The increasing comorbidity rates, which are described as a patient having more than two medical conditions, present a significant problem in contemporary healthcare. The co-occurrence of various disorders makes their diagnosis challenging, postpones clinical decision-making, and leads to unsatisfactory therapeutic gains in most cases. Traditional diagnostic and prognostic methods are often time-consuming, and they are lacking in the capability to precisely forecast the development of diseases and the interaction of comorbidities. These restraints provide the acute necessity of technological advancement of solutions that could improve the early diagnosis and clinical treatment. The review article focuses on the implementation of Artificial Intelligence (AI) in the initial prediction, diagnosis, and detection of different comorbidities. The use of these algorithms in medical diagnostics and the improvement of healthcare brought about a revolutionary shift in machine learning within the healthcare industry. Precise predictions on patient outcomes are now within reach, thanks to the training of algorithms on large medical datasets. With the help of AI-feeding systems, it is possible to integrate electronic health records, laboratory results, and medical imaging data to reveal specific correlations and initial risk factors that cannot always be observed by conventional methods. By keeping an eye out for small changes that could suggest health problems, machine learning helps doctors make accurate diagnoses and personalize treatments. The most prevalent application of this technology is to use supervised learning to develop an efficient treatment intervention plan based on patient data.
Singh et al. (Wed,) studied this question.