2f:I[703058,["/_next/static/immutable/chunks/3bhq_5eh1d760.js","/_next/static/immutable/chunks/1855-ogyi8eh4.js","/_next/static/immutable/chunks/2pfsjpn6k6zlg.js","/_next/static/immutable/chunks/26c7v3_3uwnwu.js","/_next/static/immutable/chunks/25a3ozcrvi1ff.js","/_next/static/immutable/chunks/2r4wx50nvjh_4.js","/_next/static/immutable/chunks/23fsw06hsi5b1.js","/_next/static/immutable/chunks/06_yt270h41rg.js","/_next/static/immutable/chunks/3ezsiz-7shkna.js","/_next/static/immutable/chunks/20x2y96ldu5wx.js","/_next/static/immutable/chunks/2lxntcm3pd1_h.js","/_next/static/immutable/chunks/00dgco8irq3jw.js","/_next/static/immutable/chunks/00nwe-kq0rboc.js","/_next/static/immutable/chunks/2051ffx3xzio9.js"],"PaperListPageClient"] 30:Tb9b,Histopathology with light microscopy is the reference standard for cellular evaluation of solid tissue, but whether noninvasive cross-modal imaging of skin provides clinicians with comparable histologic information that may assist in clinical decision-making is not well known. To evaluate safety and effectiveness of cross-modal imaging in obtaining in vivo images, to demonstrate that images align with corresponding skin histopathology, and to evaluate the ability of blinded readers to accurately identify tissue features on cross-modal images. This observational diagnostic study included 1 visit and follow-up call. Eligible participants were adults aged 18 to 99 years seen for routine care in the outpatient dermatology setting with an indication for lesional skin biopsy. The study was conducted from October 20, 2022, to August 11, 2023, at 2 outpatient dermatology clinics in the US. Cross-modal images and skin biopsy were collected from 1 lesion per participant. Participants were randomized to training (40%) and validation sets (60%). Comparative readers used the training set to evaluate cross-modal images against hematoxylin and eosin histopathology to validate tissue features and train blinded physician readers. Comparative readers developed a performance test with the validation set; blinded physician readers were tested on cross-modal feature identification without access to other participant data. All participants underwent cross-modal imaging before biopsy. The primary end points were 100% comparative reader agreement and validation of cross-modal image tissue features against histopathology and 90% or greater accuracy of blinded physician readers identification of primary tissue features against comparative reader annotation. Secondary end points included accuracy of blinded physician reader identification of secondary features and greater than 90% interrater agreement between blinded physician readers. The safety end point was the number of adverse events observed. A total of 65 participants (median age, 69 [range 20-93] years; 41.5% female and 58.5% male; 1.5% American Indian or Alaska Native; 13.8% Hispanic or Latino; 86.2% not Hispanic or Latino; 98.5% White) underwent cross-modal imaging. Comparative readers achieved 100% consensus and validation of cross-modal histologic features compared with histopathology. Blinded physician reader accuracy for primary histologic features was 96.4% (95% CI, 94.2%-98.7%) and for secondary histologic features was 98.5% (95% CI, 98.1%-98.9%). Interrater agreement among the blinded physician readers was high (Fleiss κ: region, 0.94 [95% CI, 0.87-1.0]; feature, 0.93 [95% CI, 0.88-0.97]). No adverse events were reported. The results of this study suggest the safety of cross-modal imaging and that trained physicians may accurately identify histologic features on cross-modal images, consistent with its US Food and Drug Administration-cleared role in assisting clinical judgment.31:T1070, Importance Histopathology with light microscopy is the reference standard for cellular evaluation of solid tissue, but whether noninvasive cross-modal imaging of skin provides clinicians with comparable histologic information that may assist in clinical decision-making is not well known. Objective To evaluate safety and effectiveness of cross-modal imaging in obtaining in vivo images, to demonstrate that images align with corresponding skin histopathology, and to evaluate the ability of blinded readers to accurately identify tissue features on cross-modal