the early 1990s, it could be argued that medical device regulation was rigorously hardware-centric, burdensome from a documentation standpoint, and fairly risk-averse.  For example, it was not until the De Novo program was established under the Food and Drug Administration Modernization Act (1997) that devices with low-to-moderate risk could be introduced as Class I or II instead of automatically starting as Class III.  Shortly thereafter, the FDA released a series of guidance documents addressing direct regulation of software in medical devices, including Off-the-shelf Software (1999), General Principles of Software Validation (2002), and Premarket Submission for Software (2005).  These documents tackled important topics such as leveraging industry software platforms and tools, planning for the entire software life cycle, and managing patient risks associated with the hardware device.

AI and Software as a Medical Device (SaMD) Risk Framework

In 2012, the FDA shifted to a global software regulatory approach by joining a consortium of medical device regulators, the International Medical Device Regulators Forum (IMDRF).  The IMDRF introduced a new paradigm: Software as a Medical Device (SaMD), defined as “software intended to be used for one or more medical purposes that perform these purposes without being part of a hardware medical device.”  This regulatory framework broadly encompasses software (including AI) that runs on general-purpose hardware and provides information directly related to clinical management.  It is particularly important for computationally intensive AI algorithms that leverage hardware agnostic cloud platforms.  The IMDRF formed a working group that developed a series of guidance documents supporting innovation and timely access to effective SaMD.  One key FDA guidance is the SaMD Risk Categorization Framework consisting of four levels (I, II, III, IV) in increasing order of patient risk.  According to Table 1, the risk categories are determined by a combination of a patient’s health condition (critical, serious, non-serious) and the impact of SaMD information on healthcare decision-making (treat or diagnose, drive clinical management, or inform clinical management).  Naturally, the necessary systematic engineering controls for risk and quality management become more rigorous with the higher classification of risk level.

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