ReviewDer Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete2020
[Artificial intelligence and smartphone program applications (Apps) : Relevance for dermatological practice].
Review in Der Hautarzt; Zeitschrift fur Dermatologie, Venerologie, und verwandte Gebiete, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 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
7 citing papers in PubMed.
- Chatbots for future docs: exploring medical students' attitudes and knowledge towards artificial intelligence and medical chatbots.Medical education online · 2023Article
- Advancing Dermatological Care: A Comprehensive Narrative Review of Tele-Dermatology and mHealth for Bridging Gaps and Expanding Opportunities beyond the COVID-19 Pandemic.Healthcare (Basel, Switzerland) · 2023Review
- The Artificial Intelligence in Teledermatology: A Narrative Review on Opportunities, Perspectives, and Bottlenecks.International journal of environmental research and public health · 2023Review
- Article
- Teledermoscopy in the Diagnosis of Melanocytic and Non-Melanocytic Skin Lesions: NurugoDiagnostics (Basel, Switzerland) · 2022Article
- The use and future perspective of Artificial Intelligence-A survey among German surgeons.Frontiers in public health · 2022Article
- Teledermatology During COVID-19: An Updated Review.American journal of clinical dermatology · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
ADVANTAGES OF ARTIFICIAL INTELLIGENCE (AI): With responsible, safe and successful use of artificial intelligence (AI), possible advantages in the field of dermato-oncology include the following: (1) medical work can focus on skin cancer patients, (2) patients can be more quickly and effectively treated despite the increasing incidence of skin cancer and the decreasing number of actively working dermatologists and (3) users can learn from the AI results. POTENTIAL DISADVANTAGES AND RISKS OF AI USE: (1) Lack of mutual trust can develop due to the decreased patient-physician contact, (2) additional time effort will be necessary to promptly evaluate the AI-classified benign lesions, (3) lack of adequate medical experience to recognize misclassified AI decisions and (4) recontacting a patient in due time in the case of incorrect AI classifications. Still problematic in the use of AI are the medicolegal situation and remuneration. Apps using AI currently cannot provide sufficient assistance based on clinical images of skin cancer. REQUIREMENTS AND POSSIBLE USE OF SMARTPHONE PROGRAM APPLICATIONS: Smartphone program applications (apps) can be implemented responsibly when the image quality is good, the patient's history can be entered easily, transmission of the image and results are assured and medicolegal aspects as well as remuneration are clarified. Apps can be used for disease-specific information material and can optimize patient care by using teledermatology.
Indexed as
Identifiers
32720165What 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.