AI Expert System for Migraine Diagnosis and Treatment: Applications and Risks

In the healthcare industry, artificial intelligence (AI) has grown to be a crucial tool for decision-making, especially in the areas of diagnosis, treatment planning, patient monitoring, and clinical decision support. Expert systems offer a unique approach among the various types of AI because they directly describe domain knowledge thru facts, rules, and inference procedures. The Migraine Diagnosis and Treatment Expert System (MDATES), a rule-based medical expert system created with the CLIPS production-rule environment, is the subject of a thorough case study in this paper. By analyzing clinical symptoms and connecting them with knowledge-based guidelines, MDATES is intended to help with migraine diagnosis and therapy suggestion. The approach takes into account a number of migraine symptoms and classifications, such as episodic and chronic migraine as well as specific migraine-related subtypes. The case study looks at the system's architecture, operation, applications, benefits, drawbacks, and hazards. An illustration of how expert knowledge can be converted into a computational decision-support system can be seen in the reported evaluation of MDATES. However, safe clinical implementation requires more than just strong diagnostic performance. Incomplete or out-of-date knowledge, wrong rules, poor generalizability, automation bias, algorithmic bias, privacy threats, cybersecurity, explainability restrictions, professional responsibility, and improper use beyond of the intended clinical scope are all significant dangers. Clinical AI systems need to go via rigorous validation, human-factor evaluation, fairness assessment, privacy protection, and ongoing monitoring, according to recent research. This paper makes the case that expert systems like MDATES should not be seen as independent substitutes for medical personnel, but rather as clinical decision-support tools. Transparent knowledge management, human supervision, external validation, safety monitoring, and unambiguous accountability are all necessary for responsible deployment.