A Review of Computer Assistance in Dermatology

Reshma, Shaik; Reeja, S R

doi:10.1109/iitcee57236.2023.10090919

Cited by 4 publications

(1 citation statement)

References 23 publications

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“…ment of various tools and techniques to aid dermatologists in their diagnosis [13], [14], [15]. For example, the development of artificial intelligence tools to aid in the diagnosis of skin disorders from images has been made possible by recent advancements in deep learning [16], [17], such as skin cancer classification [18], [19], [20], [21], [22], [23], [24], [25], [26], [27], dermatopathology [28], [29], [30], predicting novel risk factors or epidemiology [31], [32], identifying onychomycosis [33], quantifying alopecia areata [34], classify skin lesions from mpox virus infection [35], and so on [4].…”

Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4

Zhou

Sun

et al. 2023

Preprint

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Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population. Nonetheless, the field of dermatology diagnosis faces three significant hurdles. Firstly, there is a shortage of dermatologists accessible to diagnose patients, particularly in rural regions. Secondly, accurately interpreting skin disease images poses a considerable challenge. Lastly, generating patient-friendly diagnostic reports is usually a time-consuming and labor-intensive task for dermatologists. To tackle these challenges, we present SkinGPT-4, which is the world's first interactive dermatology diagnostic system powered by an advanced visual large language model. SkinGPT-4 leverages a fine-tuned version of MiniGPT-4, trained on an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors' notes. We designed a two-step training process to allow SkinGPT to express medical features in skin disease images with natural language and make accurate diagnoses of the types of skin diseases. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identifies the characteristics and categories of the skin conditions, performs in-depth analysis, and provides interactive treatment recommendations. Meanwhile, SkinGPT-4's local deployment capability and commitment to user privacy also render it an appealing choice for patients in search of a dependable and precise diagnosis of their skin ailments. To demonstrate the robustness of SkinGPT-4, we conducted quantitative evaluations on 150 real-life cases, which were independently reviewed by certified dermatologists, and showed that SkinGPT-4 could provide accurate diagnoses of skin diseases. Though SkinGPT-4 is not a substitute for doctors, it could enhance users' comprehension of their medical conditions, facilitate improve communication between patients and doctors, expedite the diagnostic process for dermatologists, and potentially promote human-centred care and healthcare equity in underdeveloped areas.

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Section: (Which Was Not Certified By Peer Review)mentioning

confidence: 99%

Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4

Zhou

Sun

et al. 2023

Preprint

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Cyber Sentinels: Illuminating Malicious Intent in Social Networks Using Dual-Powered CHAM

Terumalasetti,

Reeja

2024

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4

Zhou,

He,

Sun

et al. 2024

Nat Commun

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Large language models (LLMs) are seen to have tremendous potential in advancing medical diagnosis recently, particularly in dermatological diagnosis, which is a very important task as skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases. Here we present SkinGPT-4, which is an interactive dermatology diagnostic system based on multimodal large language models. We have aligned a pre-trained vision transformer with an LLM named Llama-2-13b-chat by collecting an extensive collection of skin disease images (comprising 52,929 publicly available and proprietary images) along with clinical concepts and doctors’ notes, and designing a two-step training strategy. We have quantitatively evaluated SkinGPT-4 on 150 real-life cases with board-certified dermatologists. With SkinGPT-4, users could upload their own skin photos for diagnosis, and the system could autonomously evaluate the images, identify the characteristics and categories of the skin conditions, perform in-depth analysis, and provide interactive treatment recommendations.

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A Review of Computer Assistance in Dermatology

Cited by 4 publications

References 23 publications

Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4

Pre-trained Multimodal Large Language Model Enhances Dermatological Diagnosis using SkinGPT-4

Cyber Sentinels: Illuminating Malicious Intent in Social Networks Using Dual-Powered CHAM

Pre-trained multimodal large language model enhances dermatological diagnosis using SkinGPT-4

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