images. Design, Setting, and Participants This observational diagnostic study included 1 visit and follow-up call. Eligible participants were adults aged 18 to 99 years seen for routine care in the outpatient dermatology setting with an indication for lesional skin biopsy. The study was conducted from October 20, 2022, to August 11, 2023, at 2 outpatient dermatology clinics in the US. Cross-modal images and skin biopsy were collected from 1 lesion per participant. Participants were randomized to training (40%) and validation sets (60%). Comparative readers used the training set to evaluate cross-modal images against hematoxylin and eosin histopathology to validate tissue features and train blinded physician readers. Comparative readers developed a performance test with the validation set; blinded physician readers were tested on cross-modal feature identification without access to other participant data. Exposure All participants underwent cross-modal imaging before biopsy. Main Outcomes and Measures The primary end points were 100% comparative reader agreement and validation of cross-modal image tissue features against histopathology and 90% or greater accuracy of blinded physician readers identification of primary tissue features against comparative reader annotation. Secondary end points included accuracy of blinded physician reader identification of secondary features and greater than 90% interrater agreement between blinded physician readers. The safety end point was the number of adverse events observed. Results A total of 65 participants (median age, 69 [range 20-93] years; 41.5% female and 58.5% male; 1.5% American Indian or Alaska Native; 13.8% Hispanic or Latino; 86.2% not Hispanic or Latino; 98.5% White) underwent cross-modal imaging. Comparative readers achieved 100% consensus and validation of cross-modal histologic features compared with histopathology. Blinded physician reader accuracy for primary histologic features was 96.4% (95% CI, 94.2%-98.7%) and for secondary histologic features was 98.5% (95% CI, 98.1%-98.9%). Interrater agreement among the blinded physician readers was high (Fleiss κ: region, 0.94 [95% CI, 0.87-1.0]; feature, 0.93 [95% CI, 0.88-0.97]). No adverse events were reported. Conclusions and Relevance The results of this study suggest the safety of cross-modal imaging and that trained physicians may accurately identify histologic features on cross-modal images, consistent with its US Food and Drug Administration–cleared role in assisting clinical judgment. 25:[[["$","script","0",{"type":"application/ld+json","dangerouslySetInnerHTML":{"__html":"{\"@context\":\"https://schema.org\",\"@type\":\"CollectionPage\",\"name\":\"Skin Lesion: condition Research & Clinical Takeaways\",\"description\":\"Explore peer-reviewed papers on Skin Lesion (condition). Clinician-written summaries and actionable practice insights. Updated daily.\",\"url\":\"https://www.reachrx.ai/hub/tag/condition/Skin%20Lesion\",\"breadcrumb\":{\"@type\":\"BreadcrumbList\",\"itemListElement\":[{\"@type\":\"ListItem\",\"position\":1,\"name\":\"Home\",\"item\":\"https://www.reachrx.ai\"},{\"@type\":\"ListItem\",\"position\":2,\"name\":\"Research Hub\",\"item\":\"https://www.reachrx.ai/hub\"},{\"@type\":\"ListItem\",\"position\":3,\"name\":\"Conditions\",\"item\":\"https://www.reachrx.ai/hub/tag/condition\"},{\"@type\":\"ListItem\",\"position\":4,\"name\":\"Skin Lesion\",\"item\":\"https://www.reachrx.ai/hub/tag/condition/Skin%20Lesion\"}]}}"}}]],["$","$L2f",null,{"title":"Skin Lesion: Research & Clinical Takeaways","description":"Discover peer-reviewed research on Skin Lesion with concise clinician summaries and next-step guidance.","papers":[{"doi":"10.1001/jamadermatol.2026.1492","pmid":"42234423","title":"Limits of Artificial Intelligence Models for Skin Cancer Diagnosis in Realistic Settings","journal_title":"JAMA Dermatology","journal_issn":"2168-6068","published_at":"2026-06-03T00:00:00.000Z","url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13234745/","source_score":29,"is_retracted":false,"is_open_access":true,"raw_metadata":{"pubmed":true,"crossref":true,"unpaywall":true},"created_at":"2026-06-10T14:23:42.253Z","updated_at":"2026-06-10T14:23:42.253Z","tags":[{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"age_group","value":"adult","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"brand","value":"PanDerm","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"category","value":"Dermatology","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"condition","value":"Skin