bluuebunny/crossref_metadata_2025_split huggingface dataset:
BT06 (P034) Using artificial intelligence to bridge the technical gap in dermatology: the role of large language models in creating an interactive Fitzpatrick skin phototype assessment
Authors: Zeina Mohamad , Zeina Bassam , Nachev Parashkev 
Published: June 2024

Abstract
Patient engagement is evolving with the advent of digital tools such as interactive websites and artificial intelligence (AI), yet significant barriers remain in the technical expertise required to create tools that benefit patients. We explore how large language models (LLMs), a type of AI that can facilitate and accelerate programming tasks, can enable clinicians and researchers to create interactive web-based patient engagement tools without the need for a dedicated web developer. We explore this possibility by developing, with the help of an LLM, an interactive skin self-assessment web tool. We used GPT4, a state-of-the-art LLM, as a web development assistant, and created an interactive JavaScript web page that guides patients through the Fitzpatrick skin phototype questionnaire. This involved building a minimal version first, and iteratively improving it. This web page initially asked the user the first two questions of the assessment, calculated a score, and returned this to the user. Once the code written by GPT4 was tested and shown to work, the tool was gradually improved to include all questions in the Fitzpatrick scale, and the result was modified to provide not only the total score, but also the result category. GPT4 allowed us, with no experience writing interactive JavaScript web pages, to develop and launch a web page that guides patients through a Fitzpatrick scale assessment. When GPT4 was asked to expand the questionnaire from two questions to the whole test, it gladly did so; however, the questions were not accurate. It then had to be given the specific wording of the questions and answers for the test, which it incorporated into functioning JavaScript code. This had to be performed in stages, adding two questions at a time, due to context limitations at the time. When this introduced issues, such as the code breaking or the website style looking inconsistent, GPT4 was able to fix these issues when they were described to it. Since the web page’s launch in March 2023, the web page has been viewed 1875 times by 1419 unique users. This study demonstrates one way in which LLMs can be used to enhance patient engagement in dermatology, by facilitating the creation of interactive skin assessments with utility in many dermatology settings. Hallucination remains a problem, and any created tools must be thoroughly verified for factuality. Although no authors had any experience writing interactive JavaScript, they have coding experience elsewhere, and future work should assess whether similar results can be achieved by a broader user base. Two authors received funding from a commercial organization for this work (A & Z Dermatology Clinic Ltd).
BT09 Multicentre prospective clinical performance analysis of an artificial intelligence as a medical device deployed within urgent suspected skin cancer pathways
Authors: Brewer Felix , Kentley Jonathan , Kalsi Dilraj , Mullarkey Dan , Thomas Lucy 
Published: June 2024

Abstract
Over 25% of patients with suspected skin cancer in England waited over 4 weeks from urgent referral to diagnosis in October 2023. Implementation of artificial intelligence (AI) can augment this pathway to improve timely diagnosis. A prospective, postdeployment, multicentre clinical performance review of AIaMD was performed. AIaMD is a UKCA class IIa-approved artificial intelligence as a medical device intended for use in the screening, triage and assessment of skin lesions suspicious for skin cancer. AIaMD was deployed at four sites as part of the National Health Service AI in Health and Care Award. Patients assessed by the most recent version of AIaMD from April 2022 to November 2023 were eligible for inclusion. Outcomes were confirmed from histology reports for cancerous lesions and histology reports or consultant teledermatology assessment for premalignant and benign lesions. AIaMD assessed 3979 lesions with outcomes confirmed, including 38 melanomas, 68 squamous cell carcinomas, 181 basal cell carcinomas, 5 rare skin cancers, and 612 premalignant lesions such as Bowen disease and actinic keratoses. The negative predictive value – how often AIaMD labelled lesions as premalignant or benign and correctly ruled out cancer – was 99.8% (539 of 540). The sensitivities of AIaMD for melanoma, all skin cancer and premalignancy were 97% (37 of 38), 96.6% (282 of 292) and 86.3% (528 of 612), respectively. The specificity of AIaMD for benign lesions was 74.9% (2118 of 2827). For benign lesions confirmed by biopsy only, the specificity was 27.6% (105 of 381). One site reported a reduction in average wait time to first appointment by 11 days, a 10% reduction in biopsies and a 13% reduction in routine follow-up appointments. This postmarket service evaluation reported the clinical outcomes of AIaMD – an AI device used for assessment of skin lesions. The pathway was sensitive, identifying 282 out of 292 skin cancers, and specific, correctly identifying three out of every four benign lesions assessed and confirmed clinically or histologically. Moreover, implementation of AIaMD was linked to benefits to the pathway such as a reduction of biopsies. The integration of AIaMD into skin cancer diagnostic pathways could significantly improve the accuracy of urgent suspected skin cancer referrals, removing unnecessary specialist review of benign lesions. Part of the deployment period was funded as part of the NHS AI in Health and Care Award, and two of the authors are employed by one of the deployment sites. Three of the authors are employed by the AI provider.
