The AI Patient Arc: Past, Present, Future

Opinder Bawa

An industry is a complicated network of connections between and among companies, customers and suppliers of goods and services.[i]  The transformation of any industry typically takes 30 to 50 years to complete and, as evidenced by how the Industrial Revolution impacted manufacturing and how Henry Ford’s assembly line impacted mass production, technology has always been a hallmark of such change.[ii]

The life sciences industry is no exception to this transformation timeline rule of thumb. By attempting to control healthcare costs and spending, as well as rectify disparities in healthcare access and quality among Americans, the 2010 Patient Protection and Affordable Care Act catalyzed the life science industry’s most recent transformation. As we approach the end of the first decade of this metamorphosis, we clearly see artificial intelligence (AI) at the center of this change, and its effects are far-reaching.

Today we see how AI substantially increases or effectively disrupts clinical trial supply chain efficiency – i.e. identifying, recruiting, and retaining patients; collecting data; core analysis; early interventions with potential subjects, etc. – and is proving essential to the successful conduct of future clinical trials and their outcomes. Those outcomes of AI-powered solutions also offer unprecedented opportunity to inform and alter daily healthcare in ways that will reap additional benefits for patients in the not-too-distant future.

US healthcare leaders are becoming more aware of this potential. One-third of healthcare leader respondents to a recent survey said they expect AI to improve the patient experience, and 31 percent believe AI will improve health outcomes.[iii] I am one of those believers.

Though AI’s application to healthcare may seem relatively recent, the AI-patient arc began some time ago, and continues to progress. It is a comprehensive spectrum, and no one part can fully be understood or appreciated without a consideration of the whole.

During the 1980s-1990s, before the life science industry’s current period of transformation, a variety of medical expert systems utilizing AI were designed, including:

• MYCIN, which used early AI tools to diagnose bacterium and recommend an appropriate antibiotic

• PUFF, which interpreted pulmonary function parameters to diagnose lung disease

• INTERNIST, which provided diagnoses in internal medicine.

However, the healthcare infrastructure-and general willingness-to leverage these expert systems did not exist, and they wound up being too expensive and cumbersome to deploy.  These complex implementations were further complicated by conflicting financial incentives.

Fast-forward to the new millennium, and examples of AI influencing and benefiting patient health are much more commonplace, thanks to emerging and more cost-effective technologies:

• January, 2017 – FDA approves a medical imaging device using deep learning to analyze MRI scans of the heart

• February, 2018 – FDA approves deep learning-based cancer detection platform to help radiologists diagnose lung and liver cancer

• February, 2018 – FDA approves AI-based stroke detection software to cut the time to clinical intervention

• April, 2018 – FDA approves AI-based platform that can diagnose diabetic retinopathy.

But these technologies are still largely for acute medical diagnosis and not embedded in a patient’s routine healthcare – yet.

The best is yet to come. 

As the life sciences industry continues its current technological transition, leading clinical trial sponsors striving for growth and success will further embrace and adopt clinical trial supply chain solutions offered by data analytics industry leaders. An example of this is Saama’s Life Science Analytics Cloud (LSAC), an AI-powered platform that seamlessly ingests, integrates, curates and harmonizes clinical trial operational and patient data from proprietary and external data sources to deliver actionable insights. Saama’s virtual assistant DaLIA (Deep Learning Intelligent Assistant), which harnesses Natural Language Processing (NLP) and Natural Language Understanding (NLU) to facilitate an unprecedented conversational experience with clinical trial data, is another example. DaLIA helps overcomes complexities and alleviates the burdens historically associated with clinical development, which improves the life sciences industry’s ability to deliver safe and effective therapies.

Going forward, solutions like these will begin to put a spotlight on early interventions, based on highly efficient clinical trials they facilitate. This will ultimately change the way healthcare is practiced overall, in particular by closing time-consuming gaps, including those between patients and medical outcomes tailored to their unique needs, fulfilling the promise of Precision Medicine. Eventually, the AI-powered technology that drives solutions like LSAC to support the clinical trial pipeline will be integrated into every aspect of healthcare and go hand-in-hand with well visits and acute care consultations. The admission of patients into clinical trials will become a daily practice for America’s healthcare providers, as just-in-time clinical trial matching technology evolves and dovetails with their day-to-day practice of medicine.

As healthcare moves more towards a proactive, preventative model from a reactive model, AI will help facilitate proactive, personalized medical interventions with predictive analytics.

The University of California’s (UC) Women Informed to Screen Depending on Measures of Risk (WISDOM) study is one such example. WISDOM’s goal is to determine if annual mammograms really are the best way to screen for breast cancer, or whether a more targeted and personalized approach factoring in data from a woman’s genetic makeup, family history, and individual risk factors would yield better results. A 5-year study initiated in 2017, WISDOM is a collaboration of the five UC medical centers to drive innovation in breast cancer prevention, screening, and treatment.

AI’s current and future impact on health is widely acknowledged and studied. The global research and consulting organization, Frost & Sullivan, predicts that AI and cognitive computing will result in savings of over $150 billion for the healthcare industry by 2025, and that the AI healthcare market will grow to $6.16 billion by 2022.[iv]

The story of how AI is improving all aspects of life sciences-from drug development to preventative healthcare to personalized medicine-is still being written. Keep in mind what I started this article with-transformations take place over a span of 30-50 years. But, undoubtedly, AI’s future impact on patients’ daily health will be largely informed and positively influenced by the amazing work being realized today by AI-based clinical trial supply chain technology.

Opinder Bawa is Vice President and Chief Information Officer at the University of San Francisco, and a member of Saama Technologies Clinical Board of Advisors.

[i] Harvard Business School, Industry Transformation, 9-701-008 July 10, 2000

[ii]https://www.forbes.com/sites/danielnewman/2017/08/08/top-5-digital-transformation-trends-in-manufacturing/#7a021d69249f

[iii] https://www.pharmavoice.com/article/2019-01-ai/

[iv]https://ww2.frost.com/news/press-releases/artificial-intelligence-healthcare-takes-precision-medicine-next-level/