Enhancing precision medicine and wellness with computing and AI across clinical, imaging, environmental, multi-omics, wearable sensors, and socio-cognitive data

Abstract

The convergence of computational capabilities and data-driven methodologies, spurred by the onset of the Third Industrial Revolution (Digital Age) as well as their integration into robotic tools and medical implants, often referred to as the Fourth Industrial Revolution (Park, 2016), is instigating a substantive transformation in clinical decision support that is rapidly changing the economics and practice of healthcare (Sutton et al., 2020). Here we introduce the strengths, challenges, and future trajectories of computational medicine, informatics, and machine learning (ML) methods as applied in the realm of precision healthcare and wellness (Lee et al., 2018). Precision medicine and precision wellness, situated at the intersection of technological advancements in clinical decision-making methods that impact the use, reuse, transformations, and analysis of data ranging from the nanoscale to clinical and societal dimensions of measurements, is the focal point of our discussion. We approach this subject through a dual perspective: one being a clinical data-focused approach that incorporates biomedical informatics with syntactic and semantic methods of interoperability (Garde et al., 2007; Strasberg et al., 2021), as well as human-interpretable decision algorithm and the other being a bottom-up approach rooted in genomics, biophysical and multiscale methods that increasingly employ robust yet human-opaque ML analytics (Lussier and Li, 2012). We conclude with an exploration of the remaining challenges, prospective opportunities, and future directions that arise at the confluence of these multifaceted methodologies inclusive of artificial intelligence and large language models in medicine (Shehab et al., 2022).