Introduction: Convolutional neural networks (CNNs) can diagnose skin cancers with impressive accuracy in experimental settings, however, their performance in the real-world clinical setting, including comparison to teledermatology services, has not been validated in prospective clinical studies.
Methods And Analysis: Participants will be recruited from dermatology clinics at the Alfred Hospital and Skin Health Institute, Melbourne. Skin lesions will be imaged using a proprietary dermoscopic camera. The artificial intelligence (AI) algorithm, a CNN developed by MoleMap Ltd and Monash eResearch, classifies lesions as benign, malignant or uncertain. This is a preintervention/postintervention study. In the preintervention period, treating doctors are blinded to AI lesion assessment. In the postintervention period, treating doctors review the AI lesion assessment in real time, and have the opportunity to then change their diagnosis and management. Any skin lesions of concern and at least two benign lesions will be selected for imaging. Each participant's lesions will be examined by a registrar, the treating consultant dermatologist and later by a teledermatologist. At the conclusion of the preintervention period, the safety of the AI algorithm will be evaluated in a primary analysis by measuring its sensitivity, specificity and agreement with histopathology where available, or the treating consultant dermatologists' classification. At trial completion, AI classifications will be compared with those of the teledermatologist, registrar, treating dermatologist and histopathology. The impact of the AI algorithm on diagnostic and management decisions will be evaluated by: (1) comparing the initial management decision of the registrar with their AI-assisted decision and (2) comparing the benign to malignant ratio (for lesions biopsied) between the preintervention and postintervention periods.
Ethics And Dissemination: Human Research Ethics Committee (HREC) approval received from the Alfred Hospital Ethics Committee on 14 February 2019 (HREC/48865/Alfred-2018). Findings from this study will be disseminated through peer-reviewed publications, non-peer reviewed media and conferences.
Trial Registration Number: NCT04040114.
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http://dx.doi.org/10.1136/bmjopen-2021-050203 | DOI Listing |
Cureus
December 2024
Community and Family Medicine, All India Institute of Medical Sciences, Bhopal, Bhopal, IND.
Background Artificial Intelligence (AI) is revolutionizing medical science, with significant implications for radiology. Understanding the knowledge, attitudes, perspectives, and practices of medical professionals and residents related to AI's role in radiology is crucial for effective integration. Methods A cross-sectional survey was conducted among members of the Indian Radiology & Imaging Association (IRIA), targeting practicing radiologists and residents across academic and non-academic institutions.
View Article and Find Full Text PDFComput Struct Biotechnol J
January 2025
Ph.D. Program in Computer Science, The Graduate Center, The City University of New York, New York, NY, USA.
Despite the wealth of single-cell multi-omics data, it remains challenging to predict the consequences of novel genetic and chemical perturbations in the human body. It requires knowledge of molecular interactions at all biological levels, encompassing disease models and humans. Current machine learning methods primarily establish statistical correlations between genotypes and phenotypes but struggle to identify physiologically significant causal factors, limiting their predictive power.
View Article and Find Full Text PDFiScience
January 2025
Cognitive Neuroimaging Unit U992, CNRS, INSERM, CEA, DRF/Institut Joliot, Université Paris-Saclay, NeuroSpin Center, 91191 Gif/Yvette, France.
The need for attention to enable statistical learning is debated. Testing individuals with impaired consciousness offers valuable insight, but very few studies have been conducted due to the difficulties inherent in such studies. Here, we examined the ability of patients with varying levels of disorders of consciousness (DOC) to extract statistical regularities from an artificial language composed of randomly concatenated pseudowords by measuring frequency tagging in EEG.
View Article and Find Full Text PDFFront Public Health
January 2025
CAS Key Laboratory of Behavioral Science, Institute of Psychology, Chinese Academy of Sciences, Beijing, China.
Introduction: Public health messaging is crucial for promoting beneficial health outcomes, and the latest advancements in artificial intelligence offer new opportunities in this field. This study aimed to evaluate the effectiveness of ChatGPT-4 in generating pro-vaccine messages on different topics for Human Papillomavirus (HPV) vaccination.
Methods: In this study ( = 60), we examined the persuasive effect of pro-vaccine messages generated by GPT-4 and humans, which were constructed based on 17 factors impacting HPV vaccination.
Precis Chem
January 2025
Key Laboratory of Precision and Intelligent Chemistry, University of Science and Technology of China, Hefei, Anhui 230026, China.
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