Artificial intelligence in medicine and medical education

摘要

Medicine is a multidisciplinary subject that is continuously evolving as medical science advances resulting in new paradigms related to the diagnosis, prognosis, treatment, and patient management of various diseases generating big data consisting of a large number of measurements and clinical data in the process. With this evolution, there is a need to restructure medical education. As artificial intelligence (AI) begins to be used to mine the enormous amount of big data generated from various medical specialties to derive more accurate paradigms for disease models and improve clinical decision-making, there will be a need for a sophisticated computer–doctor interaction to enhance the practice of medicine. Therefore, medical professionals will need to be trained in the understanding of the applications of AI in both basic and clinical medicine including its advantages such as the ability to integrate data from different modalities to arrive at more accurate diagnoses and treatment of complex diseases in a cost-effective and expeditious manner, and its limitations such as the ethics behind the use of AI, transparency, and the inability to fully understand the complex algorithms that AI is built upon. AI is a discipline in computer science that focuses on the development of algorithms and software that mimic human thought and decision-making. Due to the complexity of the human body and the various disease models, an enormous amount of data was generated in the past 20 years since the completion of the Human Genome Project.[1] Genomics data augmented data from other biomedical disciplines such as radiology, pathology, internal medicine, and surgery to produce more accurate diagnostic and therapeutic strategies for various diseases. AI has become mainstream in medicine as a way to integrate data from different modalities, platforms, and clinical data. Advances in AI that have become key in medical practice include the natural language process used to mine clinical records and data to derive hidden patterns associated with various diseases or medical phenomena.[2,3] In addition, software that relies on AI such as chatGPT[4] has been used in various aspects of medical education, including the construction of medical scenarios with different levels of difficulty to advance existing medical educational tools such as problem-based learning (PBL) by providing different medical scenarios. Speech recognition is another subset of AI that has been used in the diagnosis of various medical diseases, including Parkinson's.[5,6] Virtual chatbots were used in medicine[7] to create personalized learning experiences for students, by analyzing each student's strengths, weaknesses, and learning style and providing tailored content and feedback. Decision management systems and medical expert systems were also used to augment the diagnosis of various medical diseases and in medical education to provide the medical student with ways to test their knowledge in the diagnosis and prognosis of complex medical problems. In addition, machine learning was used to identify specific biomarkers for various cancers by mining multi-OMICs data, including genomics, transcriptomics, and epigenetics.[8] Deep learning was used in identifying hidden phenomena related to basic medical sciences, for example, unraveling the folding of various human proteins using algorithms developed by DeepMind,[9] also AI was used to identify baricitinib as therapy for COVID-19.[10] AI software and algorithms were used to augment hardware in robotic process automation, where it showed some success in automated solvent extraction.[11] In addition, AI is currently used to provide more accurate and expeditious diagnosis, to integrate data from different medical specialties such as radiology and pathology in the diagnosis and prognosis of various diseases, reduce human errors, decrease the cost of identifying biomarkers and treatment, reduce repetitive and labor-intensive tasks, and minimall