Cancer","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"condition","value":"Skin Lesion","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"impact_flag","value":"limited_evidence","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"level_of_evidence","value":"3","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"modality","value":"Artificial Intelligence / Machine Learning","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"modality","value":"Convolutional Neural Network","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"modality","value":"Foundation Model","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"sample_size","value":"n=1117","value_numeric":1117},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"study_design","value":"Diagnostic Accuracy Study","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"warning","value":"Full text was inaccessible; summary is based solely on the abstract and results excerpt. Key methodological details (lesion prevalence, histopathologic confirmation, case mix) could not be verified.","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"warning","value":"PanDerm is tagged as a brand; it is an AI model name, not a pharmaceutical product — clinical context differs from typical drug branding.","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"warning","value":"The 95% CI for the CNN vs. all-human-readers comparison (-9.8 to 8.5 pp) includes zero, which raises questions about statistical interpretation despite the reported P < .001 — this apparent inconsistency could not be resolved without full text access.","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2026.1492","tag_type":"warning","value":"The study population of physician readers was 85.7% female, which is atypical and may affect generalizability of human performance benchmarks.","value_numeric":null}],"summaries":{"what_studied":"This multi-institutional diagnostic study compared multiclass skin lesion classification accuracy of three AI models (a CNN and two foundation models: PanDerm unimodal and multimodal) against 652 physicians across varying experience levels (<1 to >10 years), using 1,117 real-world clinical and dermoscopic images with metadata (March 2023–August 2025).","key_result":"Expert dermatologists (>10 years) achieved the highest accuracy at 74.2%, outperforming all AI models. The unimodal foundation model (72.2%) beat readers with <3 years' experience (68.2%) and matched those with 3–10 years, but fell short of experts. The CNN (56.7%) was outperformed by all human readers.","limitations":"Human readers evaluated only 100 stratified random cases (vs. the full 1,117), limiting direct comparison scope. The study population was 85.7% female physicians, which may not reflect typical clinical workforces. Full text was not available for review, so methodological details (e.g., lesion prevalence, biopsy confirmation) could not be verified.","practice_takeaway":"Current AI foundation models for skin lesion diagnosis can match mid-level clinicians (<3–10 years' experience) but should not replace expert dermatologists. Deploying AI as a triage or support tool for less experienced clinicians may add value, but caution is warranted before broader clinical rollout."}},{"doi":"10.1001/jamadermatol.2025.4318","pmid":"41191381","title":"Cross-Modal Imaging in Noninvasive Identification of Histologic Features of Skin","journal_title":"JAMA Dermatology","journal_issn":"2168-6068","published_at":"2025-11-05T00:00:00.000Z","url":"https://doi.org/10.1001/jamadermatol.2025.4318","source_score":102,"is_retracted":false,"is_open_access":true,"raw_metadata":{"ooir":{"day":"2025-11-10","doi":"10.1001/jamadermatol.2025.4318","days":"30","issn":"2168-6068","rank":"4","field":"Clinical Medicine","score":"102","category":"Dermatology","categories":["Dermatology"],"_categories":["Dermatology"]},"pubmed":{"doi":"10.1001/jamadermatol.2025.4318","title":"Cross-Modal Imaging in Noninvasive