Medical foundation models have shown promise in controlled benchmarks, yet widespread deployment remains hindered by reliance on task-specific fine-tuning. Here, we introduce DermFM-Zero, a dermatology vision-language foundation model trained via masked latent modelling and contrastive learning on over 4 million multimodal data points. We evaluated DermFM-Zero across 20 benchmarks spanning zero-shot diagnosis and multimodal retrieval, achieving state-of-the-art performance without task-specific adaptation. We further evaluated its zero-shot capabilities in three multinational reader studies involving over 1,100 clinicians. In primary care settings, AI assistance enabled general practitioners to nearly double their differential diagnostic accuracy across 98 skin conditions. In specialist settings, the model significantly outperformed board-certified dermatologists in multimodal skin cancer assessment. In collaborative workflows, AI assistance enabled non-experts to surpass unassisted experts while improving management appropriateness. Finally, we show that DermFM-Zero's latent representations are interpretable: sparse autoencoders unsupervisedly disentangle clinically meaningful concepts that outperform predefined-vocabulary approaches and enable targeted suppression of artifact-induced biases, enhancing robustness without retraining. These findings demonstrate that a foundation model can provide effective, safe, and transparent zero-shot clinical decision support.

Given the widespread availability of H&E slides, there is considerable interest in determining whether molecular information can be inferred directly from tissue morphology, potentially reducing the need for costly spatial transcriptomic profiling. We assessed three state-of-the-art methods for predicting single-cell gene expression from H&E images across three skin disease contexts and two Xenium panels. As controls, we included simple linear regression models trained on embeddings from multiple foundation models, totalling 16 models evaluated in this study. We show that all models performed poorly: for most genes, prediction accuracy was near zero, and reliable predictions were largely restricted to keratinocyte-associated genes. Predicted expression failed to preserve cell-type identity and spatial organisation, with only keratinocytes forming coherent clusters, while immune, fibroblast, and other dermal populations were extensively mixed. Notably, simple ridge regression on pretrained embeddings matched or outperformed the more complex published architectures, indicating that the predictive signal originates primarily from image representations rather than model design. Our results demonstrate that current H&E-based gene expression prediction methods are not yet suitable for single-cell-level interpretation of spatial transcriptomics in skin tissue.

BACKGROUND:While dermatoscopy's added value to clinical imaging is known, the standalone value of dermatoscopy and clinical images remains unclear. OBJECTIVE:To quantify the standalone and complementary value of dermatoscopy and clinical close-up imaging across lesion types and reader expertise. METHODS:In an online reader study, 283 participants diagnosed 1567 paired images (clinical close-up/dermatoscopic) of skin cancers and mimics. The presentation order was randomized. Diagnostic accuracy, sensitivity, and specificity were compared. The effects of modality, reader expertise, and presentation order were analyzed using a generalized linear mixed model. RESULTS:Dermatoscopy alone showed higher sensitivity (85.0% vs 74.2%) but lower specificity (66.8% vs 71.9%) compared with clinical close-up images alone. It improved accuracy for melanoma and basal cell carcinoma, but not for nevi. Adding either modality increased the odds of a correct diagnosis. Dermatoscopy raised the odds by 52% (OR = 1.52; 95% CI: 1.36-1.70; P < .001), while clinical close-ups increased the odds by 40% (OR = 1.40; 95% CI: 1.20-1.61; P < .001). LIMITATIONS:Image-based evaluation simulated teledermatology rather than face-to-face assessment; potential selection and verification bias cannot be excluded. CONCLUSION:Dermatoscopy alone yields higher sensitivity for malignant lesions but lower specificity for nevi than clinical close-up images. The latter provides complementary diagnostic cues, underscoring the value of integrating both modalities for optimal assessment.

Artificial intelligence (AI) is increasingly permeating healthcare, from serving as a physician assistant to powering consumer applications. The opacity of AI algorithms makes the ability of humans to interact with AI algorithms challenging. To overcome this limitation, explainable AI (XAI) provides insight into AI decision-making, but evidence suggests that XAI can paradoxically induce bias in the human decision-making process. Here we present results from two large-scale experiments, involving 623 lay people and 153 primary care physicians (PCPs), respectively, in which a fairness-based AI model for dermatological diagnoses and different XAI-based explanations were combined to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. With fairness-constrained model training, assistance from an AI model that achieved balanced performance across skin tones improved final diagnostic accuracy and reduced skin-tone-related performance disparities among both lay people and PCPs. In this setting, LLM explanations yielded divergent effects: lay users showed higher automation bias-accuracy was boosted when the diagnoses provided by the AI model were correct but was reduced when the model erred-whereas experienced PCPs remained resilient, benefiting irrespective of the AI model's accuracy. In addition, presenting the AI model's diagnosis before human decision-making may lead to stronger anchoring bias. These findings highlight XAI's varying impacts based on human expertise and the timing of when the AI-based prediction is provided, underscoring the concept that LLMs can act as a 'double-edged sword' in medical AI and informing future human-AI collaborative system design.

