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Undergraduate medical students improve their ability to make patient management decisions through using computer-assisted learning. With this problem-solving approach an improvement in their factural knowledge also occurs and they retain a significant amount of the content.
Radiotherapy departments are becoming sophisticated in working with computers for isodose computations, treatment machine verifications and administrative and medical records. The next step lies in computer-assisted medical decision making. The logic for a patient's diagnostic work-up and treatment protocol can be stored in a computer. It can then be used as an aid in making the diagnosis, in prescribing the treatment and for quality control. For patients who fit established protocols the computer can select and list treatment using the logic of that protocol. Such a system has been implemented for the postoperative radiotherapy of breast cancer on a trial basis. Its potential usefulness is illustrated by results in 25 consecutive patients. Physician acceptance and costs of the program are under investigation.
This article describes a technique for analyzing clinical data in order to guide patient management decisions. The technique is illustrated by considering a specific decision problem encountered in the management of possible meningitis, namely, whether or not to administer antibiotics while awaiting the results of a CSF bacterial culture. Data from 303 patients with meningitis are analyzed in order to determine which combination of clinical variables best discriminates between bacterial and aseptic cases. From these variables, a probability tree is constructed that indicates the probability of bacterial meningitis, depending on a patient's clinical characteristics. In addition to identifying the most important variables, the analysis reveals that a number of tests performed routinely on patients with meningitis are of questionable diagnostic value.
The performance of a computer-based clinical consultation system is evaluated. The program, called MYCIN, is designed to function as an aid for infectious disease diagnosis and therapy selection, with an initial emphasis on bacteremias. The evaluation methodology is discussed, as well as the difficulties encountered in attempting to evaluate clinical judgments. Specialists in infectious diseases judged MYCIN's final therapy recommedation, and intermediate conclusions about the significance of the infection and identity of infecting organisms. The evaluation techniques described may be useful in assessing the performance of other clinical decision aids. Results of the evaluation show that the program's therapy recommedations meet Stanford experts' standards of acceptable practice 90.9% of the time (table 2), with some variation noted both among individual experts and between Stanford experts and others (tables 1, 2).
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Four radiologists, three of whom having no special expertise in bone tumor radiology, analysed 177 bone tumors. One of the radiologists, using a computer aided bone tumor program, performed significantly better than the other two at a comparable level of training and was able to compete successfully with the fourth radiologist experienced in bone diagnosis. The results validate the assumption that computer aided diagnostic programs may improve the diagnostic accuracy of radiologists having limited experience with the problem at hand.
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To assist clinical assessment and decision making, a computer-based system has been developed to organize key data of individual medical records and display these data in a standard graphic format for each disease. A program for multiple myeloma has been completed and 150 case records entered. The data accessed by computer-based case tracing on all cases of a particular disease are available for cumulative and comparative analyses of different therapeutic regimens.
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The benefits and capabilities of an automated medical signal analysis system that can lead to more effective patient care are identified, the capabilities of different types of systems are briefly mentioned, and automated systems that support nominative and managerial decisions are described. The need for the practicing physician to anticipate computer hardware limitations and potential errors in programming are briefly discussed.
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Emerging progress in clinical applications of patient care computing is identified. The essential clinical skill is understanding what data are appropriate in any given patient care situation and extracting enough information to make the correct management decision. The value of the computer has less to do with the internal intellectual process of diagnosis than its contribution to the more manifest actions in support of clinical patient management. Techniques with which the computer is assisting in improving the clinical decisionmaking process are reviewed, and a mechanism to link them to active patient care settings is described. In addition, a trend toward the integration of various independent subsystems, so that expensive resources can be optimized for patient needs, is noted.
A programme of computer-assisted learning has been introduced for fifth-year medical students at Glasgow University during the teaching course in general practice. The programme allows students to make decisions on all aspects of patient care, and has the potential for combining a learning situation with an objective evaluation of skills and attitudes. The programme is popular with students and could have considerable potential in medical education.
Computer-assisted learning (CAL) has been introduced as part of the undergraduate teaching course in general practice during the penultimate year of the medical course. The student is given an opportunity to make clinical decisions and to manage a case over a significant time scale. The attitudes of the students are favourable to this method of instruction.
Fibromuscular dysplasia (FMD) is a non-atherosclerotic vascular disorder with heterogeneous presentations, making diagnosis and management highly dependent on imaging and clinical expertise. This narrative review examines how artificial intelligence (AI) and machine learning (ML) are transforming FMD care. AI-enhanced imaging, particularly convolutional neural network-based analysis, improves detection of the characteristic "string-of-beads" pattern on CT angiography, magnetic resonance angiography, and ultrasound, although FMD-specific validation remains limited. ML models facilitate risk stratification, prediction of disease progression, and early identification of complications such as aneurysms and stroke by integrating clinical, imaging, and genomic data. AI-driven clinical decision support systems further enable personalized treatment selection through pharmacogenomic insights and robot-assisted interventions. Despite promising real-world applications, challenges persist, including limited large-scale datasets, workflow integration, regulatory barriers, and algorithmic bias affecting underrepresented populations. Future advances in explainable AI, federated learning, and digital health integration may enable a shift toward predictive, patient-centered FMD management.