Artificial intelligence (AI) is a system that aims to bring human thinking ability to machines and thus solve complex tasks more easily and can continuously improve itself with the data it collects. AI technologies are used in many fields, such as education, media, banking, and the defense industry. In recent years, AI has begun to manifest itself in various fields of health services, from diagnosis to treatment and patient follow-up. AI algorithms can facilitate patients' access to healthcare services, provide remote patient monitoring, shorten the diagnosis process of diseases, provide patient-specific treatment recommendations, or allow physicians to improve their practices. Thus, it can improve patient care and increase patient satisfaction, reduce costs, and speed up healthcare services. Increasing telemedicine applications during the pandemic contributed to the acceleration of the utilization of AI in healthcare, and AI-based algorithms for diagnosing and treating diseases began to be developed rapidly. With the increasing prevalence of wearable technologies and the introduction of electronic health records, there has been a tremendous explosion in individual health data. AI has contributed to health services in collecting and processing this rapidly increasing data. Although AI has a promising future in health, it also brings many ethical problems. AI systems make decisions based on the data they are trained on. If there is not enough data diversity or if biased data is used, AI systems may give inaccurate predictions or learn and reproduce these preconceptions. This manuscript is a literature review examining the impacts of AI in the healthcare sector and discusses the history of AI, AI studies applied for the diagnosis and treatment of diseases, telemedicine and preventive medicine applications, and the disadvantages of AI.
Başer et al. (Sun,) studied this question.
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