Can Artificial Intelligence “Hold” a Dermoscope?—The Evaluation of an Artificial Intelligence Chatbot to Translate the Dermoscopic Language

Diagnostics, Journal Year: 2024, Volume and Issue: 14(11), P. 1165 - 1165

Published: May 31, 2024

This survey represents the first endeavor to assess clarity of dermoscopic language by a chatbot, unveiling insights into interplay between dermatologists and AI systems within complexity language. Given complex, descriptive, metaphorical aspects language, subjective interpretations often emerge. The evaluated completeness diagnostic efficacy chatbot-generated reports, focusing on their role in facilitating accurate diagnoses educational opportunities for novice dermatologists. A total 30 participants were presented with hypothetical descriptions skin lesions, including cancers such as BCC, SCC, melanoma, cancer mimickers actinic seborrheic keratosis, dermatofibroma, atypical nevus, inflammatory dermatosis psoriasis alopecia areata. Each description was accompanied specific clinical information, tasked assessing differential diagnosis list generated chatbot its initial response. In each scenario, an extensive potential diagnoses, exhibiting lower performance cases SCC dermatoses, albeit without statistical significance, suggesting that equally satisfied responses provided. Scores decreased notably when practical signs Answers BCC scenario scores category (2.9 ± 0.4) higher than those (2.6 0.66,

Language: Английский

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Artificial Intelligence Performance in Image-Based Cancer Identification: Umbrella Review of Systematic Reviews

Journal of Medical Internet Research, Journal Year: 2025, Volume and Issue: 27, P. e53567 - e53567

Published: April 1, 2025

Background Artificial intelligence (AI) has the potential to transform cancer diagnosis, ultimately leading better patient outcomes. Objective We performed an umbrella review summarize and critically evaluate evidence for AI-based imaging diagnosis of cancers. Methods PubMed, Embase, Web Science, Cochrane, IEEE databases were searched relevant systematic reviews from inception June 19, 2024. Two independent investigators abstracted data assessed quality evidence, using Joanna Briggs Institute (JBI) Critical Appraisal Checklist Systematic Reviews Research Syntheses. further in each meta-analysis by applying Grading Recommendations, Assessment, Development, Evaluation (GRADE) criteria. Diagnostic performance synthesized narratively. Results In a comprehensive analysis 158 included studies evaluating AI algorithms noninvasive across 8 major human system cancers, accuracy classifiers central nervous cancers varied widely (ranging 48% 100%). Similarities observed diagnostic head neck, respiratory system, digestive urinary female-related systems, skin, other sites. Most meta-analyses demonstrated positive summary performance. For instance, 9 meta-analyzed sensitivity specificity esophageal cancer, showing ranges 90%-95% 80%-93.8%, respectively. case breast detection, calculated pooled within 75.4%-92% 83%-90.6%, Four reported ovarian both 75%-94%. Notably, lung was relatively low, primarily distributed between 65% 80%. Furthermore, 80.4% (127/158) high according JBI Checklist, with remaining classified as medium quality. The GRADE assessment indicated that overall moderate low. Conclusions Although shows great achieving accelerated, accurate, more objective diagnoses multiple there are still hurdles overcome before its implementation clinical settings. present findings highlight concerted effort research community, clinicians, policymakers is required existing translate this into improved outcomes health care delivery. Trial Registration PROSPERO CRD42022364278; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022364278

Language: Английский

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Artificial Intelligence Meets Real-Life Dermatology: Diagnostic Accuracy Assessment in a Retrospective Case Series

