Keeping pace with the ever-changing life sciences industry, Morrison Foerster stays on top of the trends and political, legal, and economic drivers to support striking the balance between innovative discovery and purposeful business planning to assure our clients continued success. MoFo Life Sciences shares timely legal and business insights and in-depth analyses on trends and the complex technologies at the heart of the global pharmaceutical, biotechnology, diagnostics, medical devices, digital health, and AgTech fields.Learn more about our <a href="https://www.mofo.com/capabilities/life-sciences-healthcare" target="_self">Life Sciences practice</a>.<h2 dir="ltr">ABOUT MORRISON FOERSTER</h2>Morrison Foerster is a leading global law firm that transforms complexity into advantage for its clients. Our clients include some of the largest financial institutions, banks, consulting and accounting firms, and Fortune 100, technology, and life sciences companies. Highlighting the firm’s commitment to client service, leadership in market-changing deals and impact litigation, and values-based culture, Morrison Foerster has been repeatedly recognized as one of the top 10 firms on The American Lawyer’s A-List. Year after year, the firm receives significant recognition from Chambers and The Legal 500 across their various guides, including Global, USA, Asia‑Pacific, Europe, UK, Latin America, and FinTech Legal. Our lawyers passionately care about delivering legal excellence while living our values. Morrison Foerster has a long-standing commitment to creating a culture that respects and celebrates differences, while providing an inclusive environment. The firm has achieved Mansfield Certification Plus since 2018 as a result of successfully reaching at least 30 percent women, communities of color, and LGBTQ+ lawyer representation in a notable number of current leadership roles and committees. The firm also has a long history of commitment to the community and society through providing pro bono legal services, including litigating for civil rights and civil liberties, improving public education and fostering the wellbeing of children, advocating for veterans, promoting international human rights, enforcing the right to asylum, and safeguarding the environment.

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Dr. Meghan Poon counsels clients on patent preparation and prosecution, portfolio strategy, and IP due diligence in the areas of biotechnology and biologics. She has significant experience in diverse technologies including antibodies, cell therapies, gene therapies, diagnostics, and gene-edited plants. She represents clients ranging from large international pharmaceutical companies to midsized companies, startups, and universities.Meghan has filed numerous Patent Term Extension applications to secure additional term for her clients’ patents, which cover FDA-approved products. In addition, Meghan has extensive experience addressing complex obviousness-double patenting and subject matter eligibility issues impacting her clients’ patent portfolios. Meghan also represents clients in both investor-side and target-side due diligence, in deals from seed funding to initial public offerings.Prior to law school, Meghan attended graduate school at the University of California, Los Angeles, where she received a Ph.D. in biological chemistry based on her work on protein-protein and protein-DNA interactions at a molecular level. Meghan is the author of several scientific publications and has presented her research at international conferences.Meghan received her J.D. from the University of Southern California Law School and spent a year as a visiting student at Santa Clara University, where she focused on intellectual property.

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Dr. Meghan Poon counsels clients on patent preparation and prosecution, portfolio strategy, and IP due diligence in the areas of biotechnology and biologics. She has significant experience in diverse technologies including antibodies, cell therapies, gene therapies, diagnostics, and gene-edited plants.

