Artificial Intelligence in Medical Science

2014 ◽  
pp. 11-23 ◽  
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Decision Making ◽  
Signal Processing ◽  
Expert System ◽  
Knowledge Base ◽  
Medical Diagnosis ◽  
Medical Science ◽  
Inference Engine ◽  
Medical Decision

Applying Artificial Intelligence (AI) for increasing the reliability of medical decision making has been studied for some years, and many researchers have studied in this area. In this chapter, AI is defined and the reason of its importance in medical diagnosis is explained. Various applications of AI in medical diagnosis such as signal processing and image processing are provided. Expert system is defined and it is mentioned that the basic components of an expert system are a “knowledge base” or KB and an “inference engine”. The information in the KB is obtained by interviewing people who are experts in the area in question.

scholarly journals Expert System for Diagnosis of Uterine Myomas using the Certainty Factor Method

2021 ◽  
Vol 1 (4) ◽  
pp. 103-108
Author(s):  
Syahrizal Dwi Putra ◽  
M Bahrul Ulum ◽  
Diah Aryani
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Computer System ◽  
Decision Making Process ◽  
Certainty Factor ◽  
Uterine Myomas ◽  
Factor Method ◽  
Two Factors

An expert system which is part of artificial intelligence is a computer system that is able to imitate the reasoning of an expert with certain expertise. An expert system in the form of software can replace the role of an expert (human) in the decision-making process based on the symptoms given to a certain level of certainty. This study raises the problem that many women experience, namely not understanding that they have uterine myomas. Many women do not understand and are not aware that there are already symptoms that are felt and these symptoms are symptoms of the presence of uterine myomas in their bodies. Therefore, it is necessary for women to be able to diagnose independently so that they can take treatment as quickly as possible. In this study, the expert will first provide the expert CF values. Then the user / respondent gives an assessment of his condition with the CF User values. In the end, the values obtained from these two factors will be processed using the certainty factor formula. Users must provide answers to all questions given by the system in accordance with their current conditions. After all the conditions asked are answered, the system will display the results to identify that the user is suffering from uterine myoma disease or not. The Expert System with the certainty factor method was tested with a patient who entered the symptoms experienced and got the percentage of confidence in uterine myomas/fibroids of 98.70%. These results indicate that an expert system with the certainty factor method can be used to assist in diagnosing uterine myomas as early as possible.

Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

Emerging Application of Fuzzy Expert System in Medical Domain

2015 ◽  
pp. 1-20 ◽  
Author(s):  
A. V. Senthil Kumar ◽  
M. Kalpana
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Expert System ◽  
Fuzzy Inference ◽  
Fuzzy Expert System ◽  
Inference Mechanism ◽  
Medical Practitioners ◽  
Fuzzy Operators ◽  
Decision Making Problem ◽  
Fuzzy Inference Mechanism

Fuzzy expert system is an artificial intelligence tool that helps to resolve the decision-making problem with the existence of uncertainty and plays an important role in medicine for symptomatic diagnostic remedies. In this chapter, construction of Fuzzy expert system is the focused, which helps to diagnosis disease. Fuzzy expert system is constructed by using the fuzzification to convert crisp values into fuzzy values. Fuzzy expert system consists of fuzzy inference, implication, and aggregation. The system contains a set of rules with fuzzy operators T-norm and of T-Conorm. By applying the fuzzy inference mechanism, diagnosis of disease becomes simple for medical practitioners and patients. Defuzzification method is adopted to convert the fuzzy values into crisp values. With crisp values, the knowledge regarding the disease is given to medical doctors and patients. Application of Fuzzy expert system to diagnosis of disease is mainly focused on in this chapter.

The Evaluation of Wireless Devices Used by Staff at Westmead Hospital, Sydney

2011 ◽  
pp. 96-105
Author(s):  
Sandra Synthia Lazarus
Keyword(s):  
Decision Making ◽  
Medical Diagnosis ◽  
Medical Information ◽  
Personal Digital Assistant ◽  
Medical Decision ◽  
Data Repository ◽  
Wireless Devices ◽  
Hospital Data ◽  
Westmead Hospital ◽  
Medical Reports

This chapter reports on a study to research and evaluate the use of latest generation wireless devices—typically personal digital assistant devices (PDAs)—by clinical staff at the large Westmead Hospital located in the west of Sydney, Australia. Currently, medical reports in this and other hospitals are primarily recorded on paper supported by personal computers at nursing stations. However, there is very little or no access to medical reports and decision-making tools for medical diagnosis at the patient’s bedside—the precise location at which most medical decision-making occurs. Delays in access to essential medical information can result in an increased time taken for accurate diagnosis and commencement of appropriate medical management of patients. This chapter discusses the application of hand held devices into more powerful processing tools connected to a centralised hospital data repository that can support medical applications.

