Incorporating Artificial Intelligence (AI) for Precision Medicine: A Narrative Analysis

1. Introduction

Precision Medicine was originally developed thousands of years ago in ancient times. Since then, other therapy techniques have been developed. Traditional remedies, even today, do not take into account individuals' quirks and genetic make-up, and so fail to be useful in certain situations. The drive for more accurate and successful therapy eventually led to the establishment of a scientific branch known as “personalized medicine” (Visvikis-Siest et al., 2020). Recent breakthroughs in our knowledge of the intricate interaction of health and illness have given rise to the notion of “precision medicine.” While the field of medicine has always sought precision, the term now refers to the ambitious goal of identifying and treating precise biological alterations such as specific genetic variations, transcriptional or post-transcriptional molecular pathways, or mechanisms of regulation (Rani et al., 2021).

Patients with comparable symptoms or indications, or individuals with histologically comparable tumors, can be divided into discrete subgroups defined by specific molecular anomalies, necessitating specialized therapies. Precision medicine has the ability to unleash personalized therapy and achieve better results by gaining a better grasp of individual data. Precision medicine promises to personalize medicines to particular requirements by collecting thorough insights into each individual's specific data, resulting in enhanced treatment efficacy and patient outcomes (Bottani et al., 2020; Seymour et al., 2017).

During the Classical period, when medicine was divided into sections and each doctor specialized in one disease or one body part, Herodotus emphasized the adaptability of that old “Egyptian medicine” to an individual's health situation. This was the beginning of personalized medicine, when doctors realized that categorizing diseases based on human body parts may help researchers get a better understanding of illness and, as a result, better treatment outcomes. The Greeks were drawn to this therapeutic practice, and their treatises frequently praised Egyptian medicine. Egyptian medicine did not begin to decline until the fifth century, with the birth of Hippocratic medicine (Jouanna, 2012).

The editors of a recent National Academy of Medicine report on the current and future function of artificial intelligence (AI) in healthcare emphasized AI's extraordinary potential for expanding the work of healthcare professionals while decreasing human constraints such as fatigue and apathy, as well as the risks of machine oversights. The paper emphasizes the importance of using these technologies with caution while acknowledging their great potential. Digitalization of healthcare data and rapid technological innovation are driving revolutionary shifts in the development and deployment of AI in healthcare (Khang & Ragimova et al., 2022).

Data and security, analytics, insights, and shared knowledge are critical components for effective AI adoption. Data and security promote openness and confidence in AI system training and data utilization (Wang, 2019; Johnson, 2021). The goal of precision medicine is to enhance and optimize the approach to evaluation, therapy, and prognosis by utilizing enormous biological datasets with several dimensions that represent individual differences in genes, function, and habitat. This allows doctors to personalize early intervention measures, whether therapeutic or preventive, for each patient. Using existing multivariate clinical and biological data and high-performance computing techniques, artificial intelligence (AI) systems can currently forecast risk in a variety of cancers and cardiovascular illnesses with reasonable accuracy (Uddin, 2019). The creation of biomarkers in precision medicine for early-stage lung cancer is one illustration. Biomarkers are physiological properties that may be measured. A biomarker might be anything as straightforward as blood pressure or heart rate. Because we need to analyze the impact of experimental therapies on humans during clinical trials, biomarkers are critical in drug development.