Does the Diagnostic Accuracy and Rates of Face-to-Face Visits Occurring Shortly after an Asynchronized Teledermatology Consultation Justify Its Implementation? An 18-Month Cohort Study.

INTRODUCTION: In 2019, Maccabi Health Services (MHS) rolled out the store-and-forward "Dermadetect" teledermatology consultation (TC) application. Study goal was to analyze MHS records of TCs (August 2019-February 2021) for the rate and reasons for face-to-face consultations (FTFC) occurring shortly after a TC with emphasis on FTFCs resulting in a different diagnosis for the same indication. METHODS: The records of FTFCs held up shortly after TCs were reviewed and classified into cases marked as unsuited for teledermatology, cases in which the indication differed, and cases with the same indication, which were analyzed for concordance of diagnoses. RESULTS: Dermadetect was used by 12,815 MHS beneficiaries. In 30% of cases, following FTFC occurred within the subsequent 5 months, and 901 of them occurred in the subsequent 2 weeks and were analyzed. Thirty percent were not suited for teledermatology, 15% were held for a different indication, and 55% occurred for the same indication. The diagnosis concordance between the TC and recurrent FTFC for the same indication was 97.4%, with full concordance at 68.1% and partial concordance at 29.3%. Overall, 13 patients (1.4%) of the 901 patients using the application only once had a subsequent FTFC within 2 weeks and received a different diagnosis than the one given in the TC. CONCLUSIONS: When considering the implementation of store-and-forward TC's, a 30% rate of following FTFC's during the next 5 months should be considered when planning the reimbursement model. Diagnosis discordance may be disregarded due to its low rates.

Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans.

The integration of teledermatology and artificial intelligence (AI) marks a significant advancement in dermatologic care. This study examines the synergistic interplay between these two domains, highlighting their collective impact on enhancing the accuracy, accessibility, and efficiency of teledermatologic services. Teledermatology expands dermatologic care to remote and underserved areas, and AI technologies show considerable potential in analyzing dermatologic images and performing various tasks involved in teledermatology consultations. Such integration facilitates rapid, precise diagnoses, personalized treatment plans, and data-driven insights. Our explorative study involved designing a GPT-based chatbot named "Dr. DermBot" and exploring its performance in a teledermatologic consultation process. The design phase focused on the chatbot's ability to conduct consultations autonomously. The subsequent testing phase assessed its performance against the backdrop of current teledermatologic practices, exploring the potential of AI and chatbots to simulate and potentially enhance teledermatologic health care. Our study demonstrates the promising future of combining teledermatology with AI. It also brings to light ethical and legal concerns, including the protection of patient data privacy and adherence to regulatory standards. The union of teledermatology and AI not only aims to enhance the precision of teledermatologic diagnoses but also broadens the accessibility of dermatologic services to previously underserved populations, benefiting patients, health care providers, and the overall health care system.

Application of a natural language processing artificial intelligence tool in psoriasis: A cross-sectional comparative study on identifying affected areas in patients' data.

Psoriasis is an immune-mediated skin disease affecting approximately 3% of the global population. Proper management of this condition necessitates the assessment of the body surface area and the involvement of nails and joints. The integration of natural language processing with electronic medical records (EMRs) has recently shown promise in advancing disease classification and research. This study evaluates the performance of ChatGPT-4, a commercial artificial intelligence platform, in analyzing unstructured EMR data of psoriasis patients, particularly in identifying affected body areas. The study analyzed EMR data from 94 patients treated at the Dermatology Department and Psoriasis Outpatient Clinic of Sheba Medical Center between 2008 and 2022. The data were processed using the ChatGPT-4 interface to identify and report the body areas affected by psoriasis. These identified areas were then categorized, and the accuracy of ChatGPT-4's analysis was compared with that of a senior dermatologist. The results revealed that the dermatologist identified 477 psoriasis-affected body areas. ChatGPT-4 accurately recognized 443 (92.8%) of these areas, missed 34, and incorrectly identified 30 areas as affected. From 94 cases, nail involvement was detected in 32 cases (34.0%), with ChatGPT-4 correctly identifying 29 cases. Joint involvement was noted in 25 cases (26.6%), with 24 correctly identified using ChatGPT-4. Complete accuracy was achieved in 54 cases (57.4%), although inaccuracies were observed in 40 cases (42.6%). We found that cases with more characters, words, or identified body areas were more prone to errors, suggesting that increased data complexity heightens the likelihood of inaccuracies in artificial intelligence analysis. ChatGPT-4 demonstrated high performance in analyzing detailed and complex unstructured EMR data from patients with psoriasis, effectively identifying involved body areas, including nails and joints. This highlights the potential of NLP algorithms to enhance the analysis of unstructured EMR data for both clinical follow-up and research purposes.