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PubMed1987

Artificial intelligence. Expert systems for clinical diagnosis: are they worth the effort?

Modeling the decision-making processes of human experts has been studied by scientists who call themselves psychologists and by scientists who say they are students of artificial intelligence (Al). The psychological research literature suggests that experts' decision-making processes can be adequately captured by simple mathematical models. On the other hand, those in Al who are preoccupied with human expertise maintain that complex computer models, in the form of expert systems, are required to do justice to those same processes. The resultant paradox of simple versus complex decision-making models is investigated here. The relevant literatures in psychology and Al are reviewed and, based on these findings, a resolution of the paradox is offered.

Artificial Intelligence↗
PubMed2005

Comparison efficiency of the artificial intelligence methods for the diagnosis of Acid - base and anion gap disorders.

Diagnosis of the most complicated disorders in acid-base status and accompanying electrolyte balance creates a lot of troubles for practicing physicians. The purpose of our study was to create and compare: 1) an artificial neural network, 2) genetic program, 3) fuzzy-neural system that can diagnose acid-base disorders, based on a set of laboratory gasometric and electrolyte measurements. We took into account 7 single acid-base disorders, 11 double acid-base disorders and 6 triple complicated disorders with accompanying anion gap alterations. We prepared a set laboratory measurements consisting of 250 results for training and the same number of results for testing the program. Finally, the efficiency of presented artificial intelligence (AI) methods has been described and compared.

Acid-Base Equilibrium↗
PubMed1994

International Federation of Clinical Chemistry. Use of artificial intelligence in analytical systems for the clinical laboratory. IFCC Committee on Analytical Systems.

The incorporation of information-processing technology into analytical systems in the form of standard computing software has recently been advanced by the introduction of artificial intelligence (AI) both as expert systems and as neural networks. This paper considers the role of software in system operation, control and automation and attempts to define intelligence. AI is characterized by its ability to deal with incomplete and imprecise information and to accumulate knowledge. Expert systems, building on standard computing techniques, depend heavily on the domain experts and knowledge engineers that have programmed them to represent the real world. Neural networks are intended to emulate the pattern-recognition and parallel-processing capabilities of the human brain and are taught rather than programmed. The future may lie in a combination of the recognition ability of the neural network and the rationalization capability of the expert system. In the second part of this paper, examples are given of applications of AI in stand-alone systems for knowledge engineering and medical diagnosis and in embedded systems for failure detection, image analysis, user interfacing, natural language processing, robotics and machine learning, as related to clinical laboratories. It is concluded that AI constitutes a collective form of intellectual property and that there is a need for better documentation, evaluation and regulation of the systems already being used widely in clinical laboratories.

Artificial Intelligence↗
PubMed2026

The impact of artificial intelligence on critical thinking and clinical reasoning in health professions education: A systematic review and meta-analysis.

BACKGROUND: Critical thinking and clinical reasoning underpin healthcare professionals' ability to navigate uncertainties and deliver safe and effective care. With artificial intelligence (AI) advancement and growing adoption, AI-based educational tools are increasingly used to support these cognitive competencies' development. OBJECTIVE: To synthesize randomised and controlled clinical trials on AI-based educational tools in health professions education and examine their effects on critical thinking and clinical reasoning among health professions students. METHODS: Six electronic databases were searched from January 1, 2014 to July 28, 2025 was reviewed: PubMed, Cochrane Central Register of Controlled Trials, CINAHL, Scopus, Embase and Web of Science. Two independent reviewers performed data extraction and quality assessment using standardized JBI checklists. The GRADE approach was used to assess the certainty of evidence. Studies were pooled via random-effects meta-analyses or narrative syntheses. RESULTS: Fourteen randomised controlled trials and seven controlled clinical trials were included (n = 21). Meta-analyses revealed small to medium effect sizes for the surrogate clinical reasoning outcomes of performance-based assessment scores (SMD 0.68; 95% CI [0.38, 0.98], p-value = 0.00; I2 = 38%) and knowledge test scores (SMD 0.39; 95% CI [0.09, 0.69], p-value = 0.01; I2 = 79%). Critical thinking and clinical reasoning skills and dispositions were narratively synthesized, with majority of included studies favouring AI-based interventions but the evidence had low to very low certainty. CONCLUSION: AI-based educational interventions may improve critical thinking and clinical reasoning among health profession students, but the evidence is very uncertain. This review offers preliminary insights but does not allow identification of optimal interventions or discipline-specific recommendations due to small sample sizes and substantial intervention heterogeneity. Further research is required to draw definitive conclusions. PROTOCOL REGISTRATION: CRD42025634074.