Identification of Histologic Features of Skin.","journal":"JAMA dermatology","methods":"","results":"A total of 65 participants (median age, 69 [range 20-93] years; 41.5% female and 58.5% male; 1.5% American Indian or Alaska Native; 13.8% Hispanic or Latino; 86.2% not Hispanic or Latino; 98.5% White) underwent cross-modal imaging. Comparative readers achieved 100% consensus and validation of cross-modal histologic features compared with histopathology. Blinded physician reader accuracy for primary histologic features was 96.4% (95% CI, 94.2%-98.7%) and for secondary histologic features was 98.5% (95% CI, 98.1%-98.9%). Interrater agreement among the blinded physician readers was high (Fleiss κ: region, 0.94 [95% CI, 0.87-1.0]; feature, 0.93 [95% CI, 0.88-0.97]). No adverse events were reported.","abstract":"$30","keywords":[],"pubmed_id":"41191381","conclusions":"","publication_date":"2025-11-05T00:00:00"},"crossref":{"DOI":"10.1001/jamadermatol.2025.4318","URL":"https://doi.org/10.1001/jamadermatol.2025.4318","ISSN":["2168-6068"],"link":[{"URL":"https://jamanetwork.com/journals/jamadermatology/articlepdf/2841136/jamadermatology_arron_2025_oi_250054_1762179606.25058.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"page":null,"type":"journal-article","issue":null,"score":1,"title":["Cross-Modal Imaging in Noninvasive Identification of Histologic Features of Skin"],"author":[{"given":"Sarah T.","family":"Arron","sequence":"first","affiliation":[{"name":"Palo Alto Foundation Medical Group, Palo Alto, California"}]},{"given":"Afton","family":"Cobb","sequence":"additional","affiliation":[{"name":"Advanced Dermatology, Jackson, Wyoming"}]},{"given":"Lilia M.","family":"Correa-Selm","sequence":"additional","affiliation":[{"name":"Morsani College of Medicine, University of South Florida, Tampa"},{"name":"Moffitt Cancer Center, Tampa, Florida"}]},{"given":"Katherine M.","family":"Given","sequence":"additional","affiliation":[{"name":"Given Dermatology Institute, Menlo Park, California"},{"name":"Department of Dermatology, University of California, San Francisco"}]},{"given":"Manu","family":"Jain","sequence":"additional","affiliation":[{"name":"Memorial Sloan Kettering Cancer Center, New York City, New York"}]},{"given":"David","family":"Pilkington","sequence":"additional","affiliation":[{"name":"Golden State Dermatology, Merced, California"}]},{"given":"Jennifer Y.","family":"Wang","sequence":"additional","affiliation":[{"name":"Golden State Dermatology, Merced, California"},{"name":"Stanford University School of Medicine, Stanford, California"}]},{"given":"Michael Z.","family":"Wang","sequence":"additional","affiliation":[{"name":"Golden State Dermatology, Walnut Creek, California"}]}],"issued":{"date-parts":[[2025,11,5]]},"member":"10","prefix":"10.1001","source":"Crossref","volume":null,"created":{"date-time":"2025-11-05T16:00:42Z","timestamp":1762358442000,"date-parts":[[2025,11,5]]},"indexed":{"date-time":"2025-11-05T16:02:38Z","timestamp":1762358558287,"date-parts":[[2025,11,5]]},"subject":[],"abstract":"$31","language":"en","relation":{},"resource":{"primary":{"URL":"https://jamanetwork.com/journals/jamadermatology/fullarticle/2841136"}},"subtitle":[],"deposited":{"date-time":"2025-11-05T16:00:42Z","timestamp":1762358442000,"date-parts":[[2025,11,5]]},"issn-type":[{"type":"print","value":"2168-6068"}],"published":{"date-parts":[[2025,11,5]]},"publisher":"American Medical Association (AMA)","reference":[{"DOI":"10.1159/000118508","key":"doi250054r1","year":"2008","issue":"1","author":"Mogensen","volume":"217","first-page":"14","unstructured":null,"article-title":"Morphology and epidermal thickness of normal skin imaged by optical coherence tomography.","journal-title":"Dermatology","doi-asserted-by":"publisher"},{"DOI":"10.1117/1.JBO.23.10.106007","key":"doi250054r2","year":"2018","issue":"10","author":"Dubois","volume":"23","first-page":"1","unstructured":null,"article-title":"Line-field confocal optical coherence tomography for high-resolution noninvasive imaging of skin tumors.","journal-title":"J Biomed Opt","doi-asserted-by":"publisher"},{"DOI":"10.1111/srt.12949","key":"doi250054r3","year":"2021","issue":"3","author":"Ruini","volume":"27","first-page":"340","unstructured":null,"article-title":"Line-field