Clinics in dermatology最新文献

英文中文

Ultrasonographic features of nonvascular complications of hyaluronic acid fillers: a retrospective study at a reference center for dermatologic ultrasonography 透明质酸填充剂非血管并发症的超声波特征：皮肤科超声波参考中心的回顾性研究。

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.05.006

Hyaluronic acid filler injections have been associated with early, temporary, and delayed-onset complications. High-resolution ultrasound with Doppler analysis has been increasingly used to detect and identify such complications. We comprehensively describe the most common ultrasonographic findings of nonvascular complications associated with hyaluronic acid filler injections. This retrospective, cross-sectional, observational study was conducted at a reference center for dermatologic ultrasound in Bogotá, Colombia. Ultrasound reports documented the ultrasonographic findings of nonvascular complications of hyaluronic acid filler injections. Fifty-two complications were documented in a cohort of 50 patients (women, 88%). The infraorbital region was the most common site affected (23%), followed by the nasolabial region (22%). The Tyndall effect was the most common complication (25% of all), followed by changes in rheology (21%) and pseudosarcoidal (foreign body granuloma) reaction (15%). The Tyndall effect stood out for its distinctive ultrasonographic characteristics. We discuss the ultrasonographic findings and pathogenesis of other complications, including filler migration, early hypersensitivity, aseptic abscess, overcorrection, and filler material interaction. The clinical presentation of hyaluronic acid filler complications can be confusing, delaying timely diagnosis and treatment. High-resolution ultrasound with Doppler analysis is a valuable tool for avoiding unnecessary treatments and ensuring timely diagnosis and treatment.

透明质酸（H.A.）填充剂注射与早期、暂时和延迟发病的并发症有关。带有多普勒分析的高分辨率超声波（HRUS）越来越多地被用于检测和识别此类并发症。我们全面描述了与注射 H.A. 填充剂相关的非血管并发症中最常见的超声波检查结果。这项回顾性横断面观察研究是在哥伦比亚波哥大的一家皮肤科超声参考中心进行的。超声波报告记录了注射 H.A. 填充剂后非血管并发症的超声波检查结果。在 50 名患者（女性占 88%）中，共记录了 52 例并发症。眶下区域是最常见的受影响部位（23%），其次是鼻唇部（22%）。廷德尔效应是最常见的并发症（占 25%），其次是流变学变化（21%）和假性肉芽肿（异物肉芽肿）反应（15%）。廷德尔效应因其独特的超声造影特征而引人注目。我们讨论了其他并发症的超声波检查结果和发病机制，包括填充物迁移、早期过敏、无菌性脓肿、过度矫正和填充材料相互作用。H.A.填充并发症的临床表现可能令人困惑，从而延误及时的诊断和治疗。带有多普勒分析的 HRUS 是避免不必要治疗、确保及时诊断和治疗的重要工具。

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引用次数: 0

Higher detection of melanoma on the back in married men 已婚男性背部黑色素瘤的检出率更高

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.06.001

引用次数: 0

Artificial intelligence for nonmelanoma skin cancer 人工智能治疗非黑色素瘤皮肤癌。

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.06.016

Nonmelanoma skin cancers (NMSCs) are among the top five most common cancers globally. NMSC is an area with great potential for novel application of diagnostic tools including artificial intelligence (AI). In this scoping review, we aimed to describe the applications of AI in the diagnosis and treatment of NMSC. Twenty-nine publications described AI applications to dermatopathology including lesion classification and margin assessment. Twenty-five publications discussed AI use in clinical image analysis, showing that algorithms are not superior to dermatologists and may rely on unbalanced, nonrepresentative, and nontransparent training data sets. Sixteen publications described the use of AI in cutaneous surgery for NMSC including use in margin assessment during excisions and Mohs surgery, as well as predicting procedural complexity. Eleven publications discussed spectroscopy, confocal microscopy, thermography, and the AI algorithms that analyze and interpret their data. Ten publications pertained to AI applications for the discovery and use of NMSC biomarkers. Eight publications discussed the use of smartphones and AI, specifically how they enable clinicians and patients to have increased access to instant dermatologic assessments but with varying accuracies. Five publications discussed large language models and NMSC, including how they may facilitate or hinder patient education and medical decision-making. Three publications pertaining to the skin of color and AI for NMSC discussed concerns regarding limited diverse data sets for the training of convolutional neural networks. AI demonstrates tremendous potential to improve diagnosis, patient and clinician education, and management of NMSC. Despite excitement regarding AI, data sets are often not transparently reported, may include low-quality images, and may not include diverse skin types, limiting generalizability. AI may serve as a tool to increase access to dermatology services for patients in rural areas and save health care dollars. These benefits can only be achieved, however, with consideration of potential ethical costs.

