From Single to Multi-Disease: AI Techniques in Medical Diagnostics

The healthcare sector has witnessed a transformative rise in disease diagnosis due to the emergence of Artificial Intelligence (AI) and other related processes. AI is central to success in healthcare because of the outstanding techniques which are offered by Machine Learning (ML) and Deep Learning (DL). The optimal diagnosis of disease through these techniques requires medical imaging data like ultrasound, Magnetic Resonance Imaging (MRI), mammography, and many more. This research paper covers a detailed, AI-assisted, medical survey based on the techniques used in identifying various diseases including eye diseases, lung disorders, skin diseases, and neurological disorders inclusive of Alzheimer's and cancer. The study further presents the key highlights, strengths, and limitations of allied techniques along with their result analysis. Along with the single disease prediction, this research paper additionally analyses the techniques on Multi-Disease (MltD) prediction using PRISMA guidelines. Three specific scientific databases including Web of Science (WOS), Scopus, and PubMed are utilized to identify the related articles, and in total 321 articles were obtained. After a thorough screening process, 27 articles are selected for detailed analysis to provide valuable insights for future advancements in MltD prediction models in the healthcare system.