confocal optical coherence tomography-Practical applications in dermatology and comparison with established imaging methods.","journal-title":"Skin Res Technol","doi-asserted-by":"publisher"},{"DOI":"10.1016/S0929-8266(00)00097-5","key":"doi250054r4","year":"2000","issue":"1","author":"Szymanska","volume":"12","first-page":"9","unstructured":null,"article-title":"Skin imaging with high frequency ultrasound - preliminary results.","journal-title":"Eur J Ultrasound","doi-asserted-by":"publisher"},{"DOI":"10.1177/1534734620972815","key":"doi250054r5","year":"2020","issue":"4","author":"Izzetti","volume":"19","first-page":"334","unstructured":null,"article-title":"The application of ultra-high-frequency ultrasound in dermatology and wound management.","journal-title":"Int J Low Extrem Wounds","doi-asserted-by":"publisher"},{"DOI":"10.1111/1523-1747.ep12606215","key":"doi250054r6","year":"1995","issue":"6","author":"Rajadhyaksha","volume":"104","first-page":"946","unstructured":null,"article-title":"In vivo confocal scanning laser microscopy of human skin: melanin provides strong contrast.","journal-title":"J Invest Dermatol","doi-asserted-by":"publisher"},{"DOI":"10.3390/diagnostics13050854","key":"doi250054r7","year":"2023","issue":"5","author":"Atak","volume":"13","first-page":"854","unstructured":null,"article-title":"Confocal microscopy for diagnosis and management of cutaneous malignancies: clinical impacts and innovation.","journal-title":"Diagnostics (Basel)","doi-asserted-by":"publisher"},{"DOI":"10.1002/jbio.200910020","key":"doi250054r8","year":"2009","issue":"6-7","author":"Mogensen","volume":"2","first-page":"442","unstructured":null,"article-title":"OCT imaging of skin cancer and other dermatological diseases.","journal-title":"J Biophotonics","doi-asserted-by":"publisher"},{"DOI":"10.3390/diagnostics14141522","key":"doi250054r9","year":"2024","issue":"14","author":"Razi","volume":"14","first-page":"1522","unstructured":null,"article-title":"Line-field confocal optical coherence tomography for the diagnosis of skin tumors: a systematic review and meta-analysis.","journal-title":"Diagnostics (Basel)","doi-asserted-by":"publisher"},{"DOI":"10.1158/0008-5472.CAN-08-0006","key":"doi250054r10","year":"2008","issue":"20","author":"Banihashemi","volume":"68","first-page":"8590","unstructured":null,"article-title":"Ultrasound imaging of apoptosis in tumor response: novel preclinical monitoring of photodynamic therapy effects.","journal-title":"Cancer Res","doi-asserted-by":"publisher"},{"DOI":"10.1002/14651858.CD013188","key":"doi250054r11","year":"2018","issue":"12","author":"Dinnes","volume":"12","first-page":null,"unstructured":null,"article-title":"High-frequency ultrasound for diagnosing skin cancer in adults.","journal-title":"Cochrane Database Syst Rev","doi-asserted-by":"publisher"},{"DOI":"10.1016/j.jaad.2019.03.085","key":"doi250054r12","year":"2019","issue":"2","author":"Pellacani","volume":"81","first-page":"520","unstructured":null,"article-title":"Reflectance confocal microscopy made easy: the 4 must-know key features for the diagnosis of melanoma and nonmelanoma skin cancers.","journal-title":"J Am Acad Dermatol","doi-asserted-by":"publisher"},{"DOI":"10.1001/jamadermatol.2022.1570","key":"doi250054r13","year":"2022","issue":"7","author":"Pellacani","volume":"158","first-page":"754","unstructured":null,"article-title":"Effect of reflectance confocal microscopy for suspect lesions on diagnostic accuracy in melanoma: a randomized clinical trial.","journal-title":"JAMA Dermatol","doi-asserted-by":"publisher"},{"DOI":"10.1111/exd.14830","key":"doi250054r14","year":"2023","issue":"7","author":"Al-Chaer","volume":"32","first-page":"945","unstructured":null,"article-title":"Confocal microscopy and optical coherence tomography of inflammatory skin diseases in hairs and pilosebaceous units: A systematic review.","journal-title":"Exp Dermatol","doi-asserted-by":"publisher"},{"DOI":"10.23736/S2784-8671.23.07607-7","key":"doi250054r15","year":"2023","issue":"3","author":"Donelli","volume":"158","first-page":"190","unstructured":null,"article-title":"Inflammatory and infectious skin diseases in line-field confocal optical coherence tomography: state of the art.","journal-title":"Ital J