Expert systems are computer programs that solve problems using knowledge from human experts to simulate human reasoning. They are also called knowledge-based systems or inference-based programs. Expert systems are different from traditional programs in that they process nonnumerical or symbolic information and use heuristic processing, which allows them to handle complex, non-linear relationships. Traditional programs process well-defined, alphanumeric information and use algorithmic processing, which is step-by-step and rigid. Expert systems have several components, including a knowledge base, an inference engine, and a user interface. The knowledge base contains the programmed knowledge of the expert, including both book knowledge and practical knowledge or heuristics. The inference engine is the real "know how" of an expert system, which applies the knowledge from the knowledge base to solve the problem. It works by chaining, which is reasoning about the problem, and there are two main mechanisms: forward chaining and backward chaining. Expert systems are developed using expert system shells, which are software packages designed to develop expert systems in many areas of knowledge. There are two ways to develop expert systems: inductively or deductively. Inductive expert system shells create expert systems from processing example cases, while deductive expert system shells allow knowledge to be directly programmed, usually in if...then rules. Expert systems are used in various fields, including medicine and insurance. In medicine, they have been used to interpret pulmonary function tests, review clinical pathological conferences, determine appropriate chemotherapy for certain cancers, diagnose rheumatological disease, and evaluate patients with suspected transient ischemic attacks. In insurance, expert systems are being developed in areas such as marketing and sales, underwriting, claims adjudication, investments, and data processing. Expert systems are not replacing experts but are productivity aids that eliminate routine work and provide decision assistance. They allow for expert decision making to be programmed and electronically distributed, ensuring consistent decisions throughout an organization. They are also excellent training tools, as they demonstrate the thought process of experts to users. However, they will never replace the need for experts, especially medical directors and underwriters.Expert systems are computer programs that solve problems using knowledge from human experts to simulate human reasoning. They are also called knowledge-based systems or inference-based programs. Expert systems are different from traditional programs in that they process nonnumerical or symbolic information and use heuristic processing, which allows them to handle complex, non-linear relationships. Traditional programs process well-defined, alphanumeric information and use algorithmic processing, which is step-by-step and rigid. Expert systems have several components, including a knowledge base, an inference engine, and a user interface. The knowledge base contains the programmed knowledge of the expert, including both book knowledge and practical knowledge or heuristics. The inference engine is the real "know how" of an expert system, which applies the knowledge from the knowledge base to solve the problem. It works by chaining, which is reasoning about the problem, and there are two main mechanisms: forward chaining and backward chaining. Expert systems are developed using expert system shells, which are software packages designed to develop expert systems in many areas of knowledge. There are two ways to develop expert systems: inductively or deductively. Inductive expert system shells create expert systems from processing example cases, while deductive expert system shells allow knowledge to be directly programmed, usually in if...then rules. Expert systems are used in various fields, including medicine and insurance. In medicine, they have been used to interpret pulmonary function tests, review clinical pathological conferences, determine appropriate chemotherapy for certain cancers, diagnose rheumatological disease, and evaluate patients with suspected transient ischemic attacks. In insurance, expert systems are being developed in areas such as marketing and sales, underwriting, claims adjudication, investments, and data processing. Expert systems are not replacing experts but are productivity aids that eliminate routine work and provide decision assistance. They allow for expert decision making to be programmed and electronically distributed, ensuring consistent decisions throughout an organization. They are also excellent training tools, as they demonstrate the thought process of experts to users. However, they will never replace the need for experts, especially medical directors and underwriters.