Research Square (Research Square), Journal Year: 2025, Volume and Issue: unknown

Published: April 28, 2025

Background: Although artificial intelligence (AI) has shown considerable promise in dermatological diagnostics, its real-world clinical validation remains limited. This study aimed to evaluate the diagnostic accuracy and decision-support capabilities of GPT-4.5 a routine outpatient dermatology setting. Methods: A total 402 dermatologic cases from 400 patients were retrospectively analyzed at secondary-care clinic. was provided with dermoscopic images, along brief metadata (e.g., age, lesion location, duration), generate differential diagnoses management suggestions. Model outputs compared dermatologist assessments. Performance metrics included accuracy, sensitivity, specificity, precision, F1 score. Misclassification patterns also reviewed. Results: achieved an overall 89.3% correctly identified primary diagnosis as top-ranked suggestion 71.9% cases. Sensitivity specificity 89.7% 91.4%, respectively, score 94.3%. Clinical guidance recommendations concordant physician decisions 91.0% Diagnostic higher non-biopsied (96.0%) those requiring histopathological confirmation (84.2%). Highest performance observed infectious (94.3%) inflammatory (96.2%) dermatoses. Misclassifications most common pigmented neoplasms morphologically similar disorders. Conclusion: demonstrated high strong alignment dermatology, especially for visually distinct conditions. However, declined diagnostically complex or ambiguous These findings support potential supplementary tool, while underscoring need multimodal inputs, oversight, broader prospective prior integration.

Language: Английский

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THE ANALYSIS STUDY OF ARTIFICIAL INTELIGENCE FOR SKIN CANCER : A COMPREHENSIVE SYSTEMATIC REVIEW

Journal of Advance Research in Medical & Health Science (ISSN 2208-2425), Journal Year: 2024, Volume and Issue: 10(6), P. 104 - 112

Published: June 18, 2024

Background: Skin cancer diagnosis relies heavily on the interpretation of visual patterns, making it a complex task that requires extensive training in dermatology and dermatoscopy. However, AI algorithms have been shown to accurately diagnose skin cancers, even outperforming experienced dermatologists image classification tasks constrained settings. The aim: aim this study show about artificial intelligence for cancer. Methods: By Preferred Reporting Items Systematic Review Meta-Analysis (PRISMA) 2020, was able met all requirements. This search approach, publications came out between 2014 2024 were taken into account. Several different online reference sources, like Pubmed, SagePub, Science Direct used do this. It decided not take account review pieces, works had already published, or only half done. Result: Eight found be directly related our ongoing systematic examination after rigorous three-level screening approach. Subsequently, comprehensive analysis complete text conducted, additional scrutiny given these articles. Conclusion: use has high potential facilitate way is diagnosed. Two main branches are detect classify cancer, namely shallow deep techniques.

Language: Английский

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Artificial Intelligence Smartphone Application for Detection of Simulated Skin Changes: An In Vivo Pilot Study

Skin Research and Technology, Journal Year: 2024, Volume and Issue: 30(10)

Published: Oct. 1, 2024

ABSTRACT Background The development of artificial intelligence (AI) is rapidly expanding, showing promise in the dermatological field. Skin checks are a resource‐heavy challenge that could potentially benefit from AI‐tool assistance, particularly if provided widely available AI solutions. A novel smartphone application(app)‐based system, “SCAI,” was developed and trained to recognize spots paired images skin, pursuing identification new skin lesions. This pilot study aimed investigate feasibility SCAI‐app identify simulated changes vivo. Materials methods conducted controlled setting with healthy volunteers standardized, (test spots), consisting customized 3‐mm adhesive three colors (black, brown, red). Each volunteer had total eight test adhered four areas on back legs. collected smartphone‐ template‐guided standardized before after spot application, using its backend algorithms between images. Results Twenty‐four were included, amounting 192 spots. Overall, detection identified sensitivity 92.0% (CI: 88.1–95.9) specificity 95.5% 95.0–96.0). SCAI‐app's positive predictive value 38.0% 31.0–44.9), while negative 99.7% 99.0–100). Conclusion showed detect vivo setting. app's clinical real‐life lesions remains be investigated, where false positives particular needs addressed.

Language: Английский

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Towards Explainable Deep Learning for Non-melanoma Skin Cancer Diagnosis

Lecture notes in computer science, Journal Year: 2024, Volume and Issue: unknown, P. 241 - 254

Published: Nov. 18, 2024

Language: Английский

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