Humans↗
PubMed2000

Contribution of artificial intelligence to the knowledge of prognostic factors in laryngeal carcinoma.

Many studies have investigated prognostic factors in laryngeal carcinoma, with sometimes conflicting results. Apart from the importance of environmental factors, the different statistical methods employed may have influenced such discrepancies. A program based on artificial intelligence techniques is designed to determine the prognostic factors in a series of 122 laryngeal carcinomas. The results obtained are compared with those derived from two classical statistical methods (Cox regression and mortality tables). Tumor location was found to be the most important prognostic factor by all methods. The proposed intelligent system is found to be a sound method capable of detecting exceptional cases.

Aged↗
PubMed2001

On prognostic models, artificial intelligence and censored observations.

The development of prognostic models for assisting medical practitioners with decision making is not a trivial task. Models need to possess a number of desirable characteristics and few, if any, current modelling approaches based on statistical or artificial intelligence can produce models that display all these characteristics. The inability of modelling techniques to provide truly useful models has led to interest in these models being purely academic in nature. This in turn has resulted in only a very small percentage of models that have been developed being deployed in practice. On the other hand, new modelling paradigms are being proposed continuously within the machine learning and statistical community and claims, often based on inadequate evaluation, being made on their superiority over traditional modelling methods. We believe that for new modelling approaches to deliver true net benefits over traditional techniques, an evaluation centric approach to their development is essential. In this paper we present such an evaluation centric approach to developing extensions to the basic k-nearest neighbour (k-NN) paradigm. We use standard statistical techniques to enhance the distance metric used and a framework based on evidence theory to obtain a prediction for the target example from the outcome of the retrieved exemplars. We refer to this new k-NN algorithm as Censored k-NN (Ck-NN). This reflects the enhancements made to k-NN that are aimed at providing a means for handling censored observations within k-NN.

Algorithms↗
PubMed1997

Artificial intelligence elements in multimedia system for surgery.

The paper presents a concept of a computer system designed to assist a surgeon's work both before and during a surgical operation. The aim of multimedia in the system is to ensure comfort in communication between an operating surgeon and his assistants. The elements of artificial intelligence, on the other hand, are to assist the surgeon in taking optimal decisions in difficult and untypical situations.

Artificial Intelligence↗
PubMed1990

Use of a composite polyfunctional model electrophile as a probe to analyze the performance of an artificial intelligence structure-activity method.

The CASE structure-activity relational method was applied to the model polyfunctional electrophile proposed by Ashby and associates. The predicted activities from data bases of 'structural alerts', mutagenicity in Salmonella and rodent carcinogenicity were compared. It was thus found that the predictive efficacy of CASE was increased when it employed a combination of human and artificial intelligence, as exemplified by the CASE analysis of 'structural alerts.

Animals↗
PubMed2004

Conflicts versus analytical redundancy relations: a comparative analysis of the model based diagnosis approach from the artificial intelligence and automatic control perspectives.

Two distinct and parallel research communities have been working along the lines of the model-based diagnosis approach: the fault detection and isolation (FDI) community and the diagnostic (DX) community that have evolved in the fields of automatic control and artificial intelligence, respectively. This paper clarifies and links the concepts and assumptions that underlie the FDI analytical redundancy approach and the DX consistency-based logical approach. A formal framework is proposed in order to compare the two approaches and the theoretical proof of their equivalence together with the necessary and sufficient conditions is provided.

Algorithms↗
PubMed1986

Artificial intelligence in medical diagnosis: the INTERNIST/CADUCEUS approach.

The development of computers has provided a potential tool to assist in the management of the information explosion in medicine. The field of medical diagnosis is intellectually challenging and has attracted the attention of computer scientists interested in building expert systems using artificial intelligence techniques. This paper reviews some of the problems of medical diagnosis and discusses examples of programs representing different approaches to solving these problems. The programs developed in our laboratory, INTERNIST-1/CADUCEUS, are discussed in some detail.

Artificial Intelligence↗
PubMed1998

Using artificial intelligence to automate remittance processing.

The consolidated business office of the Allegheny Health Education Research Foundation (AHERF), a large integrated healthcare system based in Pittsburgh, Pennsylvania, sought to improve its cash-related business office activities by implementing an automated remittance processing system that uses artificial intelligence. The goal was to create a completely automated system whereby all monies it processed would be tracked, automatically posted, analyzed, monitored, controlled, and reconciled through a central database. Using a phased approach, the automated payment system has become the central repository for all of the remittances for seven of the hospitals in the AHERF system and has allowed for the complete integration of these hospitals' existing billing systems, document imaging system, and intranet, as well as the new automated payment posting, and electronic cash tracking and reconciling systems. For such new technology, which is designed to bring about major change, factors contributing to the project's success were adequate planning, clearly articulated objectives, marketing, end-user acceptance, and post-implementation plan revision.