非黑色素瘤皮肤癌（NMSC）是全球最常见的五大癌症之一。非黑色素瘤皮肤癌是一个极有潜力应用包括人工智能（AI）在内的新型诊断工具的领域。在这篇范围综述中，我们旨在描述人工智能在NMSC诊断和治疗中的应用。29 篇文献介绍了人工智能在皮肤病理学中的应用，包括病变分类和边缘评估。25篇论文讨论了人工智能在临床图像分析中的应用，表明算法并不优于皮肤科医生，而且可能依赖于不平衡、无代表性和不透明的训练数据集。有 16 篇论文介绍了人工智能在 NMSC 皮肤手术中的应用，包括在切除术和莫氏手术中的边缘评估以及手术复杂性预测中的应用。11 篇出版物讨论了光谱学、共聚焦显微镜和热成像技术以及分析和解释其数据的人工智能算法。十篇出版物涉及人工智能在发现和利用非多发性硬化细胞生物标记物方面的应用。八篇论文讨论了智能手机和人工智能的使用，特别是它们如何使临床医生和患者有更多机会获得即时皮肤病评估，但准确性各不相同。五篇论文讨论了大型语言模型和 NMSC，包括它们如何促进或阻碍患者教育和医疗决策。三篇关于有色人种皮肤和 NMSC 人工智能的文章讨论了用于训练 CNN 的有限多样化数据集的问题。人工智能在改善 NMSC 的诊断、患者和临床医生教育及管理方面具有巨大潜力。尽管人工智能令人兴奋，但数据集往往没有透明的报告，可能包括低质量的图像，也可能不包括不同的皮肤类型，从而限制了普适性。人工智能可以作为一种工具，增加农村地区患者获得皮肤科服务的机会，并节省医疗费用。然而，只有考虑到潜在的道德成本，才能实现这些益处。

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引用次数: 0

Artificial intelligence in dermatology: Bridging the gap in patient care and education 皮肤病学中的人工智能：缩小患者护理和教育方面的差距。

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.06.009

The application of artificial intelligence (AI) in education and clinical medicine has shown tremendous growth. The primary explanation for this application is AI's ability to integrate efficient and tailored methods for screening, using diagnostics, and enhancement of patient and medical education. AI's wide scope of utility can be seen through its ability to improve efficiency in clinical settings through scheduling, charting, diagnostics, and screening tools, ultimately allowing physicians to spend more focused time on patient care. AI has also had a tangible impact on promoting patient education through its ability to provide patients with preliminary information regarding their diagnoses before followup and to further discussion with their physician. AI's application in medical education is promising due to its ability to provide immediate and interactive feedback to the learner, which allows for meaningful reinforcement of knowledge. AI can therefore be recognized as a tool that can provide incredible enhancement in the areas of clinical medicine and education, with meaningful opportunities for integration and application.

