Digital Medicine – The New Frontier for AI in Healthcare

Abstract

Traditionally, the field of medicine has largely been practiced using population-based approaches.1 This is clearly illustrated in the context of drug development for many indications that span oncology to infectious diseases and pain management, among others. For example, patient therapy is conventionally given using one-size-fits-all protocols, which virtually precludes realizing optimal objective response rates and overall survival outcomes. Emerging strategies are seeking to harness novel analytics platforms to interrogate population-based big data sets to parse out improved treatments that will still largely be used2 to treat individuals. It is important to note, however, that while it is common knowledge that substantial inter-patient variability in treatment outcomes exists, the degree of intra-patient variability at the N-of-1/single patient level, can potentially be so substantial that truly personalized healthcare will require dynamic modulation of the intervention and/or the intensity of care in order to sustain the optimization of efficacy and safety. While population-driven strategies will ultimately enhance the ability to identify effective interventions, the ability to use a single patient's data to guide the patient's own care will also be needed to effectively implement these interventions with unprecedented precision. The ability to individualize care will broadly impact multiple facets of medicine, ranging from drug-based therapy to emerging digital therapeutics platforms that are harnessing software as treatment. By harnessing digital medicine to realize N-of-1 interventions, it is possible to render patient responses that are more uniformly efficacious compared to traditional approaches. Effectively integrating these platforms into healthcare delivery workflows is expected to improve healthcare outcomes and cost effectiveness over traditional approaches. Among the many areas that can benefit from AI/digital medicine, N-of-1 medicine and digital therapeutics have tremendous potential to improve outcomes and reduce healthcare cost inflation. These advances have been bridged with substantial growth in the private sector to scale AI and digital medicine deployment into practice. According to Galen Growth Asia and Rock Health, two digital health analytics platforms, US$5.0B was invested in Asia and US$7.4B was invested in the US in 2019 demonstrating clear investor enthusiasm for investing in the future of healthcare.3 This special issue discusses key areas where digital medicine and AI are poised to markedly individualize intervention and enhance treatment outcomes, citing examples from the field, and provide examples of recent advances that will serve as a gateway towards next-generation, AI-enhanced digital medicine. The promise of personalized medicine will ultimately reduce the inter-patient and intra-patient variability in treatment outcomes that are observed, which are the key drivers of sub-optimal efficacy and safety that is observed across the stages of early drug development, clinical trials, and the postapproval point of care.4 Unfortunately, implementing the entire workflow of personalizing care is often incomplete, as there are many considerations that must be considered in this domain, ranging from how interventional trials are designed, to the strategies employed to maximize the number of patients that initially and continue to respond positively to treatment. One major challenge in developing novel drug treatments is the ability to sufficiently interrogate the space created by the range of possible drugs for a given indication. This is a challenge that confronts all aspects of treatment design, regardless of indication. Therefore, it impacts everything from oncology to metabolic diseases and metabolic diseases and beyond. In a recent study, an orthogonal array composite design (OACD)-driven set of drug combinations was subsequently used to optimize the identification of multi-drug regimens. Thi