Some reflections on the evaluation of expert systems in medicine.
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Artificial Intelligence↗
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Telemedicine and distributed medical intelligence.
Recent trends in health care informatics and telemedicine indicate that systems are being developed with a primary focus on technology and business, not on the process of medicine itself. The authors present a new model of health care information, distributed medical intelligence, which promotes the development of an integrative medical communication system addressing the process of providing expert medical knowledge to the point of need. The model incorporates audio, video, high-resolution still images, and virtual reality applications into an integrated medical communications network. Three components of the model (care portals, Docking Station, and the bridge) are described. The implementation of this model at the East Carolina University School of Medicine is also outlined.
Computer Communication Networks↗
Modular representation of the guideline text: an approach for maintaining and updating the content of medical education.
One of the principal challenges in the medical practice is the update of their knowledge. One of the prime roles of the Continuing Medical Education is to train the medical practitioners with the latest advances in health care, specialized to their needs. Online courses and classroom teaching with computer-based representations have become an established mode of delivering medical education. This paper deals with the modularized representation of a medical text concerning clinical practice guidelines. The proposed system takes into consideration the semantics of the Unified Medical Language System and is based upon the marking up and display of the knowledge using the XML and XSLT languages. This modularization of the concepts leads to the determination of the context of a portion or the whole document. Thus, after marking up using our system, the text components can be exchanged, modified or reconstructed, which, in turn, would help to maintain the updates in medical knowledge.
Artificial Intelligence↗
[ON THE DIFFERENTIAL DIAGNOSIS OF AUDIMUTITAS].
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Audiometry↗
Intellectual evaluations of adolescents via human figure drawings: an empirical comparison of two methods.
The human figure drawings of 200 adolescent boys were collected at a residential treatment center in a midsized, midwestern city. The drawings were scored for cognitive ability according to the systems of Buck (1966) and Goodenough and Harris (1963). Both scoring systems showed acceptable interrater reliability and both were positively and significantly related to IQ scores on the Wechsler Intelligence Scale for Children-Revised. Buck's system, however, had less of a tendency to underestimate IQ scores. Buck's system may therefore hold greater promise for the intellectual assessment of adolescents with human figure drawings.
Adolescent↗
Reusable influence diagrams.
Influence Diagrams have been recognized as a suitable formalism for building probabilistic expert systems. Nevertheless, the most part of applications consists in stand-alone systems, concerning a very limited domain. On the other hand, Artificial Intelligence research has outlined Blackboard Architectures as the basis for building expert systems in which several knowledge sources, in general built with different formalisms, cooperate to the solution of a complex task. This paper addresses the use of influence diagrams as knowledge sources of such a system, and particularly faces the problem of reusing the same influence diagram in different inference phases. We will show that, specially in planning tasks, the modularity requirement of keeping the knowledge sources separated, may imply that an influence diagram must call another influence diagram to solve itself and to maintain the coherence of the whole set of decisions underlying the plan. Conditions for the correctness of this concatenation of knowledge sources will be provided, and an example from the medical domain of therapy planning for Acute Myeloid Leukemia will be shown, as an implemented prototype exploiting these ideas.
Acute Disease↗
Medical knowledge systems: applications to telemedicine.
The application and form of electronically stored medical knowledge has a direct impact on the design of any healthcare delivery system. For those who plan for remote medical care or who work at the disaster relief level, there are specific requirements which will dictate the type of knowledge required and the vehicle best suited to deliver that information. The PC based multimedia biomedical library developed for NASA was originally intended for long-term space missions where complete isolation from each support was a distinct possibility. The library is a combination of traditional references and secondary databases structured within a primary care physician's workstation. The integration of the library and a point-of-care system allows optimal use of both resources and provides a basic building block for telemedicine networking.
Artificial Intelligence↗
Clinical decision-support for diagnosing stress-related disorders by applying psychophysiological medical knowledge to an instance-based learning system.
OBJECTIVE: An important procedure in diagnosing stress-related disorders caused by dysfunction in the interaction of the heart with breathing, i.e., respiratory sinus arrhythmia (RSA), is to analyse the breathing first and then the heart rate. Analysing these measurements is a time-consuming task for the diagnosing clinician. A decision-support system in this area would reduce the analysis task of the clinician and enable him/her to give more attention to the patient. We have created a decision-support system which contains a signal classifier and a pattern identifier. The system performs an analysis of the physiological time series concerned which would otherwise be performed manually by the clinician. METHODS: The signal-classifier, HR3Modul, classifies heart-rate patterns by analysing both cardio- and pulmonary signals, i.e., physiological time series. HR3Modul uses case-based reasoning (CBR), using a wavelet-based method for retrieving features from the signals. The system searches for familiar shapes in the signals by comparing them with shapes already stored. We have applied a best fit scheme for handling signals of different lengths, as the length of a breath is highly dynamic. We also apply automatic weighting to the features to obtain a more autonomous system. The classified heart signals indicate if a patient may be suffering from a stress-related disorder and the nature of the disorder. These classified signals are thereafter sent to the second subsystem, the pattern-identifier. The pattern-identifier analyses the classified signals and searches for familiar patterns by identifying sequences in the classified signals. The identified sequences give clinicians a more complete analysis of the measurements, providing them with a better basis for diagnosis. RESULTS AND CONCLUSION: We have shown that a case-based classifier with a wavelet feature extractor and automatic weighting is a viable option for building a decision-support system for the psychophysiological domain, as it is at par, or even outperforms other retrieval techniques and is less complex.
