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Precision medicine—of which precision prescribing is a core component—is becoming a new frontier in today’s healthcare. Both artificial intelligence (AI) and machine learning (ML) have the potential to enhance our understanding of data and therefore our ability to accurately diagnose and treat patients. By leveraging these technologies and processes, we can uncover associations between a person’s genomic makeup and their health, identify biomarkers associated with diseases, fine-tune patient selection for clinical trials, reduce costs, and accelerate drug discovery and vaccine development. Although real-world data pose challenges in terms of collection, representation, and missing or inaccurate data sets, the integration of precision medicine into healthcare is critical. Clearly, precision medicine can benefit from health information innovations that empower decision-making at the patient level. Healthcare fusion is an example of an innovative framework and process [K Zhai et al. ECKM 2022, 20(3), pp. 179–192]. Data science and process improvement are also expected to play a role in resource planning and operational efficiency for optimal patient-centered care. Driving this transformation are advances in ‘omics’ technologies, digital devices, and imaging capabilities, along with an arsenal of powerful analytics tools working across a multitude of institutions and stakeholders. Encompassing this entire ecosystem, medicine will be evidence-based and driven by three key components: (1) Data curation through clinical diagnostics and behavioral apps that capture health and disease states; (2) Individualized solutions driven by advanced data analytics and personalized therapies; and (3) Business models that deliver value and incentivize growth. The aim of this paper is to present a novel conceptual framework to leverage AI and enhance information flow to serve the patient as per components one and two.

Translational medicine, the exchange between laboratory (bench) and the clinic (bedside), is decidedly taking on a vital role. Many companies are now focusing on a translational medicinal approach as a therapeutic strategy in decision making upon realizing the expenses of drug attrition in late-stage advancement. In addition, the utility of biomarkers in clinical decision and therapy guidance seeks to improve the patient outcomes and decrease wasteful and harmful treatment. Efficient biomarkers are crucial for the advancement of diagnoses, better molecular targeted therapy, along with therapeutic advantages in a broad spectrum of various diseases. Despite recent advances in the discovery of biomarkers, the advancement route to a clinically validated biomarker remains intensely challenging, and many of the candidate biomarkers do not progress to clinical applications, thereby widening the innovation gap between research and application. The present article will focus on the clinical view of biomarkers in a reverse design, addressing how a biomarker program should appear if it is expected to create an impact on personalized medicine and patient care.