Dermatol Venerol","doi-asserted-by":"publisher"},{"DOI":"10.1016/j.ad.2016.01.010","key":"doi250054r16","year":"2016","issue":"8","author":"Agozzino","volume":"107","first-page":"631","unstructured":null,"article-title":"Reflectance confocal microscopy for inflammatory skin diseases.","journal-title":"Actas Dermosifiliogr","doi-asserted-by":"publisher"},{"DOI":"10.1097/DSS.0000000000000457","key":"doi250054r17","year":"2015","issue":"9","author":"Mamalis","volume":"41","first-page":"993","unstructured":null,"article-title":"Optical coherence tomography imaging of normal, chronologically aged, photoaged and photodamaged skin: a systematic review.","journal-title":"Dermatol Surg","doi-asserted-by":"publisher"},{"DOI":"10.1038/s41598-023-40340-0","key":"doi250054r18","year":"2023","issue":"1","author":"Bonnier","volume":"13","first-page":"13881","unstructured":null,"article-title":"Line-field confocal optical coherence tomography coupled with artificial intelligence algorithms to identify quantitative biomarkers of facial skin ageing.","journal-title":"Sci Rep","doi-asserted-by":"publisher"},{"DOI":"10.1016/j.jdcr.2018.09.019","key":"doi250054r19","year":"2018","issue":"10","author":"Levine","volume":"4","first-page":"1014","unstructured":null,"article-title":"Introduction to reflectance confocal microscopy and its use in clinical practice.","journal-title":"JAAD Case Rep","doi-asserted-by":"publisher"},{"DOI":"10.1001/jamadermatol.2018.1668","key":"doi250054r20","year":"2018","issue":"8","author":"Jain","volume":"154","first-page":"962","unstructured":null,"article-title":"Evaluation of bedside diagnostic accuracy, learning curve, and challenges for a novice reflectance confocal microscopy reader for skin cancer detection in vivo.","journal-title":"JAMA Dermatol","doi-asserted-by":"publisher"},{"DOI":"10.1093/ced/llad422","key":"doi250054r21","year":"2024","issue":"4","author":"Navarrete-Dechent","volume":"49","first-page":"408","unstructured":null,"article-title":"Continuing Medical Education-accredited reflectance confocal microscopy course in the USA and its impact on learning and clinical integration: report from a single centre.","journal-title":"Clin Exp Dermatol","doi-asserted-by":"publisher"},{"DOI":"10.1038/s41598-024-76908-7","key":"doi250054r22","year":"2024","issue":"1","author":"Montgomery","volume":"14","first-page":"26129","unstructured":null,"article-title":"Handheld multiphoton and pinhole-free reflectance confocal microscopy enables noninvasive, real-time cross-sectional imaging in skin.","journal-title":"Sci Rep","doi-asserted-by":"publisher"},{"DOI":"10.1001/jamadermatol.2015.0810","key":"doi250054r23","year":"2015","issue":"10","author":"Farnetani","volume":"151","first-page":"1075","unstructured":null,"article-title":"Skin cancer diagnosis with Reflectance confocal microscopy: Reproducibility of feature recognition and accuracy of diagnosis.","journal-title":"JAMA Dermatol","doi-asserted-by":"publisher"},{"DOI":"10.3389/fonc.2021.653140","key":"doi250054r24","year":"2021","issue":null,"author":"Broggi","volume":"11","first-page":null,"unstructured":null,"article-title":"Correlation between in vivo reflectance confocal microscopy and horizontal histopathology in skin cancer: a review.","journal-title":"Front Oncol","doi-asserted-by":"publisher"},{"DOI":"10.1016/S0006-3495(97)78886-6","key":"doi250054r25","year":"1997","issue":"6","author":"Masters","volume":"72","first-page":"2405","unstructured":null,"article-title":"Multiphoton excitation fluorescence microscopy and spectroscopy of in vivo human skin.","journal-title":"Biophys J","doi-asserted-by":"publisher"},{"DOI":"10.1117/1.1577349","key":"doi250054r26","year":"2003","issue":"3","author":"Konig","volume":"8","first-page":"432","unstructured":null,"article-title":"High-resolution multiphoton tomography of human skin with subcellular spatial resolution and picosecond time resolution.","journal-title":"J Biomed Opt","doi-asserted-by":"publisher"},{"DOI":"10.1111/exd.14899","key":"doi250054r27","year":"2023","issue":"10","author":"Chen","volume":"32","first-page":"1613","unstructured":null,"article-title":"Submicron resolution techniques: multiphoton microscopy in skin disease.","journal-title":"Exp