ArticleDigital health
Effectiveness of physician-based diagnosis versus diagnostic artificial intelligence algorithms in detecting communicable febrile diseases in Mexico.
Article in Digital health. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
1 citing paper in PubMed.
- [Medical Practice, Epidemiology, and Evidence-Based Medicine: A necessary triangle in healthcare].Revista medica del Instituto Mexicano del Seguro Social · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Digital medicine is an important tool in the current healthcare landscape. Fever is an important reason for evaluating patients at first and second levels of care and a frequent symptom of diseases subject to epidemiological surveillance. Objective: To evaluate the diagnostic effectiveness of various algorithms in detecting communicable diseases of epidemiological interest in febrile patients at Hospital General Regional No. 1, Cd. Obregón, Sonora. Methods: An observational, descriptive, and retrospective study was conducted in a second-level hospital from 1 January 2022 to 31 December 2023, to determine Cohen's kappa and the sensitivity, specificity, positive and negative predictive values, precision and Youden's J index of diagnostic algorithms for 20 communicable diseases with respect to the doctors' diagnoses. Results: Diagnostic algorithms were applied to the data of 909 cases. The sensitivities of Mediktor®, an artificial neural network-based algorithm, a medical diagnostic algorithm and a composite diagnostic algorithm were 11.97%, 64.09%, 69.92% and 99.37%, respectively, and the corresponding specificities were 93.43%, 91.24%, 27.01% and 5.11%, respectively. The neural network-based method yielded the highest Youden's J index. Conclusions: The medical diagnostic algorithm had the best sensitivity, whereas the specificity was greater for the two artificial intelligence algorithms.
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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.