AI in Healthcare Decision-Making: Can Algorithm Equal Expertise

Abstract

The utilization of AI systems serves as powerful tools to improve diagnostic precision, plan treatment processes, and even increase the quality of healthcare services. The objective of this paper is to address the question, Can AI algorithms match or surpass human expertise in healthcare decision-making? I carry out an evaluation of AI based systems against human clinical performance through the lenses of key performance indices of the clinical practice; the diagnostic accuracy, decision time, and reliability. This investigation is based on a systematic review of existing scholarly literature on applications of artificial intelligence, including of techniques of machine learning (ML), deep learning (DL), and natural language processing (NLP) models of computer vision and speech recognition systems, and their use in medicine. The analysis shows that AI systems are superior to human specialists in specific types of diagnostic work such as radiology and pathology because they are better at recognizing patterns, processing data, and building predictive models. On the other hand, autonomous AI systems lack aspects of the complex human reasoning that is contextually ethical, competent, and intuitive which is necessary for clinicians in decision-making where issues are not clear. The research concludes that even though AI can assist human skills and enhance the effectiveness of the healthcare system, the best decision making comes from hybrid structures where AI’s computational edge is integrated with human reasoning and ethical faculties. Forthcoming studies must focus upon ameliorating the shortcomings of AI, elevating the standards of data, and enhancing the collaboration of clinicians and AI systems to facilitate better care.