Beyond Patents – FDA Regulatory Approval of Medical Devices and the Software Precertification Program
Artificial Intelligence (AI) and Machine Learning (ML) are poised to revolutionize the field of healthcare. For example, researchers are leveraging deep learning methods to find new ways to efficiently diagnose and treat diseases. Although lacking a well-articulated AI strategy, the United States invested an estimated $2 billion on research and development for AI-based technologies in 2017. Since that time, the Department of Defense has also committed to providing up to an additional $2 billion per year in spending for AI technology and infrastructure over the next five years.
In line with such increased investments, there has been substantial growth in AI-based medical device patent applications over the last decade. A Juristat review of classes related to surgery, x-ray systems, and prosthetics under the United States Patent Classification system returned over 1800 published AI-related medical device patent applications since 2000, as well as a sharp and monotonic increase in such filings since 2010. However, many AI and ML-based software as a medical device (SaMD) applications commonly receive claim rejections under 35 U.S.C. §§ 101 and 112. While the USPTO has recently released updated guidance on patentable subject matter, it is not yet clear whether such guidance has actually mitigated issues relating to the patentability of inventions involving abstract ideas.
Furthermore, bringing a patented product to market is often delayed because of the hurdles involved in the Food and Drug Administration (FDA) regulatory approval process. In recognition of this inefficiency, retiring FDA Commissioner Scott Gottlieb recently led the establishment of the Software Precertification Program, which is intended to streamline the FDA approval process for AI and ML-based medical technologies.
FDA Regulatory Oversight of Medical Devices
The conventional FDA approval process for marketing new medical devices is an arduous and conservative pathway, driven by policies and procedures that are intended for hardware-based medical devices. The approval process can be broken down into the following steps:
- Device Discovery and Proof of Concept.
- Preclinical Research that includes building a prototype of the medical device to assess risk and safety.
- FDA Device Classification (Class I-III) based on the level of control necessary to ensure safety of the device. The FDA-required regulatory controls increase with increased class number.
- FDA Device Review based on safety and effectiveness of the device. Upon approval the device is cleared for public use.
- FDA Post-Market Device Safety Monitoring to ensure device safety and effectiveness.
Overall, the entire FDA medical device approval process, shown in Figure 1 below, takes an average of 3 to 7 years. FDA Device Review can take anywhere from 3 to 12 months or longer, dependent upon the medical device category and data supporting safety and effectiveness. The process is also expensive, with typical costs to bring a device for FDA review (Steps 1-3) ranging between $10 and $20 million.
This FDA review process is intended for all medical devices requiring FDA approval, including those involving AI and ML technologies. Thus, FDA oversight of AI and ML is far-reaching and even applies to SaMD. Such devices include most software and mobile apps intended to treat, diagnose, cure, mitigate, or prevent disease or other conditions as medical devices under the Federal Food, Drug, and Cosmetic Act. However, this rigid framework is ill-equipped to deal with AI-based software technology that changes in near real-time based on responses to real-world performance. AI creates a further unique problem under the current regulatory scheme because there is often lack of a tangible device. Instead, the FDA regulatory framework requires the evaluation of software code to assess the accuracy, reliability, and safety of AI-based healthcare. However, such code typically does not directly address the specific FDA metrics (e.g., safety, efficacy) required for FDA approval. Furthermore, a significant advantage of AI and ML medical devices is that they are frequently updated, in some cases in near-real-time, based on real-world data. However, the current FDA approval process is designed for devices that may be updated quarterly, annually, or even less frequently.
Software Precertification Program
The Software Precertification Program (Program) is a voluntary pathway that embodies a regulatory model tailored to assess the safety and effectiveness of AI-based software technologies without inhibiting patent access to the technologies. The foundation of the Program is identification of medical device manufacturers that have demonstrated a robust culture of quality and organizational excellence and are committed to monitoring real-world performance of their AI-based technologies.
