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As a Saudi university, it uses AI to transform the diagnosis and treatment of skin diseases

RIYADH: To increase the effectiveness and efficiency of dermatological care, experts from Saudi Arabia’s King Abdullah University of Science and Technology have created a groundbreaking new diagnostic system, SkinGPT-4, which uses the power of artificial intelligence.

Lead researcher Xin Gao, professor of computer science, co-chair of the Center of Excellence for Smart Health and chair of the Bioinformatics Platform at KAUST, says the goal of SkinGPT-4 is to detect, diagnose and identify appropriate skin treatments. diseases.

Developed in collaboration with Juexiao Zhou, a doctoral candidate at KAUST who is the first author of SkinGPT-4, Gao says the technology could provide potentially life-saving services to patients, especially those in rural areas where there is often shortage. trained dermatologists.

“These specific challenges in dermatology led to the creation of SkinGPT-4,” Gao told Arab News. “The variability in skin presentations and the need for specialized knowledge to correctly identify and treat these conditions highlighted the need for an advanced AI-based solution.”

The team identified the need for such a solution after realizing the limitations of traditional diagnostic methods and the potential of AI, especially large language models (LLMs) such as the eponymous ChatGPT, in increasing the accuracy and efficiency of dermatological diagnostics.

“With SkinGPT-4, users could upload their own skin photos for diagnosis, and SkinGPT-4 could autonomously determine the characteristics and categories of skin conditions, perform analysis, provide treatment recommendations, and enable interactive diagnosis,” Gao said.

Gao and the KAUST team behind the AI ​​Dermatologist SkinGPT-4. (Supplied)

SkinGPT-4 diagnoses conditions that have distinct visual characteristics such as acne, rosacea, melanoma, psoriasis, basal cell carcinoma, eczema and many others.

Gao said the development of SkinGPT-4 began with data collection and preprocessing, followed by model training and validation. “The team collected a large dataset of dermatology images and patient records to train the AI ​​model,” he said.

“One of the significant challenges was the integration of different types of data, including images and text, which required collaboration between computer scientists and dermatologists. A multidisciplinary team worked together to ensure that AI can effectively interpret and analyze skin disease images.”


• SkinGPT-4 diagnoses conditions that have distinct visual characteristics, such as melanoma, psoriasis and eczema.

• Uses a combination of computer vision algorithms, large language models and natural language processing.

• This technology could help doctors and patients in rural areas, where there is often a shortage of trained dermatologists.

SkinGPT-4 uses a combination of computer vision, LLM and natural language processing (NLP) algorithms to enable programs to understand human language.

“The model processes dermatology images using a vision transformer (ViT) to identify patterns and features indicating different skin conditions,” Gao said.

“ViT is consistent with an LLM named Llama-2-13b-chat on our dataset with a customized two-stage training strategy. Thanks to this, LLM Llama-2-13b-chat can understand images of skin diseases and enable conversational diagnosis with the patient in natural language.

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SkinGPT-4 could be particularly useful in diagnosing rare skin conditions that may not be easily recognized by general practitioners.

“A patient with an unusual rash can be quickly and accurately diagnosed using SkinGPT-4, which has been trained on a wide variety of dermatological images, including rare conditions,” Gao said.

“Additionally, to manage chronic skin conditions such as psoriasis, SkinGPT-4 can monitor progression and response to treatment, provide ongoing support and adjust treatment plans as needed.”

Researchers hope SkinGPT-4 will be a breakthrough for remote or underserved areas with a shortage of dermatologists.

“For example, in a rural community where the nearest dermatologist is hundreds of kilometers away, a patient has a suspicious lesion that could be a rare form of skin cancer,” Gao said.

A properly trained AI can be of immense help to doctors. (Shutterstock image)

“Using the SkinGPT-4, a local healthcare provider can take a high-resolution image of the lesion and input the patient’s medical history into the system. SkinGPT-4 analyzes the patient’s image and information and quickly provides a preliminary diagnosis and recommendations for the next course of action.

And as SkinGPT-4 evolves, Gao said the system will learn from its own mistakes through continuous learning and feedback mechanisms.

“By analyzing misdiagnoses and incorporating corrections, the system can refine its algorithms and improve accuracy over time,” he said. “This iterative learning process ensures that SkinGPT-4 evolves and adapts to new data and new trends in dermatology.”

However, Gao is keen to stress that SkinGPT-4 is not designed to completely replace dermatologists. Rather, the program is designed to serve as an evolving and optimizing tool acting as an assistant in facilitating communication between patients and physicians.

“Our goal for SkinGPT-4 is to provide patients with more information about skin conditions while offering physicians a valuable aid in the diagnostic process.”

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