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Results for “Decision Making, Computer-Assisted”

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PubMedDate not supplied

Clinical patient management and the integrated health information system.

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.

Computers↗
PubMed1975

A computer-assisted instruction course in the diagnosis and treatment of respiratory diseases.

A computer-assisted simulation of the 'chart method' of teaching has been developed and was used to provide instruction in clinical decision-making in the diagonosis and treatment of pulmonary diseases. The course requires a student to reach and to defend a diagonisis and to provide appropriate treatment for each of 10 simulated cases. Evaluation of performance and immediate feedback and correction of errors of commission and ommission are an integral part of the course. The course provides a model for development of additional programs in other subject areas.

Curriculum↗
PubMed1976

Computer-assisted learning in undergraduate medical teaching.

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.

Adult↗
PubMed1978

Attitudes of medical undergraduates in Glasgow to computer-assisted learning.

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.

Attitude of Health Personnel↗
PubMed2026

Artificial Intelligence and Machine Learning Applications in Fibromuscular Dysplasia: Transforming Diagnosis, Risk Stratification, and Clinical Decision-Making.

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.

Humans↗
PubMedDate not supplied

A computer-aided system for the diagnosis of hepato-biliary diseases. A comparison with the performance of physicians.

A computer-aided system has been developed for the diagnosis of disease of the liver and biliary system. The program is based on the use of 30 indicants, all of which are available within six hours after patient's admission: 18 of them are clinical signs, 12 are laboratory parameters including routine liver-function tests. To date, the program concerns 52 hepato-biliary diseases. From the analysis of the 30 items collected in any patient, the diagnoses are computed according to Bayes' theorem and printed by decreasing order of probability. The performance of the program was tested using records of patients with fully proven diagnosis. The first diagnosis given by the computer was correct in 57 per cent of the cases. In 80 per cent, the right diagnosis was among the first four proposed. When the performance of the model was compared to that of physicians, the number of correct answers was roughly the same for the computer and for the specialists in hepatology; in contrast, the computer's responses were far better than those of general practitioners. These results demonstrate the efficiency of the program for the diagnosis of hepato-bilitary diseases and its potential interest for helping clinicians in decision-making.

Adult↗
PubMed1979

Quality assessment and the art of medicine: the anatomy of laceration care.

Assuring high quality medical care has remained an elusive goal because of several problems which have hampered development of effective medical audit programs: inadequate patient data, unreasonable evaluative criteria and insensitive audit procedures. The present study demonstrates the use of a clinical algorithm to help overcome these problems. An examination of medical record data from a series of 703 laceration patients treated in an emergency service yielded only 27 cases (4 per cent) with medical records sufficiently complete to use for auditing physician compliance with algorithmic criteria. Substituting a structured checklist for the handwritten note increased this rate to 86 per cent. A computer-assisted branching audit of 1,400 laceration cases demonstrated that 1) physician compliance with an algorithmic instruction varied significantly (p less than .001) according to the specific instruction, and 2) compliance with a given instruction varied significantly (p less than .001) across different providers. These results underscore the need for medical audit with educational feedback which is provider specific.

Connecticut↗
PubMed1976

[User-oriented programs for semi-automated evaluation of radio in vitro tests].

Most of the programmes for the evaluation of radio in vitro tests proceed from the anticipation that the best method should give an approximation of standard values by a curve as perfect as possible. According to our experiences this demand, however, is not decisive for a good standard curve, as in principle all standard values can be incorrect. The application of relatively simple linearising transformations and an additional curve (recovery of a normal serum) guarantees the necessary precision in the programme described. After a short description of the contents of the system, the programme for the assessment of CPBA - methods is shown, by help of which the sample changer - calculator-system determines the absolute concentration of the substance to be measured. This makes the starting point for the more complex RIA programme which methodologically takes into consideration the special problems of these tests.

Diagnosis, Computer-Assisted↗
PubMed2026

A multi-scale fusion model based on multi-phase contrast-enhanced CT for predicting pancreatic cancer resectability.