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Wolfgang advises clients on contentious and non-contentious matters involving their most valuable intellectual property assets in life sciences, digital, and high-tech industries.His subjects of focus include research and development collaborations, complex licensing agreements, and other technology transactions, as well as know-how protection strategies, and employee invention law. He also has substantial experience in litigating patents, trademarks, and other intellectual property assets, often in a multijurisdictional context. In addition, Wolfgang and his team frequently clear trademarks and ad campaigns and prosecute trademark applications.Wolfgang is co-editor of our <a href="https://lifesciences.mofo.com/" dir="ltr" target="_blank">MoFo Life Sciences Blog</a>.Wolfgang is a partner of Morrison Foerster based in our German office in Berlin.<h4 dir="ltr">Representative Transactional Matters</h4><ul dir="ltr"><li>U.S.-based BioTech fund on various research collaborations and other transactions with German start-ups or research institutions.</li><li>Negotiate multimillion-euro strategic partnership between two AgTech leaders concerning the development and commercialization of pest-resistant transgenic traits.</li><li>Craft global multi-million collaboration regarding AI-powered robotic MaaS.</li><li>UK-based digital health company on product roll-out of RPM applications in Germany; coordinate international expansion.</li><li>Advise numerous German start-ups on their U.S. expansion.</li><li>Draft diverse global patent and know-how license agreements in the areas of AgTech and life sciences—including gene licensing and licensing of genome editing technologies (e.g., CRISPR)—and medical devices; draft correlating supply and distribution agreements.</li><li>Structure IP rights in connection with joint ventures and other M&A transactions, including the carve-out of a patent-heavy semiconductor business comprising nine jurisdictions into a German–U.S. joint venture.</li><li dir="ltr">Advise on government-funded, multi-partner R&D projects and/or R&D projects with academic institutions, as well as on employee invention law.</li></ul><h4 dir="ltr">Representative Contentious IP Matters</h4><ul dir="ltr"><li>Lead counsel in an international dispute between software companies on rights and obligations following termination of a multi-million licensing and distribution arrangement; coordinate local counsel.</li><li>Defend Japanese chip manufacturer in high-profile SEP-litigation; advise on global settlement arrangement.</li><li>Represent leading internet retailer in patent infringement and concurrent invalidity proceedings in the context of a multijurisdictional dispute concerning touch-screen technology.</li><li>Represent diverse pharmaceutical and life sciences companies in national and cross-border patent infringement proceedings and other technology-related disputes, including preliminary injunctions as well as pre‑trial discovery and inspection proceedings, concerning, inter alia, drug-coating technologies, toner cartridges, medicinal gases, transgenic plants, and vaccines.</li><li dir="ltr">Conduct trademark, copyright, and false advertising litigation, including, most notably, a high-profile trademark litigation for a German bank, and a nine-country cross-border trademark litigation involving a famous pharmaceutical brand.</li></ul>

