ReviewFrontiers in medicine2025
Navigating the AI tide: challenges, opportunities, and future directions for early-career dermatologists.
Review in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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
2 citing papers in PubMed.
- Current Status and Future Perspectives of Wearable Technologies for Oral Health in Clinical Applications.Diagnostics (Basel, Switzerland) · 2026Review
- Integrating Artificial Intelligence into Psychological Counseling: A Narrative Review and Governance Framework.Inquiry : a journal of medical care organization, provision and financingReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) has demonstrated diagnostic accuracy comparable to dermatologists in specific tasks (e.g., 92.5% vs. 86.6% for melanoma detection in multicenter trials), while significantly outperforming early-career physicians (15-20% higher accuracy in meta-analyses). This review synthesizes evidence on AI's transformative impact on dermatology training and practice, addressing critical gaps in ethical frameworks and implementation strategies. We propose a competency-based framework for "AI-augmented dermatology," advocating for curriculum integration of AI literacy modules, standardized human-AI workflows, and proactive engagement in regulatory processes. Early-career dermatologists must leverage AI as a safety net while strengthening irreplaceable skills in complex decision-making and patient communication to lead dermatology's AI-integrated future.
Indexed as
Identifiers
What OpenQuestion holds
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.