BT11 Artificial intelligence and ethnically diverse skin: potential, pitfalls and a model for reducing risk
Authors: Lamche Lucas , Felton Jessie 
Published: June 2024

Abstract
There is increasing demand for artificial intelligence (AI) tools to manage skin cancer; however, there is minimal diversity in images used for AI training and validation (Wen D, Khan SM, Xu AJ et al. Characteristics of publicly available skin cancer image datasets: a systematic review. Lancet Digit Health 2022; 4: e64–74). Limited validation in ethnically diverse skin, in tandem with no human ‘sense check’ on photographic input and analysis by AI, increases the risk of misdiagnosis of skin cancers. We assessed AI bias and developed a cost-effective protocol to increase diagnostic accuracy in patients with ethnically diverse skin. A dermatology nurse collected clinical history and used videodermoscopy to capture optimized macro and dermoscopic images of lesions. Data were from consented patients with self-identified ethnically diverse skin, being assessed via the 2-week-wait skin cancer teledermatology pathway over 7 months. This was combined with clinical history. Histological diagnosis (where available) and consultant diagnosis were recorded for each lesion, used as gold standard to compare Moleanalyzer pro AI assessments of lesions. This ‘real-time AI’ is run at the bedside and clearly demarcates the lesion being analysed. We analysed 69 lesions from 64 patients (45 female, 19 male) with an age range of 18–81 years (mean 49.9). The AI malignancy threshold (MT) is 0–1 (1 being highest risk of malignancy). With MT on the Moleanalyzer pro AI at &gt; 0.2, clinical–AI concordance was 75.4%, while AI sensitivity and specificity were 83.3% and 74.6%, respectively. Published results for Moleanalyzer pro AI in people with Fitzpatrick type II and III skin at MT 0.5 and above show sensitivity 81.6% and specificity 88.9%. In some cases in our study, AI picked out the incorrect darker area and not the index lesion in ethnically diverse skin. In addition, only rare subtypes of skin cancers were found in our patient population. We found that when using dermatological AI diagnostic tools, a nurse-led photography protocol is optimal. Incorporating clinical history alongside ‘real-time AI’ at the bedside, and a human ‘sense check’ on AI diagnosis, reduces risk. When using AI in an ethnically diverse population, a reduced MT must be considered to achieve a level of accuracy comparable with published data. With an effective protocol, AI can provide extensive savings and reduced waiting times. Each diagnostic AI tool developed for skin cancer should be validated with ethnically diverse skin images to reduce health inequality and potential harm to patients. We propose this is introduced into formal quality standards for AI used in skin cancer diagnosis.