Dr. Wolfgang Schönig berät Mandanten zum Schutz des geistigen Eigentums sowohl in streitigen als auch nichtstreitigen Angelegenheiten, mit einem besonderen Fokus auf die IP-rechtliche Beratung von Unternehmen aus den Bereichen Life Sciences, Digitalwirtschaft und High-Tech.Zu seinen Beratungsschwerpunkten gehören Forschungs- und Entwicklungskooperationen, komplexe Lizenzvertragsgestaltungen und andereTechnologie-Transaktionen, sowie Know-how-Schutz-Strategien und das Arbeitnehmererfinderrecht. Er verfügt zudem über umfangreiche Prozesserfahrung zur Durchsetzung von Patenten, Marken- und anderen IP-Rechten, häufig auch in einem grenzüberschreitenden Kontext. Darüber hinaus bewertet Dr. Wolfgang Schönig zusammen mit seinem Team regelmäßig die Rechtskonformität von Werbekampagnen, prüft die Verfügbarkeit von Marken und entwickelt entsprechende Anmeldestrategien.Dr. Wolfgang Schönig ist Co-Editor des Life Sciences Blogs von Morrison Foerster.Er hat seinen Doktortitel an der Heinrich-Heine-Universität Düsseldorf und seinen LL.M. an der Fordham School of Law in New York erworben, jeweils mit Schwerpunkt im gewerblichen Rechtsschutz. Seinen MBA-Abschluss erhielt er von der European University in Barcelona.Dr. Wolfgang Schönig ist Partner des deutschen Büros von Morrison Foerster in Berlin.<h4 dir="ltr">Repräsentative Transaktionen</h4><ul><li dir="ltr">BioTech-Fonds mit Sitz in den USA bei verschiedenen Forschungskooperationen und anderen Transaktionen mit deutschen Start-ups oder Forschungseinrichtungen</li><li dir="ltr">Verhandlung einer hochvolumigen strategischen Partnerschaft zwischen zwei Technologieführern im Bereich Crop Science betreffend der Entwicklung und Kommerzialisierung von bestimmten Resistenzen</li><li dir="ltr">Gestaltung einer globalen Multi-Millionen-Zusammenarbeit für KI-gestützte Roboter</li><li dir="ltr">Ein in Großbritannien ansässiges Unternehmen für digitale Gesundheit bei der Markteinführung von RPM-Anwendungen in Deutschland; Koordination der internationalen Expansion</li><li dir="ltr">Beratung zahlreicher deutscher Start-ups bei deren US-Expansion</li><li dir="ltr">Strukturierung und Verhandlung diverser Patent- und Know-How-Lizenzen in den Bereichen Ag Tech und Life Sciences – einschließlich Fragen zu genome editing wie CRISPR-Medizingeräte, sowie Gestaltung von Softwarelizenzverträgen und Erstellung korrespondierender Liefer- und Vertriebsvereinbarungen</li><li dir="ltr">Strukturierung von IP-Rechten im Zusammenhang mit Joint Ventures und anderen M&A-Transaktionen, darunter die Koordination eines neun Länder umfassenden IP Carve-Outs im Rahmen eines Halbleiter-JVs</li><li dir="ltr">Beratung zu staatlich geförderten Multi-Partner F&E-Projekten und/oder F&E-Projekten mit akademischen Einrichtungen sowie zum Arbeitnehmererfindungsrecht</li></ul><h4 dir="ltr">Repräsentative IP-Streitigkeiten</h4><ul><li dir="ltr">Lead Counsel in einem internationalen Rechtsstreit zwischen Softwareunternehmen über Rechte und Pflichten nach Beendigung einer millionenschweren Lizenz- und Vertriebsvereinbarung; Koordinierung der lokalen Anwälte</li><li dir="ltr">Verteidigung eines japanischen Chipherstellers in einem prominenten Streitfall bezüglich Standardessentieller Patente (SEP); Beratung zu einer globalen Vergleichsregelung</li><li dir="ltr">Verteidigung einer führenden Internetplattform gegen Ansprüche eines Patenttrolls wegen vermeintlicher Verletzung von Patent bezüglich Touchscreen-Technologie sowie korrespondierende US-Streitigkeiten</li><li dir="ltr">Vertretung von Pharma- und Life Science-Unternehmen in nationalen und grenzüberschreitenden Patentverletzungsverfahren und anderen technologiebezogenen Streitigkeiten, einschließlich einstweiliger Verfügungsverfahren und vorprozessualer Beweissicherungs- und Besichtigungsverfahren, unter anderem in Bezug auf Arzneimittelbeschichtungstechnologien, Tonerkartuschen, medizinische Gase, transgene Pflanzen und Impfstoffe</li></ul>

