Healthcare AI: New Approaches, Obstacles, and Prospects for Medical Technology

Abstract—The big-data age has come in cancer genomics due to the growth in scientific publication and the ubiquitous availability of genetic information made feasible by next-gen sequencing. Machine learning, deep learning, and natural language processing (NLP) are increasingly employed to solve data scalability and high dimensionality issues and convert large data sets into usable clinical information is the basis for precision medicine. This article looks at the current and future usage of AI in cancer genomics inside workflows to combine genomic analysis for precision cancer therapy. We examine existing AI solutions and their issues with cancer genomic testing and diagnostics, such variant calling and interpretation. This research and comparison examines publicly available tools or algorithms for key natural language processing technologies used in literature mining to give evidence-based treatment guidance. This research also examines the challenges of utilising AI in digital healthcare, such as data demands, algorithmic transparency, repeatability, and real-world assessment. It also discusses the need of preparing physicians and patients for digital healthcare. AI is the main engine of healthcare's shift to precision medicine, but we must also address the major issues it raises to ensure safety and a beneficial impact.