BT14 (P026) Teledermatology from the other side: a clinical photographers’ perspective
Authors: Uthayakumar Aarthy , Searle Tamara , Elshimy Noha 
Published: June 2024

Abstract
Teledermatology has transformed the way we deliver our skin cancer service, with medical photographers playing a vital role in our trust. We sought to further streamline our existing teledermatology service and explore challenges faced by the photography team. Our teledermatology model involves patients directly attending the hospital for a professional photography appointment before consultant triage of the lesion(s). After observing teledermatology photography lists with detailed photographer feedback, 1 week of teledermatology clinics across all three sites of the trust were analysed. Referral type, discrepancies between the general practitioner (GP) referral and photos taken, and chaperone availability were explored, in addition to the referral pathway and patient information supplied. In total 122 teledermatology patients were included. Photographers highlighted difficulties with patients requesting lesions not specified on the GP referral to be photographed, and not having sufficient information prior to their teledermatology appointment, making it often difficult to manage expectations. Furthermore, due to minimal staffing, the availability of chaperones was often limited. In 19% of cases there was a discrepancy between the GP referred lesion and photograph taken, which was due to additional lesions being photographed (35%), patients having multiple moles (22%), and unlikely suitability for teledermatology and no GP referral being present (17%). Improvements have been implemented at different levels along the teledermatology pathway to further streamline the service and reduce some of the pressures faced by our clinical photographer colleagues. Modifications to the GP suspected skin cancer pathway referral make it clearer which patients are suitable for teledermatology, and request a mandatory lesion and locator photograph. The triage process has been altered to improve robustness, with an additional triage step to minimize patients inappropriate for teledermatology being booked in, such as those with more than three lesions, genital lesions and patients on stretchers. Patient information has been clearly displayed in the medical photography department, highlighting that only the referred lesion can be photographed, and a paper copy of the teledermatology patient information leaflet is provided to highlight the next steps to the patient. This minimizes questions directed at the photographers and aids management of patient expectations. Finally, a protocol for medical photographers has been implemented to standardize teledermatology images taken, with changes to include polarized and nonpolarized dermoscopy. With these changes we have minimized the challenges faced by our clinical photography colleagues, whose perspective and experience of a successful teledermatology service is critical, and we believe this should be explored in order to make meaningful improvements.
Poster presentationsBT15 Ethical implications of artificial intelligence in dermatology: use-case analysis of skin cancer diagnostic smartphone apps
Authors: Shah Fazal , Arecco Daniel , Draper Heather , Matin Rubeta 
Published: June 2024

Abstract
Skin cancer is the most common cancer worldwide. Early diagnosis is crucial for improving patient survival and morbidity. Artificial intelligence (AI)-assisted smartphone applications (apps) for skin cancer potentially offer accessible, early risk assessment of suspicious skin lesions. If harnessed to effective and responsive dermatology services, they may improve patient outcomes. The integration of novel technologies into dermatology raises ethical concerns. Ethical principles for acceptable AI governance are well known. Less well elucidated are how these should be applied to actual AI apps. Ethical use-case analysis can improve this understanding. Accordingly, we undertook an ethical use-case analysis of commercially available skin cancer apps. We applied previously described methods for ethical analysis of clinical AI applications to two popular skin cancer apps: SkinVision and Scanoma (Rogers WA, Draper H, Carter SM. Evaluation of artificial intelligence clinical applications: detailed case analyses show value of healthcare ethics approach in identifying patient care issues. Bioethics 2021; 35: 623–33). Systematic searches incorporating published literature, regulatory documents, and websites were undertaken to provide a detailed description of each app, and review the available evidence about their development, effectiveness and use. Screening for inclusion was undertaken by two researchers independently. Ethical concerns were identified with reference to previously described ethical concerns and principles for AI-assisted healthcare. These data were also independently checked and agreed on. By conceptualizing ethical principles within the use context of skin cancer apps, we identified specific ethical issues arising throughout the AI lifecycle of both apps. One company provided extensive detail regarding algorithm development and decision making; this information was insufficiently reported for the other app. Other concerns identified related to the number, quality, consistency and independence of studies assessing algorithm efficacy. Limited efforts to address potential skin tone biases and exclusion of patients with darker skin as target users by one app risk perpetuating existing inequalities. Inadequate regulatory requirements were highlighted. To the best of our knowledge, this study represents the first ethical use-case analysis of patient-facing AI-assisted skin cancer apps. Our findings suggest inadequate incorporation of bioethical norms such as justice, responsibility and transparency into the development and deployment of both apps. Improved regulation will increase accountability. Ensuring ethics by design through integration between technology developers, dermatologists, ethicists and patients is urgently needed to prevent the potential benefits of AI-assisted skin cancer apps being overshadowed by potential ethical harms.