Dermatol","doi-asserted-by":"publisher"},{"DOI":null,"key":"doi250054r28","year":"1988","issue":"1","author":"Wong","volume":"14","first-page":"9","unstructured":null,"article-title":"Pain in children: comparison of assessment scales.","journal-title":"Pediatr Nurs","doi-asserted-by":null}],"short-title":[],"content-domain":{"domain":[],"crossmark-restriction":false},"original-title":[],"container-title":["JAMA Dermatology"],"reference-count":28,"published-online":{"date-time":null,"timestamp":null,"date-parts":[[2025,11,5]]},"references-count":28,"short-container-title":["JAMA Dermatol"],"is-referenced-by-count":0},"unpaywall":{"doi":"10.1001/jamadermatol.2025.4318","is_oa":true,"title":"Cross-Modal Imaging in Noninvasive Identification of Histologic Features of Skin","updated":"2025-11-07T00:31:01+00:00","published_date":"2025-11-05","best_oa_location":{"url":"https://jamanetwork.com/journals/jamadermatology/articlepdf/2841136/jamadermatology_arron_2025_oi_250054_1762179606.25058.pdf","pmh_id":null,"url_for_pdf":"https://jamanetwork.com/journals/jamadermatology/articlepdf/2841136/jamadermatology_arron_2025_oi_250054_1762179606.25058.pdf","url_for_landing_page":"https://doi.org/10.1001/jamadermatol.2025.4318"}}},"created_at":"2025-11-12T12:39:36.952Z","updated_at":"2025-12-08T12:24:58.509Z","tags":[{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"age_group","value":"adult","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"age_group","value":"older_adult","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"category","value":"Dermatology","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"condition","value":"Skin Lesion","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"impact_flag","value":"limited_evidence","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"sample_size","value":"n=65","value_numeric":65},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"sex","value":"mixed","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"study_design","value":"Diagnostic Accuracy Study","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"warning","value":"Design classified as a diagnostic accuracy study; the article describes it as an observational diagnostic study.","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"warning","value":"Performance was assessed for histologic feature identification, not disease‑level diagnostic accuracy.","value_numeric":null},{"paper_doi":"10.1001/jamadermatol.2025.4318","tag_type":"warning","value":"The term randomization refers to training/validation dataset split, not allocation to interventions.","value_numeric":null}],"summaries":{"what_studied":"An observational diagnostic study at 2 US outpatient dermatology clinics (Oct 20, 2022–Aug 11, 2023) evaluated cross‑modal skin imaging performed before biopsy in adults with an indication for lesional biopsy. Comparative readers aligned cross‑modal images with hematoxylin‑eosin histopathology to train and then test blinded physicians on image‑based identification of histologic features (training 40%, validation 60%).","key_result":"Among 65 participants, comparative readers achieved 100% consensus validating cross‑modal histologic features against histopathology. Blinded physicians identified primary features with 96.4% accuracy (95% CI, 94.2%–98.7%) and secondary features with 98.5% accuracy (95% CI, 98.1%–98.9%); interrater agreement was high (Fleiss κ: region, 0.94 [95% CI, 0.87–1.0]; feature, 0.93 [95% CI, 0.88–0.97]), and no adverse events occurred.","limitations":"Small sample (n=65) from 2 clinics and predominantly White participants (98.5%) limit generalizability. Single‑visit design with one lesion per participant.","practice_takeaway":"Cross‑modal imaging appeared safe, and trained physicians accurately recognized histologic features on these images, supporting its FDA‑cleared role as an aid to clinical judgment."}}],"initialOffset":2,"fetchConfig":{"kind":"tags","tags":[{"tag_type":"condition","value":"Skin Lesion"}]},"breadcrumbItems":[{"name":"Home","href":"/"},{"name":"Research Hub","href":"/hub"},{"name":"Conditions","href":"/hub/tag/condition"},{"name":"Skin Lesion"}],"hrefConfig":{"basePath":"/hub/papers","webViewQueryParams":{"hub_ctx":"tag","hub_tag_type":"condition","hub_tag_value":"Skin Lesion"}}}]]

Papers