The Program launched in 2017 as part of the Digital Health Innovation Action Plan, and was limited to FDA-regulated 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. Instead of the traditional FDA approval process of focusing on the eventual product, the Program focuses on the software or digital health technology developer. The developer/company-first approach should, in theory, remove traditional regulatory hurdles and permit trusted companies to harness the advantages of AI to quickly and effectively address safety concerns and respond to adverse events when they arise.
In response to a notice seeking voluntary participation, over one hundred companies applied to be included in the Program. The FDA used selective metrics such as product quality, patient safety, clinical responsibility, clinical responsibility, cybersecurity responsibility, and proactive culture to identify companies that exhibited organizational excellence. Apple, Fitbit, Johnson & Johnson, Pear Therapeutics, Phosphorus, Roche, Samsung, Tidepool, and Verily were selected as the initial nine trusted SaMD manufacturers to participate in the Program. Voluntary participation required the companies “to provide access to measures they currently use to develop, test, and maintain their software products,” including methods for post-market data collection, and allowance for FDA site visits.
Earlier this year, Director Gottlieb announced the release of the three key initiatives that outline the next phase of the Program. First, the FDA released guidance intended to explain the framework for the Program under the FDA’s current regulatory authorities. Specifically, the Program will be implemented under the De Novo pathway, traditionally used for approval of lower risk medical devices. The Program is in its infancy, but its goal is to “determine the contours of a possible regulatory model that provides efficient regulatory oversight of certain software-based medical devices from manufacturers who have demonstrated a robust culture of quality and organizational excellence (CQOE) and are committed to monitoring real-world performance while assuring that these devices are safe and effective.”
Second, the FDA released a Pre-Cert Test Plan for 2019 that outlines testing related to refinement and implementation of the Program. The goal of the Pre-Cert Test Plan is to determine how the Program can ensure safe and effective products. The FDA will compare parallel submissions made to the De Novo route and to the traditional route in an effort to determine whether the Program might provide efficiencies over the conventional FDA medical device approval process.
Third, the FDA launched an updated working model that incorporates the Regulatory Framework and Test Plan. The working model describes the proposed implementation approach and future vision for the Program.
The Program appears to be a concerted effort to streamline the regulatory process for software-based medical devices. However, many questions remain. For instance, it is not immediately clear where the bar will be set for companies to achieve the necessary CQOE required to participate in the Program. The initial slate of companies includes well-known global businesses with substantial product portfolios and a wealth of data to address the criteria for inclusion. Thus, it remains an open question whether, and how, startups and other less established companies might become certified. In addition, it is unclear whether the FDA will have authority to force a recall on companies and/or their products in the Program. Also, the FDA will need to consider situations where companies should have implemented a recall, but failed to do so. Furthermore, the protection of private information will be an important concern for AI and ML innovations that utilize large datasets that involve numerous patients and their sensitive personal information. Such privacy issues will likely represent substantial hurdles that the FDA must address moving forward.
Importantly, the U.S. currently lacks a well-defined AI strategy to address the rapid rise of Big Data and its impact on health technologies. As a result, full adoption of the Software Precertification Program appears to be years off, with only traditional FDA approval methods currently available for AI-based health technologies. Other FDA AI-based qualification programs, such as the Medical Device Development Tools (MDDT) Program, remain in the nascent stages and have not been widely utilized by AI and ML developers. Thus, today’s AI-based medical technologies continue to be subjected to lengthy regulatory timelines and risk being outdated by the time they are approved for commercialization.
Accordingly SaMD manufacturers currently face risks to their product pipeline at the USPTO as well as at the FDA. In the patent realm, despite potential pitfalls related to rejections under 35 U.S.C. §§ 101 and 112, experienced practitioners have developed effective strategies for patenting AI and ML-based technologies. On the regulatory side, innovative AI-based medical device manufacturers will likely leverage the Software Precertification Program, or other similar FDA pilot pathways, to reduce delays to marketing approval.
