Advancing precision medicine in medical education: Integrated, precise and data‐driven smart solutions

Abstract

Advances in “Precision Medicine” initiative, also known as “Personalized Medicine” is an emerging approach for disease treatment and prevention that has already led to innovative discoveries and has created “smart” applications and solutions tailored to a person's or a group of individuals' genetic profile, lifestyle, and environment interaction. Already many physicians as part of patient care routinely prescribe various molecular/genetic and other tests enabling them to select personalized treatments that improve the chances of survival and reduce exposure to adverse effects. This initiative should provide to medical and healthcare professionals with adequate resources and readily available solutions so that the target to specific treatments and care of the illnesses is achieved while at the same time protecting the privacy and safety of the individual is secured as well as the Electronic Health Records (EHRs) and whatever additional data is necessary within the context of Precision Medicine. This study, through a literature review mostly, and some case study analysis, examine whether personalized medicine is delivered to the patient in an “accurate” and “precise” way, as expected. This requires that Health and Human Services and other stakeholders and agencies collaborate to solicit the right input from patients while at the same time can identify and address any educational, practical, legal, and technical issues and providing smart solutions. The lack of proper medical education and advanced infrastructure are still major barriers to the adoption of Precision Medicine, therefore, the role of medical training in Precision Medicine is also examined and analyzed. Specific examples are discussed in an integrated, precise, and data‐driven manner to provide “smart” solutions.

Nuclear Cardiology in the Era of Precision Medicine: Tailoring Treatment to the Individual Patient

“…Data integration and analytics: Integrating clinical data, including imaging and laboratory results, with genetic information and environmental exposure data, is essential for developing personalized treatment plans. Artificial intelligence and machine learning algorithms are crucial in analyzing this vast data, identifying patterns, and predicting individual therapy responses [11].…”