Purpose.Develop a multi-scale fusion model (MSFM) based on multi-phase contrast-enhanced computed tomography (CECT) to predict pancreatic cancer (PC) resectability, thereby assisting expert decision-making.Methods.This retrospective study enrolled 280 patients with PC from four institutions, which were randomly divided into a training cohort (202 patients) and an independent test cohort (78 patients). Three-phase CECT images (arterial, venous, and delayed phases) were used for modeling. The MSFM comprises two sub-networks: (1) a multi-phase fusion network for extracting cross-phase shared fusion features, (2) a phase-specific branch network for capturing phase-specific features; and a post-fusion strategy to generate the final predictive score by integrating the shared fusion features and three groups of phase-specific features. Additionally, a human-machine fusion deep learning model (HMfDL) was constructed by fusing the predictive score of the MSFM with expert assessments.Results.In the independent test, the MSFM achieved an AUC (area under the receiver operating characteristic curve) of 0.8385 (95% CI: 0.7521-0.9249), accuracy of 84.62%, sensitivity of 72.00%, and specificity of 90.57%. This performance outperformed single-phase models (AUC range: 0.7638-0.7781), two-phase models (AUC range: 0.7826-0.7864), and ten states-of-the-art classifiers (AUC range: 0.7404-0.7796). The HMfDL further improved the performance, reaching an AUC of 0.8626 (95% CI: 0.7853-0.9400), accuracy of 91.03%, sensitivity of 80.00%, and specificity of 96.23%. Notably, the HMfDL corrected 58.82% of misdiagnosis made by experts.Conclusions. The MSFM effectively fuses multi-phase CECT to enable highly accurate predictions of PC resectability, and provides valuable support for expert decision-making through HMfDL.

Humans↗
PubMedDate not supplied

Towards computer analysis of pulmonary infiltration.

A feasibility study is described to provide quantitative texture measures to distinguish between normal lung, alveolar infiltrates and interstitial infiltrates. Advanced computer imaging technology and decision making processes were applied to distinguish between these textural patterns. The results, based on computer extracted quantitative measures, show an excellent separation of the three classes considered with 95% accuracy in the training phase and 90% accuracy in the testing phase.

Diagnosis, Computer-Assisted↗
PubMed1979

An approach to quality and performance control in a computer-assisted clinical chemistry laboratory.

A locally developed, computer-based clinical chemistry laboratory system has been in operation since 1970. This utilises a Digital Equipment Co Ltd PDP 12 and an interconnected PDP 8/F computer. Details are presented of the performance and quality control techniques incorporated into the system. Laboratory performance is assessed through analysis of results from fixed-level control sera as well as from cumulative sum methods. At a simple level the presentation may be considered purely indicative, while at a more sophisticated level statistical concepts have been introduced to aid the laboratory controller in decision-making processes.

Chemistry, Clinical↗
PubMed2026

Automated Classification of Lymphoma Subtypes From Histopathological Images Using a U-Net Deep Learning Model: Comparative Evaluation Study.

BACKGROUND: Accurate classification and grading of lymphoma subtypes are essential for treatment planning. Traditional diagnostic methods face challenges of subjectivity and inefficiency, highlighting the need for automated solutions based on deep learning techniques. OBJECTIVE: This study aimed to investigate the application of deep learning technology, specifically the U-Net model, in classifying and grading lymphoma subtypes to enhance diagnostic precision and efficiency. METHODS: In this study, the U-Net model was used as the primary tool for image segmentation integrated with attention mechanisms and residual networks for feature extraction and classification. A total of 620 high-quality histopathological images representing 3 major lymphoma subtypes were collected from The Cancer Genome Atlas and the Cancer Imaging Archive. All images underwent standardized preprocessing, including Gaussian filtering for noise reduction, histogram equalization, and normalization. Data augmentation techniques such as rotation, flipping, and scaling were applied to improve the model's generalization capability. The dataset was divided into training (70%), validation (15%), and test (15%) subsets. Five-fold cross-validation was used to assess model robustness. Performance was benchmarked against mainstream convolutional neural network architectures, including fully convolutional network, SegNet, and DeepLabv3+. RESULTS: The U-Net model achieved high segmentation accuracy, effectively delineating lesion regions and improving the quality of input for classification and grading. The incorporation of attention mechanisms further improved the model's ability to extract key features, whereas the residual structure of the residual network enhanced classification accuracy for complex images. In the test set (N=1250), the proposed fusion model achieved an accuracy of 92% (1150/1250), a sensitivity of 91.04% (1138/1250), a specificity of 89.04% (1113/1250), and an F1-score of 90% (1125/1250) for the classification of the 3 lymphoma subtypes, with an area under the receiver operating characteristic curve of 0.95 (95% CI 0.93-0.97). The high sensitivity and specificity of the model indicate strong clinical applicability, particularly as an assistive diagnostic tool. CONCLUSIONS: Deep learning techniques based on the U-Net architecture offer considerable advantages in the automated classification and grading of lymphoma subtypes. The proposed model significantly improved diagnostic accuracy and accelerated pathological evaluation, providing efficient and precise support for clinical decision-making. Future work may focus on enhancing model robustness through integration with advanced algorithms and validating performance across multicenter clinical datasets. The model also holds promise for deployment in digital pathology platforms and artificial intelligence-assisted diagnostic workflows, improving screening efficiency and promoting consistency in pathological classification.

Humans↗