人工智能（AI）在教育和临床医学中的应用呈现出巨大的增长势头。这种应用的主要原因是人工智能能够整合高效和量身定制的筛查方法，利用诊断以及加强患者和医疗教育。人工智能的广泛实用性体现在它能够通过日程安排、制表、诊断和筛查工具提高临床环境的效率，最终使医生能够将更多的时间集中在病人护理上。人工智能还能为患者提供有关诊断的初步信息，从而在后续治疗和与医生进一步讨论之前，对促进患者教育产生切实影响。同样，人工智能在医学教育中的应用也大有可为，因为它能够为学习者提供即时和互动的反馈，从而对知识进行有意义的强化。综上所述，可以认为人工智能是一种可以在临床医学和教育领域提供令人难以置信的增强功能的工具，并提供有意义的整合和应用机会。

引用次数: 0

Artificial intelligence and skin melanoma 人工智能与皮肤黑色素瘤

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.06.015

Melanoma is the deadliest skin cancer, presenting typically with changing pigmented areas and usually treated with surgical removal. As benign cutaneous pigmented lesions are very common in all populations, it can be challenging to identify which areas should be cut out or left untreated. Delayed treatment in melanoma increases the risk of death, but it is not possible to remove all lesions. Dermatoscopy uses polarized light and can be used to help distinguish melanomas from benign lesions. Dermatoscopy images with a confirmed diagnosis can be used to develop artificial intelligence (AI) as a medical device (AIaMD) tool. This contribution discusses the utilization of AI in melanoma management and describes an AIaMD tool used in current UK clinical practice on more than 80,000 patients. This is a springboard for discussing the scope, risks, and mitigations for future AI use by all clinicians involved in managing people with melanoma.

黑色素瘤是最致命的皮肤癌，通常表现为色素区域的变化，通常通过手术切除治疗。由于良性皮肤色素性病变在所有人群中都很常见，因此确定哪些部位应该切除或不做治疗是一项挑战。延迟治疗黑色素瘤会增加死亡风险，但不可能切除所有病变。皮肤镜使用偏振光，可用于帮助区分黑色素瘤和良性病变。经确诊的皮肤镜图像可用于开发人工智能医疗设备（AIaMD）工具。本文将讨论人工智能（AI）在黑色素瘤管理中的应用，并介绍一种人工智能医疗设备（AIaMD）工具，该工具目前已在英国临床实践中用于8万多名患者。以此为跳板，所有参与黑色素瘤患者管理的临床医生可以讨论未来人工智能的使用范围、风险和缓解措施。

引用次数: 0

Legal implications of artificial intelligence in health care 医疗保健领域人工智能的法律影响。

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.06.014

The last few years have seen a boom in the popularity of artificial intelligence (AI) around the world, and the health care sector has not been immune from what has been perceived by some as a revolutionary technology. Although AI has been around for many years, including in the field of health care, the recent introduction of consumer-facing generative AI tools has put a spotlight on the technology that has drawn attention from governments, corporations, consumers and more. Health care systems, physician groups, health insurance companies, and others in the space have shown an eagerness to explore AI's potential to improve various aspects of health care, but new legal risks and challenges are unfolding every day. This contribution looks at the latest health care-related measures in the United States and international legal and regulatory landscapes, as well as data privacy implications and discrimination concerns coming out of AI-enabled solutions. It also discusses concerns that health care systems and physicians alike are monitoring, including the potential for medical errors resulting from AI, liability considerations, and malpractice insurance trends.

过去几年，人工智能（AI）在全球范围内得到了蓬勃发展，医疗保健领域也未能幸免，一些人认为这是一项革命性的技术。虽然人工智能已经存在多年，包括在医疗保健领域，但最近面向消费者的生成式人工智能工具的推出使这项技术成为焦点，吸引了政府、企业、消费者等各方的关注。医疗保健系统、医生团体、医疗保险公司以及该领域的其他企业都表现出了探索人工智能潜力的热忱，以改善医疗保健的各个方面，但新的法律风险和挑战也与日俱增。本文探讨了美国和国际法律法规中与医疗保健相关的最新措施，以及人工智能解决方案对数据隐私的影响和歧视问题。它还讨论了医疗系统和医生都在关注的问题，包括人工智能导致医疗事故的可能性、责任考虑因素和医疗事故保险趋势。

