Exploring the Feasibility of Autonomous Diagnosis with AI/ML for Smart Health Care
Atul Kumar Dadhich, BABITHA B.S, Rajkumari Ghosh · 2024
Self-reliant diagnosis with synthetic Intelligence (AI) and gadget mastering (ML) has the potential to revolutionize healthcare by way of improving diagnosis accuracy and growing performance. AI/ML can be used to hit upon sicknesses from imaging scans, analyze massive datasets to become aware of traits in crucial fitness signs, and provide customized chance assessments. This study explores the feasibility of the use of AI and ML for autonomous prognosis, along with the opportunities, capability packages, challenges, and boundaries of such an era. A key consideration whilst comparing AI/ML for diagnosis is accuracy. AI/ML algorithms require access to categorized datasets, which consist of statistics points with correct descriptions of what they represent. In any other case, the algorithms can't appropriately recognize which styles in a dataset indicate a sure diagnosis. To expand an AI/ML device for self-reliant analysis properly, the accuracy of the dataset used to train the AI/ML algorithms needs to be of the highest nice. Similarly, AI/ML algorithms ought to be capable of accounting for variability in datasets which come from one-of-a-kind hospitals or medical imaging systems. Moreover, AI/ML algorithms ought to make decisions approximately interpretations of information in situations in which a definitive answer cannot be reached. In these cases, the AI/ML device should be able to evaluate the available fact points, decide the most in all likelihood analysis.