Algorithms↗
GLIF3: a representation format for sharable computer-interpretable clinical practice guidelines.
The Guideline Interchange Format (GLIF) is a model for representation of sharable computer-interpretable guidelines. The current version of GLIF (GLIF3) is a substantial update and enhancement of the model since the previous version (GLIF2). GLIF3 enables encoding of a guideline at three levels: a conceptual flowchart, a computable specification that can be verified for logical consistency and completeness, and an implementable specification that is intended to be incorporated into particular institutional information systems. The representation has been tested on a wide variety of guidelines that are typical of the range of guidelines in clinical use. It builds upon GLIF2 by adding several constructs that enable interpretation of encoded guidelines in computer-based decision-support systems. GLIF3 leverages standards being developed in Health Level 7 in order to allow integration of guidelines with clinical information systems. The GLIF3 specification consists of an extensible object-oriented model and a structured syntax based on the resource description framework (RDF). Empirical validation of the ability to generate appropriate recommendations using GLIF3 has been tested by executing encoded guidelines against actual patient data. GLIF3 is accordingly ready for broader experimentation and prototype use by organizations that wish to evaluate its ability to capture the logic of clinical guidelines, to implement them in clinical systems, and thereby to provide integrated decision support to assist clinicians.
Artificial Intelligence↗
A neural basis for general intelligence.
Universal positive correlations between different cognitive tests motivate the concept of "general intelligence" or Spearman's g. Here the neural basis for g is investigated by means of positron emission tomography. Spatial, verbal, and perceptuo-motor tasks with high-g involvement are compared with matched low-g control tasks. In contrast to the common view that g reflects a broad sample of major cognitive functions, high-g tasks do not show diffuse recruitment of multiple brain regions. Instead they are associated with selective recruitment of lateral frontal cortex in one or both hemispheres. Despite very different task content in the three high-g-low-g contrasts, lateral frontal recruitment is markedly similar in each case. Many previous experiments have shown these same frontal regions to be recruited by a broad range of different cognitive demands. The results suggest that "general intelligence" derives from a specific frontal system important in the control of diverse forms of behavior.
Adult↗
Artificial Intelligence for Colorectal Surgeons-Part II: Research Applications, Challenges in Adoption, and Practical Resources.
BACKGROUND: This is part II of a 2-part series examining artificial intelligence in colorectal surgery. Part I established foundational concepts and clinical applications. Implementation, however, requires understanding research methodologies, available resources, and the specific challenges currently limiting widespread adoption. These topics are the focus of part II. OBJECTIVE: To examine artificial intelligence's transformation of surgical research, provide practical implementation resources, address adoption challenges, and explore future directions in colorectal surgery. METHODS: Comprehensive literature review focusing on artificial intelligence research methodology, implementation barriers, educational resources, and emerging technologies relevant to colorectal surgeons. RESULTS: Artificial intelligence streamlines clinical trial design through predictive modeling and natural language processing, reducing enrollment challenges that contribute to failed or inadequate trial accrual. Machine learning enables heterogeneity analysis within clinical trials, identifying treatment-responsive subgroups. Foundation models unlock analysis of unstructured electronic health record data at scale. Professional societies and universities offer specialized artificial intelligence education programs, with open-access data sets facilitating research participation. However, implementation faces multifaceted challenges: technical infrastructure demands, with real-time processing requiring dedicated graphics processing unit clusters; regulatory frameworks struggling with continuously evolving algorithms; undefined liability distribution for artificial intelligence-assisted decisions; algorithmic bias risking health care disparities; and the "black box" problem limiting clinical trust. Economic barriers include substantial initial costs without clear reimbursement pathways. Future directions include multimodal artificial intelligence integrating imaging, genomics, and histopathology; cognitive robotic systems with real-time decision support; digital twin technology for patient-specific surgical simulation; and global surgical artificial intelligence networks enabling distributed learning across institutions. CONCLUSIONS: Although artificial intelligence offers transformative potential for colorectal surgery research and practice, successful implementation requires addressing technical, regulatory, ethical, and economic challenges. The surgeon's evolving role demands both traditional expertise and computational fluency. Future advances in multimodal integration, autonomous systems, and global collaboration will fundamentally reshape surgical practice but will require thoughtful implementation prioritizing patient benefit and clinical value.
Humans↗
Thesauri and formal classifications: terminologies for people and machines.
