[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-82594-en":3,"doc-seo-82594-105":29,"detail-sidebar-cat-0-en-105":90},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":13,"seo_description":14,"update_tm":27,"read_time":28},82594,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",7,"Healthcare","How Indian Dermatologists are Utilizing Artificial Intelligence for Clinical Practice and Workflow Management","The nationwide survey maps clinical bottlenecks reported by Indian dermatologists to their current adoption of artificial intelligence, with a special focus on atopic dermatitis. A problem-first methodology links technology-agnostic frustrations and AD-specific management hurdles to AI usage patterns, barriers, and ethical concerns. Among 377 respondents, adherence and difficult-case planning were frequent challenges. Nearly half adopted general-purpose LLMs for literature synthesis and documentation, with barriers differing by experience and heightened concern about patient self-misdiagnosis.","arXiv :2607 .0 1252v 1 [ cs .CY] 3 Jun 2026  \nHow Indian Dermatologists are Utilizing Artificial Intelligence for Clinical Practice and Workflow Management: A Nationwide Survey with a Special Focus on atopic dermatitis  \nDipayan Sengupta 1 , Saumya Panda2 , Sandipan Dhar3 , Dipankar De4 , Deepika Pandhi5 , and  \nNarayanan B6  \n1 Charnock Hospital, Kolkata, India  \n2 Department of Dermatology, Jagannath Gupta Institute of Medical Sciences and Hospital, Kolkata, India  \n3 Department of Pediatric Dermatology, Institute of Child Health, Kolkata-700017, India  \n4 Department of Dermatology, Venereology, and Leprology, Postgraduate Institute of Medical Education and Research, Chandigarh-160012, India  \n5 Department of Dermatology and STD, University College of Medical Sciences & GTBH, University of Delhi, Delhi-110095, India  \n6 Department of DVL, Sree Balaji Medical College and Hospital, Chennai, India  \nJuly 3, 2026  \nAbstract  \nBackground: The development of artificial intelligence (AI) in dermatology has historically prioritized visual diagnostic algorithms, while other clinical and administrative components of chronic disease management have received comparatively less attention. This study utilized a problem-first methodology to map the clinical bottlenecks reported by Indian dermatologists—with a specific focus on atopic dermatitis (AD)—against their current adoption of AI tools.  \nMethods: In a study commissioned by the Society for Eczema Studies (SES), a nationwide, crosssectional survey was administered to practicing Indian dermatologists (N = 377) . The instrument evaluated technology-agnostic clinical frustrations and AD-specific management hurdles prior to assessing AI usage patterns, technological barriers, and ethical apprehensions. Data were analyzed using descriptive statistics, Pearsons Chi-square (χ2 ) tests, Benjamini-Hochberg False Discovery Rate (FDR) corrections, and multivariate logistic regression.  \nResults: Chronic disease management challenges, including patient adherence (61.3%) and treatment planning in difficult or refractory cases (57.0%), were reported more frequently than diagnostic uncertainty (48.0%) . In AD management, objective severity scoring (e.g., EASI/SCORAD) was commonly reported as a challenge (47.7%) and had the lowest satisfaction rate among the measured workflow areas. Current AI adoption was reported by 49 .9% of respondents and was most commonly represented by general Large Language Models (LLMs), used for tasks such as summarizing research and drafting clinical or academic content rather than specialized image analysis.  \nBarriers to adoption varied by experience level: veteran dermatologists (> 20 years practice) more frequently cited a lack of training (64.3%, p = 0 .019), whereas junior dermatologists (≤ 5 years) more frequently cited a lack of clinical utility after trying AI tools (22.8%, p = 0 .038) . AI users were also more likely than non-users to report concern regarding patient self-misdiagnosis and anxiety. After adjustment for clinical experience and academic affiliation, active AI usage remained independently associated with this concern (adjusted odds ratio [aOR]: 2.25, 95% CI: 1.45 – 3.50, p = 0 .0003) . AI users also expressed higher concern regarding use by non-dermatologists after FDR correction (padj = 0 .0251) .  \nConclusion: Indian dermatologists reported frequent use of general-purpose AI tools, particularly for literature synthesis, documentation, and academic tasks, while their highest reported clinical needs related to chronic disease management and AD workflow support. AI users were more likely than non-users to report concerns regarding patient self-diagnosis and non-specialist use. Future dermatology AI tools may be more clinically useful if developed around clinician-supervised workflow support rather than standalone diagnostic applications.  \n1 Introduction  \n1.1 The “Technology-Push” Dilemma in Dermatological AI  \nDermatology, an inherently visual and pattern-driven","cbCaijWpqjPl4jmh","https://ap.wps.com/l/cbCaijWpqjPl4jmh","pdf",150392,1,28,"English","en",105,"# Abstract\n# Introduction\n## The “Technology-Push” Dilemma in Dermatological AI\n## The Need for a “Problem-First” Framework","[{\"question\":\"What was the main aim of the nationwide survey of Indian dermatologists?\",\"answer\":\"To map clinical bottlenecks reported by Indian dermatologists—especially for atopic dermatitis—to their current adoption of AI tools, including usage patterns, barriers, and ethical concerns.\"},{\"question\":\"How did the study measure clinical frustrations and AI adoption?\",\"answer\":\"It used a cross-sectional, nationwide survey of 377 practicing dermatologists, first assessing technology-agnostic clinical frustrations and AD-specific hurdles, then evaluating AI usage patterns, barriers, and ethical apprehensions.\"},{\"question\":\"Which AI tools were most commonly adopted, and for what purposes?\",\"answer\":\"Current adoption was reported by 49.9% of respondents, most often involving general Large Language Models used for tasks such as summarizing research and drafting clinical or academic content rather than specialized image 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was the main aim of the nationwide survey of Indian dermatologists?","Question",{"text":74,"@type":75},"To map clinical bottlenecks reported by Indian dermatologists—especially for atopic dermatitis—to their current adoption of AI tools, including usage patterns, barriers, and ethical concerns.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How did the study measure clinical frustrations and AI adoption?",{"text":79,"@type":75},"It used a cross-sectional, nationwide survey of 377 practicing dermatologists, first assessing technology-agnostic clinical frustrations and AD-specific hurdles, then evaluating AI usage patterns, barriers, and ethical apprehensions.",{"name":81,"@type":72,"acceptedAnswer":82},"Which AI tools were most commonly adopted, and for what purposes?",{"text":83,"@type":75},"Current adoption was reported by 49.9% of respondents, most often involving general Large Language Models used for tasks such as summarizing research and drafting clinical or academic 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