AI virtual assistants have significant potential to alleviate the pressure on overly burdened healthcare systems by enabling patients self-assess their symptoms and seek further care when appropriate. For these make a meaningful contribution globally, they must be trusted professionals alike, service needs of in diverse regions segments population. We developed an assistant which provides with triage diagnostic information. Crucially, system is based generative model, allows for relatively...

Introduction An artificial intelligence as a medical device (AIaMD), built on convolutional neural networks, has demonstrated high sensitivity for melanoma. To be of clinical value, it needs to safely reduce referral rates. The primary objective this study was demonstrate that the AIaMD had higher rate correctly classifying lesions did not need referred biopsy or urgent face-to-face dermatologist review, compared teledermatology standard care (SoC), while achieving same detect malignancy....

Introduction Deep Ensemble for Recognition of Malignancy (DERM) is an artificial intelligence as a medical device (AIaMD) tool skin lesion assessment. Methods We report prospective real-world performance from its deployment within cancer pathways at two National Health Service hospitals (UK) between July 2021 and October 2022. Results A total 14,500 cases were seen, including patients 18–100 years old with Fitzpatrick types I–VI represented. Based on 8,571 lesions assessed by DERM confirmed...

Artificial Intelligence (AI) systems could improve system efficiency by supporting clinicians in making appropriate referrals. However, they are imperfect nature and misdiagnoses, if not correctly identified, can have consequences for patient care. In this paper, findings from an online survey presented to understand the aptitude of GPs (n = 50) appropriately trusting or output a fictitious AI-based decision support tool when assessing skin lesions, identify which individual characteristics...

Introduction Identification of skin cancer by an Artificial Intelligence (AI)-based Digital Health Technology could help improve the triage and management suspicious lesions. Methods The DERM-003 study (NCT04116983) was a prospective, multi-center, single-arm, masked that aimed to demonstrate effectiveness AI as Medical Device (AIaMD) identify Squamous Cell Carcinoma (SCC), Basal (BCC), pre-malignant benign lesions from dermoscopic images Suspicious were suitable for photography photographed...

Abstract A secondary care trust received over 2800 urgent suspected skin cancer referrals (USCRs) in 2021, 50% more than 2016 (www.england.nhs.uk/statistics/statistical-work-areas/cancer-waiting-times), < 1 10 of which resulted diagnosis (www.cancerdata.nhs.uk/cwt_conversion_and_detection). Judicious implementation artificial intelligence (AI) may improve referral accuracy. We conducted a prospective, postdeployment, single-centre clinical performance review UK Conformity Assessed...

Abstract Artificial intelligence (AI) is a rapidly emerging field in dermatology, aimed at delivering efficient and effective patient care. To date, there lack of substantial evidence for the use this technology clinical setting. However, pandemic era has placed significant pressures on dermatology services, specifically skin cancer clinics our trust, which experienced 30% increase demand compared with prepandemic levels. A novel solution urgent referrals was to test AI teledermatology...

Abstract Teledermatology can help support the timely diagnosis of skin cancer. NHS England recently published a roadmap to accelerate roll-out teledermatology services nationally, including an updated series audit and quality control standards. However, there remains no consensus as ideal methodology, which contributes paucity comparative evidence. This study aims describe reproducible methodology for monitoring clinician performance in pathways. We present novel quantitative risk scoring...

Abstract Dermatology services are facing rising demand with limited workforce and resources. Artificial intelligence as a medical device (AIaMD) may offer way to expand capacity improve patient outcomes. The aim of this project was develop cost–utility health economic model an AIaMD used for the screening, triage assessment lesions suspicious skin cancer using dermoscopic images. This report focuses on patients referred by general practitioners dermatology urgent suspected pathway. takes...

Abstract Over 25% of patients with suspected skin cancer in England waited over 4 weeks from urgent referral to diagnosis October 2023. Implementation artificial intelligence (AI) can augment this pathway improve timely diagnosis. A prospective, postdeployment, multicentre clinical performance review AIaMD was performed. is a UKCA class IIa-approved as medical device intended for use the screening, triage and assessment lesions suspicious cancer. deployed at four sites part National Health...

Abstract Teledermatology can help support the timely diagnosis of skin cancer. NHS England recently published a roadmap to accelerate roll-out teledermatology services nationally, including an updated series audit and quality control standards. However, there remains no consensus as ideal methodology, which contributes paucity comparative evidence. This study aims describe reproducible methodology for monitoring clinician performance in pathways. We present novel quantitative risk scoring...

ConclusionsThere was qualitative evidence of learning from the GP and geriatric trainees, with good formal feedback patients.This pilot project provided an exciting template to improve training both GPs geriatricians, care older population facilitate closer working between primary secondary care.Barriers implementation may include existing pressures on services take registrars out use 30-minute appointments.Evaluation costeffectiveness patient outcomes should be investigated in future studies.■

Abstract Deep Ensemble for Recognition of Malignancy (DERM) is an artificial intelligence as a medical device (AIaMD) tool skin lesion assessment. We report prospective real-world performance from its deployment within cancer pathways at two National Health Service hospitals (UK). Between July 2021 and October 2022, 14,500 cases were seen, including patients 18–100 years old with Fitzpatrick types I–VI represented. Based on 8,571 lesions assessed by DERM confirmed outcomes, versions A B...