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Wolfgang Schönig is a partner of Morrison Foerster (International) LLP based in our German office in Berlin. His practice specialty is advising on IP aspects relative to transactions in, among others, the life sciences, AgTech and pharmaceuticals industry, as well as legal representation in IP disputes. Wolfgang is specialized in research and development collaborations, complex licensing agreements, technology acquisitions, know-how protection strategies, and employee invention law.

Wolfgang Schönig focuses his practice on IP aspects relative to transactions in the digital, telecoms, high tech, life sciences, and pharmaceuticals industry, as well as legal representation in IP disputes.

Wolfgang Schönig advises clients on IP aspects relating to transactions in, among others, the life sciences, AgTech and pharmaceuticals industry, as well as legal representation in IP disputes. His additional focus areas include research and development collaborations, complex licensing agreements, technology acquisitions, know-how protection strategies, and employee invention law.

Wolfgang Schönig

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This post is part of MoFo’s 2025 Intersection of AI and Life Sciences blog series. In this blog series, we explore how artificial intelligence is revolutionizing research, innovation, and patient care in the life sciences. Stay tuned for expert insights regarding the impact of AI on intellectual property, licensing, contracts, regulatory policy, enforcement, privacy, and venture markets in life sciences.Transparency of artificial intelligence (AI) systems in healthcare is quickly becoming a central concern for regulators due to potential impacts on patient safety, healthcare quality, bias mitigation, and public trust. To address these challenges, the Office of the National Coordinator for Health Information Technology (ONC) introduced the <a href="https://www.federalregister.gov/documents/2024/01/09/2023-28857/health-data-technology-and-interoperability-certification-program-updates-algorithm-transparency-and" target="_blank">Health Data, Technology, and Interoperability: Certification Program Updates, Algorithm Transparency, and Information Sharing</a> (HTI-1) rule, establishing comprehensive transparency requirements for AI-based decision support intervention technology (DSI). Additionally, the Food and Drug Administration (FDA) has advanced transparency recommendations for AI models in medical product submissions, signaling a broader regulatory alignment toward robust AI accountability standards. This article reviews the impact of these regulatory developments on the use of AI in healthcare and compares the requirements to those applicable under the EU AI Act.Background: Health IT Certification ProgramThe 21st Century Cures Act of 2016 (“Cures Act”)[1] requires the ONC to administer the voluntary Health IT Certification Program (“Program”), which sets standards, specifications, and requirements for health information technology that is certified through the Program (“Certified Health IT”) to manage and exchange data. While the Program is voluntary, more than 96% of U.S. hospitals and 78% of U.S. office-based physicians use Certified Health IT,[2] in large part because the Centers for Medicare & Medicaid Services require the use of Certified Health IT through their Promoting Interoperability Programs.[3]The HTI-1 rule updates the Program’s standards and requirements for Certified Health IT. Most significantly, the rule expands the certification criteria for DSIs, including new transparency requirements for Health IT Modules.[4] Specifically, the rule imposes requirements on Evidence-based and Predictive DSIs:<ul><li>Evidence-based DSIs: These systems generate recommendations based on clinical guidelines or other expert consensus, meaning they reflect how the world should look.[5]</li><li>Predictive DSIs: These systems generate recommendations based on patterns in real world data.[6] Predictive DSIs include a wide range of computational methods, from simple risk calculators like the <a href="https://intensivecarenetwork.com/Calculators/Files/Apache4.html" target="_blank">Acute Physiology and Chronic Health Evaluation (APACHE) IV</a>, to deep neural networks, large language models, and other generative AI.</li></ul>Source Attribute RequirementsUnder the HTI-1 rule, developers of Certified Health IT must enable authorized users to access detailed “source attributes” associated with both Evidence-based and Predictive DSIs. These source attributes, listed in <a href="https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-D/part-170/subpart-C/section-170.315#p-170.315" target="_blank">45 CFR § 170.315(b)(11)(iv)</a>, provide transparency into how DSIs generate recommendations. Developers are only required to provide the information for the DSIs they supply, not for Predictive DSIs developed by their customers or third parties.Requirements for Evidence-based DSIsPreviously, the disclosure requirements for Evidence-based DSIs focused primarily on clinical research publications describing each DSI’s clinical value, release and revision dates, the individuals responsible for interpreting and translating clinical guidelines into computational forms, and their funding source.The HTI-1 rule expands these requirements, mandating that Certified Health IT also enable authorized users to review additional critical information, such as patient demographics, social determinants of health, and health status assessments.[7] With these expanded requirements, the ONC aims to advance transparency and explicitly address health equity throughout the DSI’s life cycle, from design to implementation.Requirements for Predictive DSIsThrough an expansion of source attributes, the HTI-1 rule requires Health IT Modules to disclose new sets of information that guide Predictive DSI recommendations. Users are now able to review information about a Predictive DSI’s design, development, training, and evaluation. While a full list of source attributes can be found in <a href="https://www.ecfr.gov/current/title-45/subtitle-A/subchapter-D/part-170/subpart-C/section-170.315" target="_blank">45 CFR 170.315(b)(11)(iv)(B)</a>, the overview below highlights some of the new information fields that limited sets of users must have access to.