BT16 BT17 Dermatology advice and guidance: a service evaluation
Authors: Sathanantham Kugili , Hickey Sian , Ellison Judith 
Published: June 2024

Abstract
We wish to present a service evaluation undertaken of the regional dermatology advice and guidance (A&G) service provided within our locale. As part of the national Elective Care Transformation Programme, A&G services were created, primarily as a consultant dermatologist-led service prior to referral. This was designed to transform the way in which primary care referrals are managed with continued access to specialist clinical advice. The process permits the streamlined conversion to National Health Service referral if required and is being advocated as a potential main referral pathway to dermatology in England. This has resulted in a 400% increase in dermatology A&G requests since the onset of the COVID-19 pandemic in 2020 across England. In real-world practice, this translated to 200 referrals over a 2-week period across four trusts. Our service evaluation analysed the practices across the region to capture the way this service is currently delivered. We focused on the grade of the provider of A&G, whether a rota system was in place, how this was job planned into clinical activity, the service platform utilized and the size of the region the service covered. Each referral was assessed to ascertain the grade of the referrer and whether appropriate imaging and an adequate past medical and medication history were provided. From our service evaluation, approximately 25% of A&G outcomes were directed to have a referral only, 30% of outcomes were advice followed by formal referral if advice did resolve the problem, and 25% of referrals were given advice only. Overall, 20% of referrals were rejected due to either inadequate imaging, inadequate history and imaging, or an inadequate history alone. In conclusion, 70% of the surveyed consultants felt the A&G referrals were subjectively useful to patients and as an educational tool to general practitioner colleagues. This service evaluation provided a baseline understanding of A&G: 25% of current referrals were managed via this service alone, and 30% of the referrals were given a trial of advice with safety-net formal referral advocated if advice did not resolve the issue. As a result, nearly 50% of referrals were managed via an electronic referral service where they would have previously been managed via a face-to-face clinic appointment.
BT20 Comparison of colour accuracy of a DSLR camera and mobile phone cameras used for dermoscopy images: a pilot study
Authors: Teo Daryl , Potluru Aparna , Salako Kazeem 
Published: June 2024

Abstract
Dermatology, being a visual specialty, lends itself well to the advent of telemedicine. Utilization of teledermatology has been greatly expanded since the COVID-19 pandemic. Patients are increasingly making use of their own personal devices to capture images of their skin conditions to show the clinician. Advances in cameras of mobile phone devices have also encouraged good-quality image capturing. However, mobile phone cameras have inbuilt software that is often calibrated to deliver appealing consumer photography. Each company has their own software and marketing regarding this. The present pilot study aims to compare the colour accuracy of DSLR and mobile phone cameras used in dermoscopy images. Dermoscopic images allow for consistent lighting to standardize comparison. We used a DSLR camera (Nikon D7200 body with the DermLite Foto II Pro dermoscopy lens) as the standard for comparison, which is likely to produce a better image. Four mobile phones were used (Apple iPhone 12 Pro, Samsung Galaxy Note 8, Samsung Galaxy S21 Ultra, and Google Pixel 7A) and dermoscopy images were captured through a Heine DELTAone. Three photos of a melanocytic naevus each from five participants were taken. All raw images were transferred to a computer and analysed with FIJI, ImageJ freeware. RGB images were converted to the CIELAB colour space. The CIELAB colour space was used as a device-independent model to compare colour difference, which was determined by the computation of total colour difference (ΔE). ΔE values in all participants were calculated, with the lowest mean difference for Pixel 7A (12.9), followed by iPhone 12 Pro (18.6), Note 8 (20.0) and S21 Ultra (26.1). Of note, there was a greater mean difference across all devices in our participants of Afro-Caribbean ethnicity (25.7) compared with other participants. There is variation in colour accuracy between mobile phone devices in comparison with DSLR photography for our dermoscopic images of melanocytic naevi. This difference appears more pronounced in Black individuals. It is important for dermatologists to be aware of the devices used in capturing these images when they are used as part of any telemedicine service. Careful consideration is required for assessing rashes in patients with darker skin tones, especially as those clinical photographs would not have consistent lighting and exposure factors, which can influence the final image. The study can be expanded to include more participants and images to further investigate these differences.