Accounts Payable and Receivable↗
PubMed1988

Modern pacemakers: implantable artificial intelligence?

For over twenty years computer programs have been written for applications in cardiology. Approximately one decade ago the first Expert Systems using knowledge on cardiology were built. The use of Expert System shells enabled a breakthrough. Many systems were developed, each covering a small part of cardiology. In the pacing environment Expert Systems can be used to select the optimal pacemaker therapy or to analyze complex pacemaker electrocardiograms. With the help of these artificial intelligence techniques pacemakers can be designed, that behave in an intelligent way. Finally this knowledge representation enables the reproducible simulation of the heart-pacemaker interaction.

Artificial Intelligence↗
PubMed1988

Artificial intelligence in medical diagnosis.

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.

Artificial Intelligence↗
PubMed2001

An artificial intelligent algorithm for tumor detection in screening mammogram.

Cancerous tumor mass is one of the major types of breast cancer. When cancerous masses are embedded in and camouflaged by varying densities of parenchymal tissue structures, they are very difficult to be visually detected on mammograms. This paper presents an algorithm that combines several artificial intelligent techniques with the discrete wavelet transform (DWT) for detection of masses in mammograms. The AI techniques include fractal dimension analysis, multiresolution markov random field, dogs-and-rabbits algorithm, and others. The fractal dimension analysis serves as a preprocessor to determine the approximate locations of the regions suspicious for cancer in the mammogram. The dogs-and-rabbits clustering algorithm is used to initiate the segmentation at the LL subband of a three-level DWT decomposition of the mammogram. A tree-type classification strategy is applied at the end to determine whether a given region is suspicious for cancer. We have verified the algorithm with 322 mammograms in the Mammographic Image Analysis Society Database. The verification results show that the proposed algorithm has a sensitivity of 97.3% and the number of false positive per image is 3.92.

Algorithms↗
PubMed2000

[Diagnosis of cerebellum-pontine angle tumors using artificial intelligence techniques].

We are concerned in this paper with learning classification procedures from known cases. More precisely, we provide a diagnostic model that discriminate between cerebellum-pontine angle (CPA) tumors and otorhinolaryngological (ENT) disorders. Usually, in order to distinguish between CPA tumors and ENT disorders one must perform clinical-neurological examination together with expensive radiological imagery (CT and MRI). The proposed model was obtained through artificial intelligence methods and presented a good accuracy level (88.4%) when tested against new cases, considering only clinical examination without radiological imagery results.

Artificial Intelligence↗
PubMed1987

A therapy planning architecture that combines decision theory and artificial intelligence techniques.

Through our experience with the ONCOCIN cancer therapy consultation system, we have identified a set of medical planning problems to which no single existing computer-based reasoning technique readily applies. In response to the need for automated assistance with this class of problems, we have devised a computer program called ONYX that combines decision-theoretic and artificial intelligence approaches to planning. We discuss our rationale for devising a new planning architecture and describe in detail how that architecture is implemented. The program's planning process consists of three steps: (i) the use of rules derived from therapy planning strategies to generate a small set of plausible plans, (ii) the use of knowledge about the structure and behavior of the human body to create simulations that predict possible consequences of each plan for the patient, and (iii) the use of decision theory to rank the plans according to how well the results of each simulation meet the treatment goals. This architecture explicitly manages the uncertainty inherent in many planning tasks, introduces a possible mechanism for the dissemination of decision-theoretic therapy advice, and potentially increases the number of problem solving domains in which expert system techniques can be effectively applied.

Artificial Intelligence↗
PubMed1989

Self-organisation and living systems: Is DNA an 'artificial intelligence'?

There seems little doubt that the maintenance and development of living systems is crucially dependent on an internal organisation of monumental complexity--particularly in higher living species. It is suggested that current thinking--particularly relating to the role of DNA in the total process cannot explain the underlying mechanisms and that a radical rethinking will be necessary. To this end it is proposed that DNA has a unique molecular electronic structure enabling it to operate as a computer analogue system for the highly efficient storage of information and as a type of artificial intelligence through which the information is translated and implemented to organise and control all aspects of the construction and activity of living systems.

Artificial Intelligence↗