引用次数: 0

The state of artificial intelligence for systemic dermatoses: Background and applications for psoriasis, systemic sclerosis, and much more 人工智能在系统性皮肤病中的应用现状：银屑病、系统性硬化症等疾病的背景与应用。

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.06.019

Artificial intelligence (AI) has been steadily integrated into dermatology, with AI platforms already attempting to identify skin cancers and diagnose benign versus malignant lesions. Although not as widely known, AI programs have also been utilized as diagnostic and prognostic tools for dermatologic conditions with systemic or extracutaneous involvement, especially for diseases with autoimmune etiologies. We have provided a primer on commonly used AI platforms and the practical applicability of these algorithms in dealing with psoriasis, systemic sclerosis, and dermatomyositis as a microcosm for future directions in the field. With a rapidly changing landscape in dermatology and medicine as a whole, AI could be a versatile tool to support clinicians and enhance access to care.

人工智能（AI）已逐步融入皮肤病学，人工智能平台已尝试识别皮肤癌，诊断良性与恶性病变。人工智能程序虽然没有那么广为人知，但也被用作诊断和预后工具，用于治疗全身性或皮肤外受累的皮肤病，尤其是自身免疫性疾病。我们介绍了常用的人工智能平台，以及这些算法在治疗银屑病、系统性硬化症和皮肌炎方面的实际应用，以此作为该领域未来发展方向的缩影。随着皮肤病学和整个医学领域的快速变化，人工智能将成为支持临床医生和提高医疗服务可及性的多功能工具。

引用次数: 0

Characteristics and career outcomes of dermatology-focused medical student research grant recipients 以皮肤病学为重点的医学生研究补助金获得者的特征和职业成果。

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.07.015

Grace Y. Duan MD , Zi-Yi Choo MD , Dima Kenj Halabi BS , Adena E. Rosenblatt MD, PhD , Arlene M. Ruiz de Luzuriaga MD, MPH, MBA

Although several dermatology-focused research grants for medical students exist, studies have yet to evaluate the outcomes of grant recipients, such as entry into dermatology residency and academic careers. We have described the characteristics of recipients of dermatology-focused medical student research grants and outcomes, including entry into dermatology residency and academic careers, and we have focused on seven dermatology-focused national and regional research grants eligible for US medical students. Data were obtained from publicly available online sources for grants from 2004 to 2023. Of the 235 medical student recipients of dermatology research grants between 2004 and 2023, 45.5% attended one of the top 20 medical schools funded by National Institutes of Health research grants. Of those who completed medical school, 68.3% advanced to a dermatology residency (n = 123/180). Among board-certified dermatologists, 44.7% held an academic position (n = 34/76); among those who attended a top 20 medical school, 50% held an academic position (n = 23/46) compared with 36.7% who did not (n = 11/30). Limitations of this study include selection bias and incomplete data availability. Medical student research grants allow students to thoughtfully engage in dermatology research early in medical education. These grants may facilitate entry into dermatology residency and academic careers and lead to continued research endeavors.

虽然目前已有几项针对医学生的皮肤病学研究补助金，但尚未有研究对补助金获得者的成果（如进入皮肤病学住院实习和学术生涯）进行评估。我们描述了以皮肤病学为重点的医学生研究补助金获得者的特征和结果，包括进入皮肤病学住院医师培训和学术生涯，并重点研究了符合美国医学生资格的七项以皮肤病学为重点的国家和地区研究补助金。我们从网上公开获取了 2004 年至 2023 年的研究基金数据。在 2004 年至 2023 年期间获得皮肤病学研究基金的 235 名医学生中，45.5% 就读于美国国立卫生研究院研究资助的前 20 所医学院之一。在完成医学院学业的学生中，68.3%升入皮肤科住院医师培训机构（人数=123/180）。在获得委员会认证的皮肤科医生中，44.7%的人担任学术职务（人数=34/76）；在就读于排名前20的医学院的医生中，50%的人担任学术职务（人数=23/46），而没有担任学术职务的人占36.7%（人数=11/30）。本研究的局限性包括选择偏差和数据不完整。医学生研究补助金允许学生在医学教育早期就深思熟虑地参与皮肤病学研究。它们可以帮助学生进入皮肤科住院医师培训和学术生涯，并促使他们继续从事研究工作。

