Implementation of artificial intelligence in medicine

Artificial Intelligence (AI) simulates human-like intelligent behavior and critical thinking using computers and technology. John McCarthy introduced the term AI in 1956, defining it as the science and engineering of creating intelligent machines. Alan Turing, one of AI's founders, proposed the Turing Test to evaluate machine intelligence. AI gained interest in the 1980s and 1990s, with applications in healthcare using techniques like fuzzy expert systems, Bayesian networks, and neural networks. By 2016, healthcare saw the highest AI research investment. AI in medicine is divided into virtual (e.g., electronic health records) and physical (e.g., surgical robots) subcategories. AI aids diagnosis through flowcharts and database-driven deep learning approaches. Notable examples include projects by Google and Stanford University. AI also assists in patient referrals, diagnostics, predictions, and record organization. Effective AI algorithms require structured data and accuracy testing. Models like ChatGPT and Med-PaLM show potential in medical applications, enhancing diagnosis and treatment efficiency. This study reviews AI's implementation in medicine, its pros and cons, and relevant research.