<ul><li>Design:<ul><li>Basic information about the DSI, such as who the DSI developer is and their funding source, the technology’s intended use and patient population, any known risks, limitations, and inappropriate uses.</li></ul></li><li>Training and Development:<ul><li>Exclusion and inclusion criteria that influenced the datasets used to train the DSI.</li><li>Descriptions of the training datasets and demographic representativeness.</li><li>Approaches taken to ensure that the DSI’s output is fair and to manage, reduce, or eliminate bias.</li></ul></li><li>Evaluation:<ul><li>External validation process: Descriptions of any external validation process and the data source, clinical setting, or environment where the DSI’s validity and fairness was assessed.</li><li>Quantitative measures of performance: A DSI’s validity and fairness are shown both in test data derived from the same source as the initial training data and other data. The Health IT Module must also make reference to evaluations that describe the DSI’s clinical value.</li><li>Updates and ongoing monitoring: A description of how and how often (1) updates occur, and (2) validity and fairness are monitored over time. A limited set of users must also have access to a description of the frequency by which the DSI’s performance is corrected when risks are identified.</li></ul></li></ul>Risk management practices for Predictive DSIUnder the HTI-1 rule, for each Predictive DSI that is supplied by the developer as part of its Certified Health IT Module, the developer must apply and document risk management practices related to eight characteristics: validity, reliability, robustness, fairness, intelligibility, safety, security, and privacy. This includes conducting a risk analysis of these topics, as well as applying risk mitigation practices. Additionally, developers of Certified Health IT must institute policies and controls for governance, including those governing how data is acquired, managed, and used. Developers must provide a summary of this information to ONC-Authorized Certification Bodies, which then release this information publicly.By adding both the risk management requirement and the expanded list of Predictive DSI source attributes, the ONC aims to increase public trust of predictive algorithms used in healthcare, better inform the larger discussion of how to evaluate the performance of Predictive DSIs and enable users to determine whether a Predictive DSI is “fair, appropriate, valid, effective, and safe.”[8]Transparent Reporting of How Health IT Is UsedAs required by the Cures Act,[9] the HTI-1 rule establishes Conditions and Maintenance of Certification requirements (the “Insights Condition”) that will provide insights into the use of specific Certified Health IT functionalities, as well as allow the ONC to better understand end users’ experience with Certified Health IT. Under the Insights Condition, health IT developers who participate in the Program and meet the threshold requirements (including a minimum number of hospital sites or clinician users) must, starting in 2026, collect data on one or more of seven measures, such as the number of individuals who access their electronic health information (EHI). Beginning in 2027, developers must then submit reports on such data.Transparency Beyond HTI-1: AI Used in Drug Submissions and Medical DevicesAI Models Used to Support Drug Submissions and Post-marketing RequirementsIn response to the increased use of AI in regulatory drug submissions, FDA recently published a <a href="https://www.fda.gov/media/184830/download" target="_blank">draft guidance document</a> that recommends how drug sponsors and other interested parties should evaluate the credibility of AI models that produce data or information intended to support either: (1) FDA regulatory determinations regarding a drug’s safety, effectiveness, or quality, or (2) conformance with FDA’s regulatory authority over a drug’s safety, effectiveness, or quality, such as post-marketing requirements.[10] The draft guidance strongly encourages sponsors to engage early with FDA to discuss the AI model risk and appropriate credibility assessment activities. The guidance also provides recommendations for developing, executing, documenting, and assessing a credibility assessment plan, which may be submitted to FDA, depending on the AI model, its context of use, and how the sponsor engages with the agency.User-Centered Approach to AI-enabled Medical DevicesFDA also recently released a <a href="https://www.fda.gov/media/184856/download" target="_blank">draft guidance document</a> that contains recommendations for developing a transparent AI-enabled medical device that is centered around users.[11] Reflecting the <a href="https://www.fda.gov/media/179269/download?attachment" target="_blank">2024 Transparency for Machine Learning-Enabled Medical Devices Guiding Principles</a>, the draft instructs sponsors to consider transparency throughout a device’s life cycle, from design to decommission. According to the draft, sponsors should use a holistic approach to transparency, which involves ensuring that a device’s intended users receive and understand key information. For example, FDA encourages sponsors to provide timely and contextually relevant information through the user interface, such as through device alerts. The draft identifies relevant considerations, including user needs and limitations and appropriate communication styles and formats, as well as examples of how to communicate information about a device’s AI model and the limitations of that device.The EU AI Act and Healthcare TransparencyThe <a href="https://eur-lex.europa.eu/eli/reg/2024/1689/oj/eng" target="_blank">EU Artificial Intelligence Act</a> (“EU AI Act”) establishes comprehensive, binding