ReviewDermatopathology (Basel, Switzerland)2021
Artificial Intelligence in Dermatopathology: New Insights and Perspectives.
Review in Dermatopathology (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed.
- An artificial intelligence model of whole-slide pathology specimens differentiating cutaneous high-grade squamous proliferations.Virchows Archiv : an international journal of pathology · 2025Article
- Current Trends and Future Directions of Digital Pathology and Artificial Intelligence in Dermatopathology: A Scientometric-Based Review.Diagnostics (Basel, Switzerland) · 2025Review
- Congenital and Neonatal Skin Disorders: Histopathological Diagnosis and Syndromic Associations.Cureus · 2025Review
- Enhanced melanoma and non-melanoma skin cancer classification using a hybrid LSTM-CNN model.Scientific reports · 2025Article
- Artificial Intelligence in the Histopathological Assessment of Non-Neoplastic Skin Disorders: A Narrative Review with Future Perspectives.Medical sciences (Basel, Switzerland) · 2025Review
- A Novel Automatic Algorithm to Support Lung Ultrasound Non-Expert Physicians in Interstitial Pneumonia Evaluation: A Single-Center Study.Diagnostics (Basel, Switzerland) · 2024Article
- A Spitzoid Tumor dataset with clinical metadata and Whole Slide Images for Deep Learning models.Scientific data · 2023Article
- Paediatric Spitzoid Neoplasms: 10-Year Retrospective Study Characterizing Histological, Clinical, Dermoscopic Presentation and FISH Test Results.Diagnostics (Basel, Switzerland) · 2023Article
- Artificial Intelligence Applied to a First Screening of Naevoid Melanoma: A New Use of Fast Random Forest Algorithm in Dermatopathology.Current oncology (Toronto, Ont.) · 2023Article
- Machine learning applied to MRI evaluation for the detection of lymph node metastasis in patients with locally advanced cervical cancer treated with neoadjuvant chemotherapy.Archives of gynecology and obstetrics · 2023Article
- Review
- A machine learning approach applied to gynecological ultrasound to predict progression-free survival in ovarian cancer patients.Archives of gynecology and obstetrics · 2022Observational
- Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network.Diagnostics (Basel, Switzerland) · 2022Article
- Improving the Diagnosis of Skin Biopsies Using Tissue Segmentation.Diagnostics (Basel, Switzerland) · 2022Article
- An Audit and Survey of Informal Use of Instant Messaging for Dermatology in District Hospitals in KwaZulu-Natal, South Africa.International journal of environmental research and public health · 2022Article
- A Novel Hybrid Deep Learning Approach for Skin Lesion Segmentation and Classification.Journal of healthcare engineering · 2022Article
- Validity of Machine Learning in Predicting Giant Cell Arteritis Flare After Glucocorticoids Tapering.Frontiers in immunology · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
17 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, an increasing enthusiasm has been observed towards artificial intelligence and machine learning, involving different areas of medicine. Among these, although still in the embryonic stage, the dermatopathological field has also been partially involved, with the attempt to develop and train algorithms that could assist the pathologist in the differential diagnosis of complex melanocytic lesions. In this article, we face this new challenge of the modern era, carry out a review of the literature regarding the state of the art and try to determine promising future perspectives.
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.