Unlocking new potential of clinical diagnosis with artificial intelligence: Finding new patterns of clinical and lab data.

Abstract

Recent advancements in science and technology, coupled with the proliferation of data, have also urged laboratory medicine to integrate with the era of artificial intelligence (AI) and machine learning (ML). In the current practices of evidence-based medicine, the laboratory tests analysing disease patterns through the association rule mining (ARM) have emerged as a modern tool for the risk assessment and the disease stratification, with the potential to reduce cardio-vascular disease (CVD) mortality. CVDs are the well recognised leading global cause of mortality with the higher fatality rates in the Indian population due to associated factors like hypertension, diabetes, and lifestyle choices. AI-driven algorithms have offered deep insights in this field while addressing various challenges such as healthcare systems grappling with the physician shortages. Personalized medicine, well driven by the big data necessitates the integration of ML techniques and high-quality electronic health records to direct the meaningful outcome. These technological advancements enhance the computational analyses for both research and clinical practice. ARM plays a pivotal role by uncovering meaningful relationships within databases, aiding in patient survival prediction and risk factor identification. AI potential in laboratory medicine is vast and it must be cautiously integrated while considering potential ethical, legal, and pri-vacy concerns. Thus, an AI ethics framework is essential to guide its responsible use. Aligning AI algorithms with existing lab practices, promoting education among healthcare professionals, and fostering careful integration into clinical settings are imperative for harnessing the benefits of this transformative technology.