Artificial Intelligence in Medical Diagnosis

Abstract

In an attempt to overcome limitations inherent in conventional computer-aided diagnosis, investigators have created programs that simulate expert human reasoning. Hopes that such a strategy would lead to clinically useful programs have not been fulfilled, but many of the problems impeding creation of effective artificial intelligence programs have been solved. Strategies have been developed to limit the number of hypotheses that a program must consider and to incorporate pathophysiologic reasoning. The latter innovation permits a program to analyze cases in which one disorder influences the presentation of another. Prototypes embodying such reasoning can explain their conclusions in medical terms that can be reviewed by the user. Despite these advances, further major research and developmental efforts will be necessary before expert performance by the computer becomes a reality.

A fuzzy expert system for diagnosis and treatment of musculoskeletal disorders in wrist

“…• Knowledge base includes structured data to encode medical knowledge; • Inference engine includes procedures to use knowledge and directs the rules in the knowledge base; • Working memory protects data and facts when the program runs; • User interface provides an interface between the system and application for effective use; • Description mechanism explains the results and tracks rules used to achieve results [2]; • Elites, the expert system needs to acquire their knowledge to insert into the knowledge base; • User uses the expert system; • Knowledge engineering collaborates the elite to shape subject knowledge to be transferred to the computer [11]; • Knowledge acquisition utilities allow adding, editing and deleting rules in the knowledge base [2].…”

Cloud enabled data analytics and visualization framework for health-shocks prediction

“…These uncertainties are proportionally related to the number and types of variables, the interactions necessary with lay individuals, domain experts and organizations and the sampling methods in collecting the data. It is hard for patients for instance to present their own symptoms and describe how they feel due to language barrier, illiteracy (especially in developing countries), and usage of non-medical vocabulary, which prevents practicing physicians and paramedics from accurately describing and recording their observations [15]. Additionally there are hundreds of inaccuracies due to carelessness of lab technicians and malfunctioning equipment, even under ideal conditions [16].…”

How do experts recognize schizophrenia: the role of the disorganization symptom

“…Expert systems are most useful for medical specialties in which a well-established etiopathogenesis accounts for a disease model. 2 Since the etiopathogenesis of most psychiatric disorders is unknown, a definitive disease model and its constructs have not yet been validated.…”

“…5 Since the 1970's, artificial intelligence techniques have been applied to develop expert systems. 2 The main focus of the research in this area was to transfer human skills into intelligent tools.…”