application of artificial intelligence in clinical diagnosis training

today, due to the increase in survival rates for acute and chronic patients, the rapid pace of scientific advancements, and the changing patterns of diseases, accurate medical and nursing diagnoses have become more complex. this complexity has led to consequences such as higher patient costs and ineffective treatment systems (1). currently, patients receive the correct diagnosis and treatment less than 50% of the time during their first visit. there is clear evidence of a 13 to 17-year gap between research and practice in clinical healthcare (2). this indicates that current methods of transferring scientific knowledge to clinical settings are ineffective (3). training doctors and nurses to understand and remember all the complexities of modern healthcare, even within their own specialties, is a costly and time-consuming process. for example, the average training time for a surgeon is currently 10 years or 10,000 hours (4).since 1956, when linguists, mathematicians, and psychologists first defined the concept of artificial intelligence, this technology has made significant advancements and expanded its influence in various aspects of human life. from automated driving and search engines to mobile phone-based software like social media and entertainment programs, artificial intelligence has become an integral part of daily life (5). in the field of medical education, artificial intelligence has revolutionized the landscape and paradigm. over the past three decades, the development of artificial intelligence has shown promise in updating medical education and enhancing clinical training. by creating simulated environments based on multiple scenarios to improve decision-making, understanding, and predicting treatment outcomes, artificial intelligence has the potential to transform medical education for both professors and students (6).studies have indicated that traditional clinical decision support systems are not as effective as they could be, and incorporating artificial intelligence into clinical diagnosis training could be beneficial. software programs equipped with artificial intelligence that are accessible via mobile phones and connected to reliable global databases have the capability to analyze observational and interventional data. this suggestion is particularly appealing to the new generation of students who are more inclined towards virtual education.