standards to ensure safety, transparency, accountability, and fairness in AI systems, particularly those classified as “high risk.” Under the EU AI Act, AI systems used in healthcare typically qualify as high risk when they involve diagnosis, prevention, monitoring, prediction, prognosis, or treatment, and are thus subject to the <a href="https://eur-lex.europa.eu/eli/reg/2017/745/oj/eng" target="_blank">EU Medical Devices Regulation</a>.For such high-risk AI systems, the EU AI Act imposes a broad set of risk-management requirements, including the requirement to maintain detailed documentation and a clear disclosure of the AI system’s capabilities, limitations, development, training, and validation processes implemented by the high-risk AI system provider for the deployer thereof. A key aspect of the risk management obligations for high-risk AI systems is human oversight. High-risk AI systems must be designed so healthcare professionals can understand, interpret, and potentially override AI-generated recommendations, ensuring transparency and reducing risks associated with bias or errors. Additionally, the EU AI Act mandates ongoing post-market monitoring, requiring providers to promptly report significant incidents or malfunctions. This proactive transparency aligns with similar objectives outlined in FDA’s guidelines on AI model performance monitoring.The EU AI Act also includes transparency requirements for specific non-high-risk AI systems. These obligations ensure that individuals are informed when interacting with AI, even when the systems are not classified as high risk. Providers must disclose clearly that users are interacting with AI-generated content or outputs, thereby maintaining transparency across all AI applications, including those that indirectly impact healthcare contexts such as administrative or informational tools.Compared to the U.S. regulatory framework (HTI-1 rule and FDA guidelines), the EU AI Act imposes legally binding obligations with stronger enforcement mechanisms and significant penalties for non-compliance, positioning itself as a more rigorous international standard for AI transparency in healthcare.ConclusionThe HTI-1 rule has established a regulatory baseline for AI transparency standards in the U.S. healthcare system, complemented by FDA guidelines emphasizing clarity and user-centered approaches. The EU AI Act, however, sets a more stringent international benchmark through binding transparency obligations, mandatory third-party assessments, and continuous oversight.These frameworks collectively underscore transparency as essential to ensuring safety, fairness, effectiveness, and public trust in AI-driven healthcare technologies. Stakeholders must carefully navigate these regulatory landscapes, aligning practices proactively with both evolving U.S. standards and the rigorous requirements of the EU AI Act. Ongoing monitoring of regulatory developments will remain crucial as healthcare and AI technologies continue to evolve. [1] 21st Century Cures Act, Pub. L. No. 114-255, § 13103, 130 Stat. 1033 (2016); 42 U.S.C. 300jj–11(c)(5)).[2] HealthIT.gov, HTI-1 Final Rule, <a href="https://www.healthit.gov/topic/laws-regulation-and-policy/health-data-technology-and-interoperability-certification-program" target="_blank">https://www.healthit.gov/topic/laws-regulation-and-policy/health-data-technology-and-interoperability-certification-program</a> (last visited Dec. 26, 2024).[3] ONC, ONC Health IT Certification Program: Overview (last visited Dec. 26, 2024), <a href="https://www.healthit.gov/sites/default/files/PUBLICHealthITCertificationProgramOverview.pdf" target="_blank">https://www.healthit.gov/sites/default/files/PUBLICHealthITCertificationProgramOverview.pdf</a>.[4] A Health IT Module is “any service, component, or combination thereof that can meet the requirements of at least one certification criterion adopted by the Secretary.” HealthIT.gov, Frequently Asked Questions, <a href="https://www.healthit.gov/faq/what-health-it-module" target="_blank">https://www.healthit.gov/faq/what-health-it-module</a> (last visited Dec. 26, 2024).[5] HealthIT.gov, Two Sides of the AI/ML Coin in Health Care, <a href="https://www.healthit.gov/buzz-blog/blog-series-artificial-intelligence-machine-learning/ai-ml-in-health-care" target="_blank">https://www.healthit.gov/buzz-blog/blog-series-artificial-intelligence-machine-learning/ai-ml-in-health-care</a> (last visited Dec. 26, 2024).[6] Id. Predictive DSIs “support decision-making based on algorithms or models that derive relationships from training data and then produce an output that results in prediction, classification, recommendation, evaluation, or analysis.” 89 Fed. Reg. 1192, 1426 (Jan. 9, 2024).[7] Pursuant to President Trump’s Executive Order titled “Defending Women from Gender Ideology Extremism and Restoring Biological Truth to the Federal Government,” issued on January 20, 2025, ONC announced on March 21, 2025, that the agency will not take any enforcement action against developers of Certified Health IT for not complying with HTI-1 requirements surrounding sexual orientation and gender identity, including patient demographics requirements. HealthIT.gov, Enforcement Discretion and Certification Guidance, <a href="https://www.healthit.gov/topic/certification-ehrs/enforcement-discretion" target="_blank">https://www.healthit.gov/topic/certification-ehrs/enforcement-discretion</a> (last visited Apr. 15, 2025).[8] 89 Fed. Reg. 1192, 1194-1195 (Jan. 9, 2024).[9] 21st Century Cures Act, Pub. L. No. 114-255, § 4002, 130 Stat. 1033 (2016).[10] For more details, see our <a href="https://lifesciences.mofo.com/topics/fda-releases-draft-guidance-on-evaluating-the-risk-and-credibility-of-ai-used-in-establishing-drug-and-device-safety-effectiveness-and-quality">recent blog post</a> covering this draft guidance document.[11] For more details, see our <a href="https://lifesciences.mofo.com/topics/fda-draft-guidance-on-artificial-intelligence-enabled-device-software-functions">recent blog post</a> covering this draft guidance do

