Abstract

The healthcare sector caters to millions of people and makes a significant contribution to the local economy. The inclusion of artificial intelligence and machine learning in healthcare is not only benefiting society but also overcoming various challenges associated with it. Artificial intelligence is a branch of computer science that is used to induce human-like intelligence into machines. Machine learning is a subset of artificial intelligence that makes machines capable of learning and giving the desired conclusions without explicit programming and human support. Machine learning in the healthcare sector is making huge advancements and yielding positive results. The increasing applications of machine learning have earned it a valuable spot in the healthcare sector. From specialized robots in hospitals to automated software for disease prediction and detection, machine learning is taking over almost all areas of healthcare with the aim of reducing the workload of medical experts and also delivering services to individuals at home with cost-effective solutions. With the advancement of technology, the introduction of portable systems has led to the availability of enormous amounts of medical data, which is difficult to analyze by human experts because it takes a lot of time, effort, and analytical costs. Machines are better in speed, endurance, and pattern identification as compared to humans. With the introduction of machine learning in healthcare, the task of managing massive data has become easier as automated machine learning models not only help in data analysis but are also capable of detecting underlying data patterns that may be difficult for clinical experts to come across. Machine learning can ease the task of identifying and detecting various diseases by providing complex algorithms such as Artificial Neural Networks (ANNs). With the introduction of neural networks, the analysis can be done on various data parameters given their ability to self-learn, memorize, and provide quality treatment. Machine learning not just focuses on the physical well-being of an individual but also their mental health by coming up with artificial-intelligence-based mood trackers and self-assessing applications for stress diagnosis. One of the major applications of machine learning is to detect and identify dangerous diseases, such as diabetes and cancer, that are difficult to detect at the initial stage and are detected at subsequent stages when it is too late. The use of early detection systems can save many lives by providing timely treatment of patients. Another important application of machine learning in the healthcare field is the introduction of bionic microchips. The fusion of bionics and machine learning will bring a revolutionary change in the healthcare sector. One such example is implanting bionic chips in the brain to monitor brain activity for the identification of neurological disorders like epilepsy. The AIenabled bionic hand uses a man-machine interface to interpret the patient's intent and send the commands to the artificial limb, thus helping the patient make more natural movements and controlling the prosthetics more precisely. There is a tremendous use of machine learning and artificial intelligence in providing customized solutions to patients, as one solution does not cater to many patients. Therefore, customized solutions according to their medical history are a feasible choice. Machine learning plays an enormous role in drug discovery by improving decision-making in pharmaceutical data through high-quality data. It provides immediate assistance to the patients using the healthcare chatbot systems that suggest immediate solutions to them. There is no area left in the healthcare industry of which machine learning is not a part. Machine learning in the healthcare industry can yield efficient and timely results without any human intelligence. This is just the beginning. Machine learning in healthcare has a bright future that will revolutionize the field of medicine and healthcare.