Strategies to Unlock AI’s Potential in Health Care, a Mintz Series
The Journal of the American Medical Association in its September 18, 2018 issue included four articles on deep learning and Artificial Intelligence (AI). In one of several viewpoint pieces, On the Prospects for a (Deep) Learning Health Care System, the author’s conclusions aptly describe why health care providers, entrepreneurs, investors and even regulators are so enthusiastic about the use of AI in health care:
Pressures to deploy deep learning and a range of tools derived from modern data science will be relentless, given the extraordinarily rich information now available to characterize and follow vast numbers of patients, the ongoing challenges of making sense of the complexity of human biology and health care systems, and the potential for smart information technology to support tomorrow’s clinicians in the provision of safe, effective, efficient, and humanistic care.
It seems that just about every week people who work in or are interested in health care are talking about AI. The Partnership for Artificial Intelligence and Automation in Healthcare (PATH) recently held its first ever Summit in Washington D.C. to address AI best practices, new innovations and regulatory and other barriers to widespread adoption of AI in clinical settings.
Rapid developments in this space are unfolding alongside existing regulatory regimes developed to address earlier technologies that need to be carefully navigated. Payments based on AI applications present challenges for payers who are only now beginning to address payment for telehealth technologies, let alone AI. Products with AI also present unique contractual and liability-related issues that must be addressed before and during commercialization. Mintz is examining this space, with a particular focus on AI’s impact on the health care sector. Beginning today, we will be issuing a series of blog posts on topics of interest to those developing or commercializing AI for use in the health care delivery system. Our posts will address:
- How to get a patent for your AI related invention
- Navigating the FDA rules for a medical device with AI
- Privacy and security considerations for the use of AI
- Commercialization strategies, including how to structure arrangements that maximize revenue and minimize risk
- Payment for clinical applications
- Product liability concerns
We will also look to our colleagues at ML Strategies for a post on what to expect from Capitol Hill in this area in the coming months.
Published alongside this introduction is our first installment of the series. The post, by our colleague Christina Sperry, examines common pitfalls to avoid when attempting to patent AI-related innovations.
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