AIMC Topic: Dermatology

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Artificial Intelligence in Patch Testing: Comprehensive Review of Current Applications and Future Prospects in Dermatology.

JMIR dermatology
BACKGROUND: The integration of artificial intelligence (AI) into patch testing for allergic contact dermatitis (ACD) holds the potential to standardize diagnoses, reduce interobserver variability, and improve overall diagnostic accuracy. However, the...

Closing the AI generalisation gap by adjusting for dermatology condition distribution differences across clinical settings.

EBioMedicine
BACKGROUND: Generalisation of artificial intelligence (AI) models to a new setting is challenging. In this study, we seek to understand the robustness of a dermatology (AI) model and whether it generalises from telemedicine cases to a new setting inc...

Generating dermatopathology reports from gigapixel whole slide images with HistoGPT.

Nature communications
Histopathology is the reference standard for diagnosing the presence and nature of many diseases, including cancer. However, analyzing tissue samples under a microscope and summarizing the findings in a comprehensive pathology report is time-consumin...

Evaluating medical AI systems in dermatology under uncertain ground truth.

Medical image analysis
For safety, medical AI systems undergo thorough evaluations before deployment, validating their predictions against a ground truth which is assumed to be fixed and certain. However, in medical applications, this ground truth is often curated in the f...

A Systemic Review of Large Language Models and Their Implications in Dermatology.

The Australasian journal of dermatology
In computational linguistics, large language models have reached a significant turning point. They have quickly spread throughout several sectors, including the medical field. By integrating demographics, clinical photos, medical interviews, or genet...

Predictive modeling and optimization in dermatology: Machine learning for skin disease classification.

Computers in biology and medicine
The accurate diagnosis of skin diseases is crucial for effective patient management and treatment, yet traditional diagnostic methods often involve subjective interpretation and can lead to variability in outcomes. In this study, we harness the power...

The good, the bad, and the ugly: Ethical considerations regarding artificial intelligence assistance in administrative physician tasks.

Clinics in dermatology
Artificial intelligence is a powerful tool that can potentially transform the diagnostic, therapeutic, and administrative practice of dermatology. Physicians are expected to complete electronic health record documentation in a timely fashion, prepare...

Multi-task AI models in dermatology: Overcoming critical clinical translation challenges for enhanced skin lesion diagnosis.

Journal of the European Academy of Dermatology and Venereology : JEADV
BACKGROUND: The surge in AI models for diagnosing skin lesions through image analysis is notable, yet their clinical implementation faces challenges. Common limitations include an over reliance on dermoscopy, lack of real-world applicability when onl...

Identifying Research Hotspots and Trends in Psoriasis Literature: Autotuned Topic Modeling with Agent.

The Journal of investigative dermatology
The rapid expansion of psoriasis research presents challenges in efficient analysis and trend identification, necessitating advanced approaches. We propose AgenTopic, an interactive topic modeling framework that integrates Bidirectional Encoder Repre...

The use of a ChatGPT-4-based chatbot in teledermatology: A retrospective exploratory study.

Journal der Deutschen Dermatologischen Gesellschaft = Journal of the German Society of Dermatology : JDDG
BACKGROUND AND OBJECTIVES: Integration of artificial intelligence in healthcare, particularly ChatGPT, is transforming medical diagnostics and may benefit teledermatology. This exploratory study compared image description and differential diagnosis g...