ArticleCureus2025
Real-World Application of an AI-Assisted Digital Workflow for Clinical Observational Data Collection in Dermatology Residency Programs: An Innovative Pilot Report From India.
Article in Cureus, 2025. 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
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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
1 citing paper in PubMed.
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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
introductionResidency research is a cornerstone of academic dermatology training, yet paper-based data collection remains labor-intensive, error-prone, and a major source of stress for trainees. Electronic data capture (EDC) platforms can mitigate these issues but are often costly and difficult to implement in resource-limited settings. We evaluated a low-cost, artificial intelligence (AI)-assisted workflow leveraging widely available Google Workspace tools to streamline data collection and enhance data integrity during dermatology residency research.
methodsIn this prospective, single-center simulation study, 100 hypothetical patient records with 41 variables each (total 4,100 fields) were independently entered using (i) conventional paper-based data collection with subsequent manual digitization, and (ii) a digital workflow using Google Forms, automated coding in Google Sheets, PDF generation, and real-time AI-assisted validation via ChatGPT. Mean entry time per record, error rates per field, and absolute risk reduction were calculated. Paired t-tests, McNemar's test with continuity correction, exact binomial p-values, and Newcombe 95% confidence intervals were used for analysis.
resultsThe AI-assisted workflow reduced mean entry time from 24.4 ± 1.5 minutes to 7.5 ± 1.1 minutes per record (mean paired difference=16.9 minutes, 95% CI: 16.61-17.19, p<0.001, Cohen's d=11.66). Error rates decreased from 8.54% (350/4,100) to 2.39% (98/4,100), yielding an absolute risk reduction of 6.15 percentage points (95% CI: 4.82-7.47) and a 72% relative reduction in errors (McNemar's χ²(1)=6.13, p=0.013; exact binomial p=0.0078). No cases demonstrated new errors unique to the digital workflow.
conclusionAn AI-assisted, Google Workspace-based workflow significantly reduced both time and error rates in simulated dermatology research data collection. This approach is low-cost, rapidly deployable, and scalable to resource-constrained academic centers. Adoption of such workflows has the potential to improve research efficiency, enhance data integrity, and reduce resident stress, ultimately fostering a stronger research culture in dermatology training programs.
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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.