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Marijn Storm specializes on the intersection of law and technology, with a focus on cutting-edge technologies, such as (generative) artificial intelligence (AI) and large language models, ethical tech, and biometrics. 

Marijn Storm

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Brigid is a food, drug, and medical device lawyer who counsels life sciences companies on a wide range of U.S. Food & Drug Administration pre- and post-market regulatory issues.

Brigid DeCoursey Bondoc

Brigid is a food, drug, and medical device lawyer who counsels life sciences companies on a wide range of U.S. Food & Drug Administration pre- and post-market regulatory issues.

Brigid Bondoc

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Associate

Elena Catherine Klonoski

Elena Klonoski

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On March 10, the U.S. Department of Health and Human Services (HHS) issued a <a href="https://www.hhs.gov/about/news/2025/03/10/hhs-secretary-kennedy-directs-fda-explore-rulemaking-eliminate-pathway-companies-self-affirm-food-ingredients-safe.html" target="_blank">press release</a> emphasizing “radical transparency to make sure all Americans know what is in their food” and announcing that HHS Secretary Robert F. Kennedy Jr. directed the U.S. Food and Drug Administration (FDA) to explore rulemaking to close what Kennedy calls a “loophole” regarding disclosures for food ingredients. Specifically, Secretary Kennedy’s statements targeted the “self-affirmation” marketing pathway in the Substances Generally Recognized as Safe (GRAS) Final Rule that allows manufacturers to introduce new food additives without any premarket submission to FDA.Prior to 1997, companies could petition FDA to review GRAS status of substances, but such a review was a resource-intensive process for FDA. <a href="https://www.fda.gov/food/generally-recognized-safe-gras/how-us-fdas-gras-notification-program-works" target="_blank">In 1997</a>, <a href="https://www.govinfo.gov/content/pkg/FR-1997-04-17/pdf/97-9706.pdf" target="_blank">FDA proposed a rule</a> outlining a GRAS notification program that allows companies to inform FDA of the companies’ determination that a food additive is GRAS, a less resource-intensive process for FDA. In 2016, the self-affirmation marketing pathway was codified at <a href="https://www.ecfr.gov/current/title-21/chapter-I/subchapter-B/part-170/subpart-B/section-170.35" target="_blank">21 C.F.R. § 170.35</a>. If FDA follows Secretary Kennedy’s directive by eliminating GRAS’s self-affirmation pathway, companies would be required to publicly notify FDA of new ingredients and the new ingredient’s underlying safety data, before the new ingredients are introduced in the food supply.Acting FDA Commissioner Sara Brenner agreed with Secretary Kennedy, stating that “the FDA is committed to further safeguarding the food supply by ensuring the appropriate review of ingredients.” FDA has recently undergone a significant reorganization of its Human Foods Program, and the administration’s FY2025 budget calls for <a href="https://www.fda.gov/media/176923/download#:~:text=The%20FY%202025%20Budget%20provides,$3.5%20billion%20in%20user%20fees." target="_blank">additional resources</a> “to strengthen and modernize FDA’s capacity to protect and promote a safe, nutritious U.S. food supply,” despite a net-zero change in the overall funding level for the agency. Whether FDA will have sufficient resources to revise the rule and ultimately review many more GRAS notifications remains to be seen.MoFo will monitor FDA’s treatment of GRAS.

HHS Secretary to FDA: Consider Elimination of GRAS Self-affirmation Pathway

Nature's Path Foods

Suja Juices

Unilever USA Inc.

Nature’s Path Foods and Sprouts Farmers Market

Iovate Health Sciences, International

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Claudia focuses her practice on the defense of consumer class actions and on complex commercial litigation, including unfair business competition, false advertising, product liability, and privacy mat

Claudia Vetesi

Claudia focuses her practice on the defense of consumer class actions and on complex commercial litigation, including unfair business competition, false advertising, product liability, and privacy matters. She is currently representing numerous consumer goods manufacturers and retailers in false advertising and mislabeling class actions.