Lentigo maligna (LM) is a subtype of cutaneous melanoma in situ that develops on chronically sun-damaged skin in elderly individuals. Its diagnosis and management remain challenging due to its slow progression, frequent occurrence on cosmetically sensitive areas in frail individuals, and tendency for subclinical peripheral extension. Despite its potential for invasive transformation, the natural history of LM remains incompletely understood and evidence-based management strategies for this specific entity remain limited. This work aims to bridge the gap between scarce high-quality evidence and the clinicians' need for practical guidance on LM management by formulating evidence-based and expert consensus-driven recommendations for diagnosis, treatment and follow-up. Under the coordination of the International Dermoscopy Society, a global, multidisciplinary consortium of 53 experts-including dermatologists, dermato-oncologists, dermatologic surgeons, radiologists, radiotherapists, pathologists and epidemiologists-formulated recommendations through structured consensus, informed by a comprehensive review of current scientific evidence and clinical practice standards. Optimal management of LM requires accurate diagnosis and individualized treatment planning. Non-invasive skin imaging techniques, especially dermoscopy and reflectance confocal microscopy, aid in diagnosis, biopsy orientation, lesion delineation and post-treatment monitoring. Multiple partial biopsies help confirm diagnosis and rule out invasion. Complete surgical excision remains the first treatment option. No definitive safety margins can be recommended for standard surgery; margin-controlled techniques are preferable for large or ill-defined lesions. Topical imiquimod and radiotherapy are effective alternatives where surgery is unsuitable. Topical imiquimod is useful as primary, adjuvant or neoadjuvant therapy. Blind destructive methods should be avoided. Close clinical and imaging follow-up is needed. Patient-centred care, shared decision-making, and a multidisciplinary approach are critical for optimal outcomes. These international consensus recommendations summarize current best practices for LM management, offering practical, evidence-informed guidance to clinicians while acknowledging the potential of future research in refining these conclusions.

Smartphone applications for dermatology are widely available across Europe, yet evidence on their characteristics, validation, and regulatory compliance remains limited. This review, conducted by the European Academy of Dermatology and Venereology (EADV) Artificial Intelligence (AI) Task Force, systematically searched app stores in 44 European countries, identifying 1746 apps in 2024, of which 420 met inclusion criteria. Specifically for AI-based skin cancer screening apps, the search was updated in June 2026. Apps were categorized by function, with metadata extracted on cost, target audience, AI presence, General Data Protection Regulation (GDPR) compliance, and CE (Conformité Européenne) marking. Most apps (61%) were available on both Google Play and Apple App Store; 94% were free, and 60% targeted laypersons. Twelve percent reported AI functionality in 2024, 41% of these focused on skin cancer diagnosis. Only 2% reported CE marking and 10% GDPR compliance. Scientific validation was limited. Twenty percent of apps linked to peer-reviewed publications, 24 apps had clinical trial evaluations, 5 underwent randomized controlled trials (RCTs), and 6 had real-world post-deployment studies. The 2026 search identified 38 AI-based skin cancer screening apps. Adherence to EADV recommendations was poor. Key principles including explainability, inclusivity, and data sharing were met by ≤ 37% of apps. Most dermatology apps target laypersons for teledermatology, education, and self-diagnosis. Major gaps exist in regulatory compliance, clinical evidence, and transparency, raising patient safety concerns. These shortcomings raise concerns about reliability and safety, underscoring the need for stronger quality assurance, real-world validation, and inclusion of diverse skin types, in line with EADV AI Task Force recommendations.

BACKGROUND AND OBJECTIVES:For thick melanomas, retaining the primary lesion during neoadjuvant therapy may enhance immune response through increased antigen load. This study assessed whether a central section from serially processed melanoma specimen, representing a transectional incisional biopsy, accurately reflects Breslow thickness and ulceration compared to full serial sectioning. PATIENTS AND METHODS:From a single center, histological slides of 78 invasive melanomas from 76 patients were retrospectively collected. Specimen were digitized, tumor-thickness and ulceration were measured across all tissue sections. The most central section was compared to the overall specimen. RESULTS:Across 78 paired comparisons, the median difference between central and maximum depth measurement was below 0.2 mm (0 mm, IQR 0.02, p < 0.001), with 64 (82.1%) cases ≤ 0.2 mm. Ulceration was present in 9 cases (11.5%), always detectable in the central section. T-stage remained unchanged in 73/78 cases (93.6%), the remaining 5 cases showed higher stages with full workup within T-stages ≤ 2. CONCLUSIONS:In this retrospective cohort, a central section provided Breslow thickness and T-stage equivalent to full serial sectioning in most cases, underestimation occurred only in thin melanomas. These findings provide a pre-clinical foundation for trials testing whether retaining the primary melanoma during neoadjuvant therapy may enhance treatment efficacy.

