ReviewJournal of medical Internet research2017
A Learning Health Care System Using Computer-Aided Diagnosis.
Review in Journal of medical Internet research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
19 citing papers in PubMed.
- Evaluation of a Medical Interview-Assistance System Using Artificial Intelligence for Resident Physicians Interviewing Simulated Patients: A Crossover, Randomized, Controlled Trial.International journal of environmental research and public health · 2023Trial
- Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study.International journal of environmental research and public health · 2021Trial
- Application of Artificial Intelligence in Inborn Errors of Immunity Identification and Management: Past, Present, and Future-A Systematic Review.Journal of clinical medicine · 2025Review
- Generalizing through Forgetting - Domain Generalization for Symptom Event Extraction in Clinical Notes.AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science · 2023Article
- Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Article
- The Application of Projection Word Embeddings on Medical Records Scoring System.Healthcare (Basel, Switzerland) · 2021Article
- Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM.Sensors (Basel, Switzerland) · 2021Article
- Status and Recommendations of Technological and Data-Driven Innovations in Cancer Care: Focus Group Study.Journal of medical Internet research · 2020Article
- Recording COVID-19 consultations: review of symptoms, risk factors, and proposed SNOMED CT terms.BJGP open · 2020Article
- Differential Diagnosis Assessment in Ambulatory Care With an Automated Medical History-Taking Device: Pilot Randomized Controlled Trial.JMIR medical informatics · 2019Article
- Vision for the future on urolithiasis: research, management, education and training-some personal views.Urolithiasis · 2019Review
- Artificial intelligence in reproductive medicine.Reproduction (Cambridge, England) · 2019Review
- Machine learning to refine decision making within a syndromic surveillance service.BMC public health · 2019Article
- Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study.Orphanet journal of rare diseases · 2019Article
- Calculation of a Primary Immunodeficiency "Risk Vital Sign" via Population-Wide Analysis of Claims Data to Aid in Clinical Decision Support.Frontiers in pediatrics · 2019Article
- Artificial intelligence in radiology.Nature reviews. Cancer · 2018Review
- Possible Sources of Bias in Primary Care Electronic Health Record Data Use and Reuse.Journal of medical Internet research · 2018Article
- Failure to shorten the diagnostic delay in two ultra-orphan diseases (mucopolysaccharidosis types I and III): potential causes and implications.Orphanet journal of rare diseases · 2018Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Physicians intuitively apply pattern recognition when evaluating a patient. Rational diagnosis making requires that clinical patterns be put in the context of disease prior probability, yet physicians often exhibit flawed probabilistic reasoning. Difficulties in making a diagnosis are reflected in the high rates of deadly and costly diagnostic errors. Introduced 6 decades ago, computerized diagnosis support systems are still not widely used by internists. These systems cannot efficiently recognize patterns and are unable to consider the base rate of potential diagnoses. We review the limitations of current computer-aided diagnosis support systems. We then portray future diagnosis support systems and provide a conceptual framework for their development. We argue for capturing physician knowledge using a novel knowledge representation model of the clinical picture. This model (based on structured patient presentation patterns) holds not only symptoms and signs but also their temporal and semantic interrelations. We call for the collection of crowdsourced, automatically deidentified, structured patient patterns as means to support distributed knowledge accumulation and maintenance. In this approach, each structured patient pattern adds to a self-growing and -maintaining knowledge base, sharing the experience of physicians worldwide. Besides supporting diagnosis by relating the symptoms and signs with the final diagnosis recorded, the collective pattern map can also provide disease base-rate estimates and real-time surveillance for early detection of outbreaks. We explain how health care in resource-limited settings can benefit from using this approach and how it can be applied to provide feedback-rich medical education for both students and practitioners.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.