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引用次数: 0

Improving data participation for the development of artificial intelligence in dermatology 提高数据参与度，促进皮肤科人工智能的发展。

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.06.013

Artificial intelligence (AI) has the potential to significantly impact many aspects of dermatology. The visual nature of dermatology lends itself to innovations in this space. The robustness of AI algorithms depends on the quality, quantity, and variety of data on which it is trained and tested. Image collections can suffer from inconsistencies in image quality, underrepresentation of various anatomic sites and skin tones, and lack of benign counterparts leading to underperformance of algorithms in contexts other than one in which it is developed. Access to care, trust, rights, control, and transparency all play roles in the willingness of patients and health care providers and systems to collect, provide, and share data. Opportunities to improve data participation for the development of AI include the establishment of data hubs and public algorithms, federated learning strategies, development of renumeration ecosystems for patients and systems, and development of criteria and mechanisms for transparency.

人工智能（AI）有可能对皮肤科的许多方面产生重大影响。皮肤病学的可视化特性为这一领域的创新提供了条件。人工智能算法的稳健性取决于其训练和测试数据的质量、数量和种类。图像收集可能存在图像质量不一致、各种解剖部位和肤色代表性不足以及缺乏良性对应数据等问题，导致算法在开发环境之外的其他环境中表现不佳。获得护理、信任、权利、控制和透明度都会影响患者、医疗服务提供者和系统收集、提供和共享数据的意愿。改善数据参与人工智能开发的机会包括建立数据中心和公共算法、联合学习策略、为患者和系统开发薪酬生态系统，以及制定透明度标准和机制。

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Revolutionizing teledermatology: Exploring the integration of artificial intelligence, including Generative Pre-trained Transformer chatbots for artificial intelligence-driven anamnesis, diagnosis, and treatment plans 变革远程皮肤病学：探索人工智能的整合，包括 GPT 聊天机器人用于人工智能驱动的病史分析、诊断和治疗计划。

IF 2.3 4区医学 Q2 DERMATOLOGY

Clinics in dermatology

Pub Date : 2024-09-01 DOI: 10.1016/j.clindermatol.2024.06.020

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.

远程皮肤病学和人工智能（AI）的融合标志着皮肤病护理领域的重大进步。本研究探讨了这两个领域之间的协同作用，强调了它们对提高远程皮肤科服务的准确性、可及性和效率的共同影响。远程皮肤病学将皮肤病学护理扩展到偏远和服务不足的地区，而人工智能技术在分析皮肤病学图像和执行远程皮肤病学咨询所涉及的各种任务方面显示出巨大的潜力。这种整合有助于快速、精确的诊断、个性化的治疗方案和数据驱动的洞察力。我们的探索性研究包括设计一个基于 GPT 的聊天机器人 "Dermbot 医生"，并探索其在远程皮肤科会诊过程中的表现。设计阶段的重点是聊天机器人自主进行咨询的能力。随后的测试阶段以目前的远程皮肤病学实践为背景，对其性能进行了评估，探索了人工智能和聊天机器人在模拟和潜在增强远程皮肤病学医疗保健方面的潜力。这项研究展示了远程皮肤科与人工智能相结合的美好前景。它还揭示了伦理和法律问题，包括保护患者数据隐私和遵守监管标准。远程皮肤病学与人工智能的结合不仅旨在提高远程皮肤病学诊断的精确度，还能扩大皮肤病学服务的可及性，使以前得不到充分服务的人群也能享受到服务，从而使患者、医疗服务提供者和整个医疗系统受益。

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