BT27 How reliable is teledermatology in diagnosing skin cancer? A retrospective analysis of skin cancer outcomes from a single centre
Authors: Moon Emily , Elkins Anna , Johnson Jo , Ahmad Maria , Peng Chenjing 
Published: June 2024

Abstract
Teledermatology has become a pivotal tool to meet the increasing demands on skin cancer services. Factors such as referral quality and clinical judgement need to be considered when evaluating its effectiveness and reliability. A single-centre retrospective study was conducted to evaluate the diagnostic accuracy of a pilot teledermatology service used to triage 2-week-wait skin cancer referrals from primary care. Between May and July 2023 (total 158 referrals), 45% of 2-week-wait teledermatology referrals were accepted into a face-to-face (F2F) clinic, of which 80% were seen in a 2-week-wait clinic, either by dermatology or plastic surgery. The remaining accepted cases were redirected into a general dermatology clinic. Of the 87 referrals returned, 31% were due to poor-quality photographs or insufficient information provided, and 69% were thought to be appropriate for primary care management. A detailed analysis of 100 referrals was conducted to assess the referral quality and clinical outcomes. Of the 51 patients with a suspected skin cancer diagnosed on teledermatology, 21% (5 of 24) of melanomas, 17% (4 of 23) of squamous cell carcinomas and 75% (3 of 4) of basal cell carcinomas were confirmed on histological analysis. Of the 21 patients who were suspected to have a precancerous or benign lesion on teledermatology, two were diagnosed with a malignancy on histology. When comparing the teledermatology diagnosis vs. histological diagnosis as the gold standard, diagnostic accuracy was 0.66 [95% confidence interval (CI) 0.51–0.78], with 92% sensitivity, 42% specificity and κ = 0.33 (95% CI 0.11–0.56). This was comparable with the diagnostic accuracy of a F2F clinic review, which was 0.66 (95% CI 0.51–0.79) with 96% sensitivity, 38% specificity and κ = 0.33 (95% CI 0.13–0.54). When only the F2F diagnosis was used as the gold standard, the diagnostic accuracy of teledermatology was higher at 0.78 (95% CI 0.66–0.87), with 91% sensitivity, 56% specificity and κ = 0.5 (95% CI 0.28–0.71). Our study demonstrated that teledermatology can be a useful tool to reduce unnecessary F2F clinic reviews, if adequate information and photographs are provided. It has demonstrated high skin cancer diagnostic sensitivity, but with compromised specificity. There was a moderate level of concordance between diagnoses made in teledermatology and the F2F clinic. Although teledermatology and F2F assessment had very comparable diagnostic accuracies, a higher sensitivity and lower specificity were noted on F2F assessment. This could be attributed to the initial diagnosis made on teledermatology influencing the subsequent clinical decision in a F2F clinic. Ongoing caution, critical reflection and more studies are warranted to elucidate teledermatology’s full potential in the diagnosis and management of skin cancer.