Artificial intelligence expert systems for clinical diagnosis: Are they worth the effort?

Carroll, B A

doi:10.1002/bs.3830320404

Cited by 23 publications

(8 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This problem relates to the difficulty of finding available experts that are recognized in the field as knowledgeable. Often intelligent systems' developers assume human expertise to be 'the ideal' and do not question the suggestions of the experts and, similarly, clinicians also seem to have high confidence in their decisions (Carroll, 1994;Ridderikhoff & van Herk, 1999).…”

Section: Acquiring Informationmentioning

confidence: 98%

Neural Expert Systems in Medical Image Interpretation: Development, Use, and Ethical Issues

Pouloudi¹,

Magoulas²

2000

Journal of Intelligent Systems

View full text Add to dashboard Cite

In this paper, the appropriate level and role of neural network-based methodologies in the development and use of expert systems for medical image interpretation is investigated as technical, organizational, and social issues become intertwined. The notion of the information life cycle is applied to highlight ethical issues during the acquisition, processing and storage, dissemination and use of clinical information. These issues are further analyzed from a stakeholder perspective to accentuate the role of human agents in avoiding ethical risks. Relevant stakeholders, other than the key participants-namely system developers and medical users-are identified. The results of this analysis indicate that each stage of the development and use of a neural expert system entails ethical issues. Significantly, the responsibility for medical image interpretation is affected by contextual factors and should be shared amongst the main stakeholders. These conclusions are useful for the stakeholder groups that are conscious of their obligation to behave ethically and for researchers who wish to investigate further the ethical implications of artificial intelligence use in medicine. +

show abstract

Section: Acquiring Informationmentioning

confidence: 98%

Neural Expert Systems in Medical Image Interpretation: Development, Use, and Ethical Issues

Pouloudi¹,

Magoulas²

2000

Journal of Intelligent Systems

View full text Add to dashboard Cite

show abstract

“…Such situations arise because even post hoc it may not be clear what the proper answer should have been (e.g., what negotiation strategy should be adopted, what clause should be inserted into a contract). The most apparent example of differences in success metrics is that knowledge-based systems research tends virtually to ignore technical validity (Carroll, 1987;Turban & Aronson, 1998). This is a consequence of the assumption that human experts are the normative standard and that the experts are able to achieve an outstanding level of decisionmaking performance (Hayes-Roth et al, 1983), and also reflects the difficulty of validating output in knowledge-based environments.…”

Section: Success In the Knowledge-based System Traditionmentioning

confidence: 99%

“…In addition, researchers in knowledge-based systems use other criteria that are not used by vanilla DSS researchers. For example, a major goal of knowledge-based systems is to mimic the reasoning process that an expert would have used (Carroll, 1987;Davis & Lenat, 1980;Rangaswamy et al, 1987Rangaswamy et al, , 1989Turban & Aronson 1998).…”

Section: Success In the Knowledge-based System Traditionmentioning

confidence: 99%

See 1 more Smart Citation

Marketing Decision Support and Intelligent Systems: Precisely Worthwhile or Vaguely Worthless?

Eisenstein¹,

Lodish²

2002

Handbook of Marketing

View full text Add to dashboard Cite

“…As a conse-quence, much research effort has been spent on rule-based or cognitive expert systems. Statistical systems, such as that of de Dombal [13][14][15] for the diagnosis of abdominal pain, can only offer statistical probabilities and no detailed explanations despite their greater diagnostic accuracy [16].…”

Section: Construction Of the Expert Systemsmentioning

confidence: 99%

Expert Systems for the Prediction of Ovulation: Comparison of an Expert System Shell (Expertech Xi Plus) with a Program Written in a Traditional Language (BASIC)

Schreiner¹,

Chard²

1990

Methods Inf Med

View full text Add to dashboard Cite

The use of an expert system shell (EXPERTECH Xi Plus) in the construction of an expert system for the diagnosis of infertility has been evaluated. A module was devised for predicting ovulation from the medical history alone. Two versions of this system were constructed, one using the expert system shell, and the other using QuickBASIC. The two systems have been compared with respect to: (1) ease of construction; (2) ease of knowledge base update; (3) help and explanation facilities; (4) diagnostic accuracy; (5) acceptability to patients and clinicians; (6) user-friendliness and ease of use; (7) use of memory space; and (8) run time. The responses of patients and clinicians were evaluated by questionnaires. The predictions made by the computer systems were compared to the conclusions reached by clinicians and to the “gold standard” of day 21 progesterone.The conclusions of this pilot study are: (1) the construction of this expert system was NOT facilitated by the use of this expert system shell; (2) update of the knowledge base was not facilitated either; (3) the expert system shell offered built-in help and explanation facilities, but as the system increased in complexity these became less useful; (4) after initial adjustment of decision thresholds the diagnostic accuracy of the system equalled that of the clinician; (5) the patient response to computer history-taking was very favorable but much less favorable to computer diagnosis; (6) the clinicians took a positive attitude to computer diagnosis; (7) the systems were easy to use; (8) the expert systems shell required much more memory space and had a much slower response time than the system written in BASIC.

show abstract

Artificial intelligence expert systems for clinical diagnosis: Are they worth the effort?

Cited by 23 publications

References 40 publications

Neural Expert Systems in Medical Image Interpretation: Development, Use, and Ethical Issues

Neural Expert Systems in Medical Image Interpretation: Development, Use, and Ethical Issues

Marketing Decision Support and Intelligent Systems: Precisely Worthwhile or Vaguely Worthless?

Expert Systems for the Prediction of Ovulation: Comparison of an Expert System Shell (Expertech Xi Plus) with a Program Written in a Traditional Language (BASIC)

Contact Info

Product

Resources

About