Claudia M. Vetesi

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Alex M. Schroth

Alex Schroth

HHS Secretary to FDA: Consider Elimination of GRAS Self-affirmation Pathway

This post is part of MoFo’s 2025 Intersection of AI and Life Sciences blog series. In this blog series, we explore how artificial intelligence is revolutionizing research, innovation, and patient care in the life sciences. Stay tuned for expert insights regarding the impact of AI on intellectual property, licensing, contracts, regulatory policy, enforcement, privacy, and venture markets in life sciences.On January 6, 2025, FDA released a draft guidance entitled <a href="https://www.fda.gov/media/184856/download" target="_blank">Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations</a>. In accordance with FDA’s total product life cycle (TPLC) approach to the oversight of medical devices, the draft guidance outlines marketing submission content recommendations for devices that include at least one artificial intelligence (AI)-enabled device software function, as well as recommendations for the design, development, deployment, and maintenance of such devices. The guidance also provides a recommended approach to transparency and bias for these devices, which FDA encourages developers to incorporate from device design through decommission.The guidance encourages early engagement with FDA, especially when the device involves new and emerging technology or when device validation involves novel methods.Comments and suggestions regarding the draft guidance must be submitted by April 7, 2025. If you would like assistance in making a comment or suggestion, please reach out to the FDA + Healthcare Regulatory and Compliance team at Morrison Foerster.Information That Sponsors Should Include in a Submission for an AI-Enabled Device[1]Device DescriptionThe device description should provide information that helps FDA understand the characteristics of the AI-enabled devices, such as device inputs and device outputs, how AI is used to achieve the device’s intended use, the level and type of training that intended users have and/or will receive, and any calibration and/or configuration procedures that must be regularly performed by users. Additionally, sponsors should include a description of all configurable elements (i.e., alert thresholds), as well as whether the users who make configuration decisions require any qualifications or training.User Interface InformationSponsors should include information about the user interface and device workflow, including how information is presented to users. This can be conveyed through different methods, such as photographs, a written description, example reports, and recorded video.Labeling InformationAn AI-enabled device’s labeling should explain many pieces of information to the user at an appropriate reading level, such as how AI is used to achieve a device’s intended use, what the model output means, the degree of automation that the device exhibits, and installation and implementation instructions. The labeling should also describe the model architecture, model development data (including demographic distributions), performance data (including primary endpoints of the validation study), device performance metrics across important subgroups, performance monitoring, and known limitations.Risk Management FileSponsors should provide, as part of a “Risk Management File,” a risk management plan, including a risk assessment.Training and Testing Data InformationSponsors should include in a marketing submission information on data collection, development and test data independence, reference standards, and representativeness. The draft provides a template for organizing this information that lists specific descriptions and information that should be provided. If there are any differences in the data management between the development phase and the validation phase, the sponsor should explain the differences and justify them.Model Description and DevelopmentSponsors should include descriptions of each model used as part of AI-enabled devices, including any quality control criteria. Additionally, sponsors should include a description of how models were trained and an explanation of any pre-trained models that were used.Validation TestingSponsors should include validation testing that provides objective information to characterize the model’s performance. The draft discusses both performance validation and human factors validation (or, alternatively, an evaluation of usability; see Appendix D). Sponsors should include information regarding all study protocols, including study design and analysis details, as well as study results.Performance Monitoring PlansIf a sponsor elects to employ proactive performance monitoring as a means of risk control and to provide safety and effectiveness assurances, the sponsor should include information regarding their performance monitoring plans.CybersecuritySponsors should include cybersecurity controls and security risk management relevant to the AI components or features.Public Submission SummaryFor most marketing authorization decisions, sponsors are required to provide public submission summaries. To support transparency, sponsors of AI-enabled devices should include in this summary specific information about device characteristics, such as descriptions of the statistical confidence level of predictions and descriptions of the development and validation datasets. Sponsors are encouraged to use model cards to organize this information (see Appendix E).TransparencyAppendix B contains recommendations for developing a transparent device around users, starting at the design phase of the TPLC. Reflecting the <a href="https://www.fda.gov/media/179269/download?attachment" target="_blank">2024 Joint Guiding Principles</a>, the draft guidance instructs sponsors to take a holistic approach to transparency, emphasizing that transparency should be considered during device design and throughout the full continuum of implementation. The draft includes examples of relevant considerations. Appendices E and F include examples of how to communicate information about a device’s AI model and the limitations of that device, based on FDA’s “user-centered research.” Performance Validation ConsiderationsAppendix C lists recommendations for clinical performance validation, such as pre-specifying study protocols and statistical analysis plans, as well as providing examples of when to conduct a precision study. This section also calls attention to scenarios leading to potential bias.<hr class="divider">[1] This section highlights examples of information that sponsors should include in a submission for an AI-Enabled Device. For a complete list, see U.S. Food & Drug Admin., Artificial Intelligence-Enabled Device Software Functions: Lifecycle Management and Marketing Submission Recommendations: Draft Guidance for Industry and Food and Drug Administration Staff (2025).