Terminologies are now software. They are key components of the integration of electronic patient records, decision support systems and information retrieval systems. To be used as software, the different types of content in traditional terminologies must be separated, which we term here: conceptual, linguistic, inferential and pragmatic. The conceptual knowledge at the heart of the terminology needs to be expressed formally in order to provide a dependable framework for the other types of knowledge. Information left implicit in most existing coding and classification systems must be made explicit. The test of the resulting terminologies is how well they support software for key functions: including data entry, information retrieval, mediation, indexing, and authoring.
Abstracting and Indexing↗
Effects of temporal smearing on temporal resolution, frequency selectivity, and speech intelligibility.
Envelopes of speech were smeared in 23 parallel frequency channels. The smeared speech was presented to normal-hearing listeners, and the effects of different smearing magnitudes on speech intelligibility were measured by obtaining speech recognition scores. It was demonstrated theoretically and experimentally that the system consisting of the computer smearing and the auditory system had reduced temporal resolution but nearly normal frequency resolution. Speech intelligibility of the processed vowel-consonant nonsense syllables was tested for low- and high-pass filter conditions. The overall speech recognition scores as well as the recognition scores of the consonants grouped according to articulatory features were analyzed. The results indicated that smearing with a narrow temporal window did not degrade speech. The larger equivalent rectangular durations (ERDs) of the resultant temporal window (RTW) of the combined system (temporal smearing plus auditory system) produced a small but significant reduction in speech intelligibility for the low-pass filter condition. Scores for the RTWs > 16 ms were significantly different from the score for the 7.7-ms RTW for the high-pass filter condition, but this effect was small and did not differ across articulatory features.
Adolescent↗
Decision support system for classification of epilepsies in childhood.
Diagnosis of epilepsy in childhood is often difficult as the symptoms are often atypical and the epilepsy syndromes are multiform. Methods from the domain of artificial intelligence give the opportunity to formalize medical knowledge and standardize various diagnostic procedures in specific domains of medicine. We developed a decision support system using artificial intelligence techniques for the classification and ultimately the diagnosis of epilepsies and epilepsy syndromes in children. The system incorporates knowledge from the International Classification of Epilepsies and Epileptic Syndromes. It was assessed using clinical data and the system's conclusions were compared with the diagnoses proposed by an experienced doctor. The system and the physician reached identical diagnoses in 85.2% of the cases. In an additional 8.2% of the cases, the system's diagnosis was similar to that of the physician, thus raising its overall success rate to 93.4%. The system can be helpful, especially for trainees, since it only needs to import the clinical and laboratory data. Decision making and differential diagnosis are then performed automatically.
Child↗
Discovering patterns to extract protein-protein interactions from full texts.
MOTIVATION: Although there are several databases storing protein-protein interactions, most such data still exist only in the scientific literature. They are scattered in scientific literature written in natural languages, defying data mining efforts. Much time and labor have to be spent on extracting protein pathways from literature. Our aim is to develop a robust and powerful methodology to mine protein-protein interactions from biomedical texts. RESULTS: We present a novel and robust approach for extracting protein-protein interactions from literature. Our method uses a dynamic programming algorithm to compute distinguishing patterns by aligning relevant sentences and key verbs that describe protein interactions. A matching algorithm is designed to extract the interactions between proteins. Equipped only with a dictionary of protein names, our system achieves a recall rate of 80.0% and precision rate of 80.5%. AVAILABILITY: The program is available on request from the authors.
Algorithms↗
[An expert system for choosing prostheses for the lower extremities (hip) and assessing the quality of the prosthesis. I].
The authors describe the structure, characteristic features and experience gained with the employment of an expert system for the choice of hip prostheses (in cases of unilateral amputation) and assessment of the prosthetics quality. Word the requirements for hardware and software, present functional subsystems corresponding to certain interrelated stages of prosthetics. A variant has been devised of an appropriate training expert system with a profound representation of intelligence in an object area.
Expert Systems↗
Light and electron microscopy of the cornea in systemic mucopolysaccharidosis type I-S (Scheie's syndrome).
A 37-year-old man with coarse facies, stiff joints, corneal clouding, and normal intelligence sought medical attention. The diagnosis of a systemic mucopolysaccharidosis (MPS) type I-S (Scheie's syndrome) was confirmed by the presence of lysosomal alpha-L-iduronidase deficiency and excessive urinary dermatan and heparan sulfate excretion. The corneal button after perforating keratoplasty of the right eye demonstrated mucopolysaccharides consisting of numerous vacuoles containing fibrillogranular and partly membranebound material in epithelial cells, histiocytes, keratocytes, and extracellular matrix. Endothelial cells were distinctly free of storage material. The epithelial basement membrane showed frequent breaks, whereas Bowman's layer was only slightly attenuated. Irregular collagen fibrils and fibrous long-spacing collagen were noted near degenerating distended keratocytes. The Descemet's membrane was normal. The literature of six reported histopathological examinations of the cornea in Scheie's syndrome is reviewed. Detection of fibrous long-spacing collagen seems to be a typical abnormality of the cornea in MPS I-S.
Adult↗
Senior management must champion business intelligence programs.
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Administrative Personnel↗