The application of Artificial Intelligence (AI) to the triage of suspicious skin lesions and the detection of skin cancer in primary care clinical settings
type: CreativeWork
description: The application of Artificial Intelligence (AI) to the triage of suspicious skin lesions and the detection of skin cancer in primary care clinical settings Background Skin cancers, including melanoma and the keratinocyte carcinomas (Basal Cell Carcinomas and Squamous Cell Carcinomas) are the most common cancers worldwide with rising incidence in most populations. Earlier diagnosis of skin cancer is associated with better outcomes for patients and improved survival. The first step in diagnosis of skin cancer is a patient, or their friend or relative, noticing a skin lesion that they are concerned about because of a change in appearance or new symptoms. In primary care, general practitioners (GPs) and other primary care practitioners then face the difficult task of triaging suspicious skin lesions to decide which lesions need onward referral on an urgent suspected skin cancer pathway for assessment in secondary care. There has been growing use of teledermatology services in recent years to triage urgent suspected skin cancer referrals from primary to secondary care, and discharge clearly benign skin lesions before they are seen in secondary care clinics. Artificial intelligence (AI) algorithms, primarily neural networks, have been applied to the diagnosis of skin cancer with promising diagnostic accuracy results; AI has been shown to be able to perform on a par with consultant dermatologists in the diagnosis of skin cancer. In the UK the current focus is on implementing AI in teledermatology settings, to help triage out clearly benign skin lesions referred from primary care and reduce the number of patients that need review in secondary care clinics. However, several questions remain about the efficacy, safety, and cost effectiveness of this approach. There are several alternative points in the skin cancer diagnostic pathway where AI could be implemented with various risks and benefits; implementation earlier in the pathway could help improve awareness and early prese…
abstract: The application of Artificial Intelligence (AI) to the triage of suspicious skin lesions and the detection of skin cancer in primary care clinical settings Background Skin cancers, including melanoma and the keratinocyte carcinomas (Basal Cell Carcinomas and Squamous Cell Carcinomas) are the most common cancers worldwide with rising incidence in most populations. Earlier diagnosis of skin cancer is associated with better outcomes for patients and improved survival. The first step in diagnosis of skin cancer is a patient, or their friend or relative, noticing a skin lesion that they are concerned about because of a change in appearance or new symptoms. In primary care, general practitioners (GPs) and other primary care practitioners then face the difficult task of triaging suspicious skin lesions to decide which lesions need onward referral on an urgent suspected skin cancer pathway for assessment in secondary care. There has been growing use of teledermatology services in recent years to triage urgent suspected skin cancer referrals from primary to secondary care, and discharge clearly benign skin lesions before they are seen in secondary care clinics. Artificial intelligence (AI) algorithms, primarily neural networks, have been applied to the diagnosis of skin cancer with promising diagnostic accuracy results; AI has been shown to be able to perform on a par with consultant dermatologists in the diagnosis of skin cancer. In the UK the current focus is on implementing AI in teledermatology settings, to help triage out clearly benign skin lesions referred from primary care and reduce the number of patients that need review in secondary care clinics. However, several questions remain about the efficacy, safety, and cost effectiveness of this approach. There are several alternative points in the skin cancer diagnostic pathway where AI could be implemented with various risks and benefits; implementation earlier in the pathway could help improve awareness and early prese…
author: Jones, Owain
keywords: Skin cancer, Primary care, Artificial Intelligence
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/b784e7ce-1b77-4121-9ab5-ef905c096d77/download
license: http://purl.org/NET/rdflicense/allrightsreserved
date published: 2025-01-31
date modified: 2026-07-22T22:24:10Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.119946