ReviewDiagnostics (Basel, Switzerland)2024
Artificial Intelligence in Cutaneous Leishmaniasis Diagnosis: Current Developments and Future Perspectives.
Review in Diagnostics (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers, 1 of them a synthesis that pooled it.
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
8 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Human Leishmaniasis in Algeria: A Systematic Review and Meta-Analysis.Acta parasitologica · 2025Pooled it
- Feasibility study of an AI-powered mobile app to support cutaneous leishmaniasis diagnosis in the Brazilian Amazon.PLoS neglected tropical diseases · 2026Article
- Decision-support system for live detection of Leishmania parasites from microscopic images with deep learning.BMC infectious diseases · 2026Article
- From dogma to data: charting a path forward for clinico-immunological research in Ethiopian cutaneous leishmaniasis.Infectious diseases of poverty · 2026Review
- Leishmaniasis in Morocco: Epidemiology, Transmission Dynamics, and the Potential of Artificial Intelligence for Disease Management.Journal of parasitology research · 2026Review
- A lightweight deep learning framework for reliable microscopy-based diagnosis of cutaneous leishmaniasis.PloS one · 2026Article
- Enhancing Automated Breast Cancer Detection: A CNN-Driven Method for Multi-Modal Imaging Techniques.Journal of personalized medicine · 2025Article
- Cutaneous Leishmaniasis in the Context of Global Travel, Migration, Refugee Populations, and Humanitarian Crises.Clinics and practice · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
Cutaneous Leishmaniasis (CL) is a major global health problem requiring appropriate diagnosis methods. Its diagnosis is challenging, particularly in resource-limited settings. The integration of Artificial Intelligence (AI) into medical diagnostics has shown promising results in various fields, including dermatology. In this systematic review, we aim to highlight the value of using AI for CL diagnosis and the AI-based algorithms that are employed in this process, and to identify gaps that need to be addressed. Our work highlights that only a limited number of studies are related to using AI algorithms for CL diagnosis. Among these studies, seven gaps were identified for future research. Addressing these considerations will pave the way for the development of robust AI systems and encourage more research in CL detection by AI. This could contribute to improving CL diagnosis and, ultimately, healthcare outcomes in CL-endemic regions.
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.