Comprehensive Review of AI Applications in Medical Diagnostics: Techniques, Challenges, and Future Directions
TL;DR
An all-encompassing review of current advances in AI requests across miscellaneous healing concentration, containing endemic disease discovery, main central nervous system, cardiology, tumor, and ophthalmology, and oncology is determined.
Abstract
Artificial intelligence (AI) has enhance critical to implant healing interpreter, accompanying the allure of the potential to improve the speed, veracity, and ability of ailment disease and classification This study determines an all-encompassing review of current advances in AI requests across miscellaneous healing concentration, containing endemic disease discovery, main central nervous system, cardiology, tumor, and ophthalmology. The review examines standard AI systems to a degree, deep learning, machine intelligence, and mixture models, emphasizing their effectiveness in demonstrative tasks including biosignals, clinical dataset, and medical depictions. In addition, the study debate challenges to executing AI in healthcare, including limited data, model interpretability, moral concerns, and legal limits. The study decides by investigating future directions, including allied education, explainable AI, and unification accompanying the Internet of Medical Things (IoMT). In order to close the scientific and practical gaps between AI research and clinical use, this study integrates insights from infectious illnesses, cardiology, neurology, ophthalmology, and oncology. For researchers, physicians, and legislators seeking to appreciate the potential and disadvantages of artificial intelligence in reconstructing medical diagnoses, this study serves as a priceless resource.