© 2019 McDonnell Boehnen Hulbert & Berghoff LLP
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 See, e.g., Lily Peng & Varun Gulshan, Deep Learning for Detection of Diabetic Eye Disease, Google AI Blog (Nov. 29, 2016), https://ai.googleblog.com/2016/11/deep-learning-for-detection-of-diabetic.html; Thomas M. Maddox et al., Questions for Artificial Intelligence in Health Care, 321 JAMA Surg. 31, 31 (2019).
 AI Policy – United States, Futureoflife.org, https://futureoflife.org/ai-policy-united-states/ (last visited Mar. 8, 2019).
 See Michael Borella, USPTO on Patent Eligibility — Examples 38 & 39, Patent Docs (Jan. 15, 2019), https://www.patentdocs.org/2019/01/uspto-on-patent-eligibility-examples-38-39.html.
 FDA, The Device Development Process, FDA.Gov (Mar. 7, 2019), https://www.fda.gov/ForPatients/Approvals/Devices/default.htm.
 Gail A. Van Norman, Drugs, devices, and the FDA: Part 2: An Overview of Approval Processes: FDA Approval of Medical Devices, 1 JACC: Basic to Translational Science 277, 277, 283 (2016).
 Id. at 279.
 Id. at 278.
 FDA, Developing a Software Precertification Program: A Working Model, at 6 (Jan. 2019), (hereinafter “Developing a Software Precertification Program”). available at https://www.fda.gov/downloads/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/UCM629276.pdf.
FDA, Learn about Device Approvals, https://www.fda.gov/ForPatients/Approvals/default.htm (last visited Mar. 8, 2019); see also FDA, Device Approvals, Denials and Clearances Databases, https://www.fda.gov/medicaldevices/productsandmedicalprocedures/deviceapprovalsandclearances/default.htm (last visited Mar. 8, 2019).
Developing a Software Precertification Program, supra note 10 at 6.
 Id. at 6.
 FDA, Software as a Medical Device, https://www.fda.gov/medicaldevices/digitalhealth/softwareasamedicaldevice/default.htm (last visited March 11, 2019); see also FDA, Center for Devices & Radiological Health Digital Health Program, Digital Health Innovation Action Plan, available at https://www.fda.gov/downloads/MedicalDevices/DigitalHealth/UCM568735.pdf.
 FDA, FDA Selects Participants for New Digital Health Software Precertification Pilot Program, (Sept. 26, 2017) https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm577480.htm (hereinafter “FDA Selects Participants for New Digital Health Program”).
 Developing a Software Precertification Program, supra note 14 at 11.
 FDA Selects Participants for New Digital Health Program, supra note 17.
 FDA Statement, Statement from FDA Commissioner Scott Gottlieb, M.D., on The Agency’s New Actions Under the Pre-Cert Pilot Program to Promote a More Efficient Framework for the Review of Safe and Effective Digital Health Innovations, (Jan. 7, 2019), https://www.fda.gov/NewsEvents/Newsroom/PressAnnouncements/ucm629306.htm.
 Id.; see also FDA, Software Precertification Program: Regulatory Framework for Conducting the Pilot Program within Current Authorities, (Jan. 2019), available at https://www.fda.gov/downloads/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/UCM629278.pdf (hereinafter “Regulatory Framework for Conducting the Program”).
 Regulatory Framework for Conducting the Program, supra note 22 at 1.
 FDA, Software Precertification Program 2019 Test Plan (Jan. 2019), available at https://www.fda.gov/downloads/MedicalDevices/DigitalHealth/DigitalHealthPreCertProgram/UCM629277.pdf.
 Id. at 2.
 See Developing a Software Precertification Program, supra note 14.
 See FDA, Medical Device Development Tools (MDDT), https://www.fda.gov/medicaldevices/scienceandresearch/medicaldevicedevelopmenttoolsmddt/.
 See Michael Borella, How to Draft Patent Claims for Machine Learning Inventions, Patent Docs (Nov. 25, 2018), https://www.patentdocs.org/2018/11/how-to-draft-patent-claims-for-machine-learning-inventions.html.