The Evaluation of Wireless Devices Used by Staff at Westmead Hospital

2011 ◽  
pp. 811-821
Author(s):  
Sandra Synthia Lazarus
Keyword(s):  
Decision Making ◽  
Medical Diagnosis ◽  
Medical Information ◽  
Personal Digital Assistant ◽  
Medical Decision ◽  
Data Repository ◽  
Wireless Devices ◽  
Hospital Data ◽  
Westmead Hospital ◽  
Medical Reports

This chapter reports on a study to research and evaluate the use of latest generation wireless devices— typically personal digital assistant devices (PDAs)—by clinical staff at the large Westmead Hospital located in the west of Sydney, Australia. Currently, medical reports in this and other hospitals are primarily recorded on paper supported by personal computers at nursing stations. However, there is very little or no access to medical reports and decision-making tools for medical diagnosis at the patient’s bedside—the precise location at which most medical decision-making occurs. Delays in access to essential medical information can result in an increased time taken for accurate diagnosis and commencement of appropriate medical management of patients. This chapter discusses the application of hand held devices into more powerful processing tools connected to a centralised hospital data repository that can support medical applications.

scholarly journals Technology of Supporting Medical Decision-Making Using Evidence-Based Medicine and Artificial Intelligence

2020 ◽  
Vol 176 ◽  
pp. 1703-1712
Author(s):  
Georgy Lebedev ◽  
Eduard Fartushnyi ◽  
Igor Fartushnyi ◽  
Igor Shaderkin ◽  
Herman Klimenko ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Evidence Based Medicine ◽  
Medical Decision Making ◽  
Medical Decision ◽  
Evidence Based ◽  
Based Medicine

Limits of trust in medical AI

2020 ◽  
Vol 46 (7) ◽  
pp. 478-481 ◽  
Author(s):  
Joshua James Hatherley
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Diabetic Retinopathy ◽  
Deep Learning ◽  
Clinical Practice ◽  
Medical Decision Making ◽  
Hospital Readmissions ◽  
New Drugs ◽  
Medical Decision ◽  
Practice Of Medicine

Artificial intelligence (AI) is expected to revolutionise the practice of medicine. Recent advancements in the field of deep learning have demonstrated success in variety of clinical tasks: detecting diabetic retinopathy from images, predicting hospital readmissions, aiding in the discovery of new drugs, etc. AI’s progress in medicine, however, has led to concerns regarding the potential effects of this technology on relationships of trust in clinical practice. In this paper, I will argue that there is merit to these concerns, since AI systems can be relied on, and are capable of reliability, but cannot be trusted, and are not capable of trustworthiness. Insofar as patients are required to rely on AI systems for their medical decision-making, there is potential for this to produce a deficit of trust in relationships in clinical practice.

May We Support Your Decision?

1996 ◽  
Vol 1 (3) ◽  
pp. 175-178 ◽  
Author(s):  
Colin Gordon
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Decision Making ◽  
Clinical Practice ◽  
Decision Support ◽  
Health Care Professionals ◽  
Medical Decision Making ◽  
Electronic Patient Record ◽  
Medical Decision ◽  
Technical Developments

Expert systems to support medical decision-making have so far achieved few successes. Current technical developments, however, may overcome some of the limitations. Although there are several theoretical currents in medical artificial intelligence, there are signs of them converging. Meanwhile, decision support systems, which set themselves more modest goals than replicating or improving on clinicians' expertise, have come into routine use in places where an adequate electronic patient record exists. They may also be finding a wider role, assisting in the implementation of clinical practice guidelines. There is, however, still much uncertainty about the kinds of decision support that doctors and other health care professionals are likely to want or accept.

Study on Fault Diagnosis Expert System of Aircraft Power-Supply System

2014 ◽  
Vol 945-949 ◽  
pp. 2617-2622 ◽  
Author(s):  
Xu Yan Zhuang ◽  
Ya Yun Xu ◽  
Yong Bao
Keyword(s):  
Fault Diagnosis ◽  
Expert System ◽  
Knowledge Base ◽  
Power Supply ◽  
Power Supply System ◽  
System Development ◽  
Inference Engine ◽  
Supply System ◽  
Development Platform ◽  
Development Tool

We always regard aircraft power supply system as the "blood system" of aircraft. It plays a very important role in aircraft work. In view of its fault diagnosis present situation and in order to improve fault diagnosis efficiency, we put forward to use expert system development tool CLIPS to build up fault diagnosis expert system. In this paper, we choose the power-supply system of Cirrus SR20 as diagnosis object, and choose CLIPS as development tool to build up knowledge base and inference engine. By using Eclips development platform to write interface programs and using CLIPS JNI to call CLIPS programs we successfully complete the expert system total performance including knowledge base, inference engine and interface.

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