Abstract

A subset of machine learning (ML) known as “deep learning” (DL) has gained popularity recently as a result of advancements in the use of artificial neural networks, huge data, and processing power. DL in healthcare is significantly influencing the healthcare system by enhancing diagnosis and raising patient outcome standards. DL helps clinicians analyze data and discover a variety of illnesses, such as heart difficulties and cancers, that can be found using picture analysis, cancer diagnosis using malignant cells found in the body, diabetes patients' blood sugar levels, and cancer that can be found in blood samples. Rapid disease detection made possible by the application of DL and ML has allowed doctors to save lives quickly, spend priceless time with their patients, and decrease hospital stays and healthcare costs.
With the ability to handle “large complex data,” DL and ML have become quite popular over the past 10 years and are now finding application in the healthcare industry. Computational models built on neural networks can learn to describe data at different levels of abstraction, thanks to DL. When ML is used in clinical decision-making, it implies that the system will interpret a particular individual by collecting and analyzing data pertinent to that individual's health, and it will then use the data to explain about the best method that should be used to maintain or improve the individual's health. According to research, ML has the potential to aid in the identification of numerous mental diseases as well as to enhance patient outcomes.
The most quickly expanding concept of the 21st century is the incorporation of AI into medical research. The great potential of AI in healthcare has been revealed by the dramatic rise in AI-related research and publications over the past 10 years. In order to manage the massive and multidimensional data necessary for healthcare research, ML is used. Other applications of AI algorithms in healthcare include diagnosis, prognostication, decision assistance, screening and triage, and treatment suggestion. When used in clinical trials, ML has the potential to improve the process' generalizability, patient-centeredness, accuracy, and success. From preclinical drug development to pretrial planning, including study implementation, to data management and analysis, ML works across the spectrum of clinical trials in healthcare. In actuality, DL machines would not take the job of advisors; instead, they will enhance our abilities to diagnose aberrant lesions in remote settings in a clear and understandable context.
DLM may offer new hope and an affordable alternative for health service providers in low-income countries with diverse, low-density demographics who want to take advantage of technological advancements in developed countries and receive relatively comparable standard care at reasonable costs while investing in expanding their care provision and addressing the shortage of medical professionals needed to reach their goal of universal health coverage.