Importance:Artificial intelligence (AI) systems for skin cancer detection perform well in controlled settings but frequently underperform in everyday clinical practice, raising critical questions about their readiness for deployment. Objective:To compare the diagnostic accuracy of AI algorithms vs human evaluators across varying expertise levels for skin lesion diagnosis, including rare and atypical cases, in a realistic clinical context. Design, Setting, and Participants:This multi-institutional diagnostic study compared diagnostic performance among AI models and physician readers with varying dermatological expertise, ranging from less than 1 year to more than 10 years of experience. A dataset of dermatological images representing everyday clinical scenarios was used and contained 1117 cases, including clinical and dermoscopic images with associated metadata. Study inclusion spanned from March 16, 2023, to August 1, 2025. Exposures:Three AI algorithms: a first-generation convolutional neural network (CNN) and 2 foundation models (PanDerm unimodal and multimodal). Human readers evaluated 100 stratified, random cases drawn from the same dataset. Main Outcomes and Measures:The primary outcome was reader-level multiclass diagnostic accuracy for skin lesion classification. Secondary outcomes were binary benign vs malignant sensitivity, specificity, and balanced accuracy. Performance was compared between AI algorithms and human readers stratified by experience level. Results:A total of 652 physicians (median [IQR] age, 33 [29-37] years; 559 [85.7%] female) contributed to 1092 test iterations. All human readers outperformed the CNN (mean [SD] accuracy, 65.9% [10.5%] vs 56.7% [3.9%]; difference, 9.2 percentage points [pp]; 95% CI, -9.8 to 8.5 pp; P < .001). Unimodal accuracy exceeded readers with less than 3 years of experience (mean [SD] accuracy, 72.2% [3.5%] vs 68.2% [7.6%]; difference, 4.0 pp; 95% CI, 3.2-4.9 pp; P < .001). With a mean (SD) accuracy of 74.2% (5.7%), experts with more than 10 years of experience achieved the highest multiclass diagnostic accuracy, outperforming all AI models on this primary end point, which included 56.7% (3.9%) for CNN, 72.2% (3.5%) for the unimodal model, and 66.3% (3.8%) for the multimodal model. Conclusions and Relevance:In this diagnostic study, a modern foundation model surpassed readers with less than 3 years of experience on accuracy of skin lesion diagnosis and matched those with 3 to 10 years of experience but remained inferior to experts with more than 10 years of experience, highlighting both the promise and current limitations of AI in dermatologic diagnosis.

Aging is the primary risk factor for chronic disease and is characterized by profound structural and architectural remodeling of human tissues. Here, we present a comprehensive assessment of these changes using 25,712 whole-slide histopathological images from 40 tissue types across 983 individuals in the Genotype-Tissue Expression cohort. By leveraging deep learning, we quantified nuanced morphological alterations to develop 'tissue clocks', predictors of biological age that reflect tissue structural integrity and physiological fitness. These clocks correlate with established aging markers, such as telomere attrition, subclinical pathologies and comorbidities. Through a systematic evaluation of biological aging rates across organs, we identified associations of tissue-specific age acceleration with demographic, lifestyle and medical factors, highlighting potentially modifiable risk factors that affect tissue aging. Furthermore, by integrating paired histology and transcriptomic data, we developed a strategy to predict tissue-specific age gaps directly from blood samples. We validated this approach by identifying disease-relevant organ aging across independent cohorts for eight prevalent diseases, including Alzheimer's disease, stroke and Crohn's disease. This work positions tissue architecture as a critical integrator of molecular and cellular changes over the course of aging, demonstrates that histopathological imaging provides a robust framework for monitoring tissue-specific aging and offers a scalable foundation for understanding organ-level physiological decline in health and disease.

Abstract Basal cell carcinoma (BCC) and cutaneous squamous cell carcinoma (SCC) are the most common keratinocyte-derived malignancies, yet they differ markedly in invasiveness, metastatic potential, and immune contexture. Although cancer-associated fibroblasts (CAFs) are increasingly recognized as key regulators of tumor architecture and tumor immunity, the spatial organization of distinct CAF subtypes in cutaneous carcinomas and their functional relationship with immune cells remains incompletely understood. Using a 33-plex imaging mass cytometry (IMC) panel, we profiled 28 regions of interest (ROIs) from 17 human BCC and SCC specimens, encompassing more than 739,000 single cells, and integrated these data with RNA fluorescence in situ hybridization (RNA-FISH), immunohistochemistry (IHC), multiplex immunofluorescence, and in vitro functional assays. We identified four fibroblast populations, including immunomodulatory CAFs (iCAFs), matrix CAFs (mCAFs), myofibroblast-like CAFs (myoCAFs), and reticular fibroblasts (retFIBs), and found that aggressive tumor subtypes were characterized by increased stromal area, extracellular matrix deposition, and altered CAF composition. CAF composition differed most prominently across BCC subtypes, with nodular BCC enriched for mCAFs and infiltrative BCC showing increased myoCAF density, consistent with a shift toward a contractile stromal program. Spatial analyses revealed distinct CAF-immune niches: iCAFs localized to immune-cell-rich, inflamed niches enriched for activated and/or exhaustion-associated immune-cell marker programs, whereas myoCAFs occupied fibroblast-dense, immune-poor niches with globally reduced immune activation. mCAFs were preferentially associated with immune cell accumulation in the stroma and spatial immune compartmentalization, with limited immune cell presence within tumor nests. At the invasive front, CAF-immune coupling was highly subset-dependent, with iCAFs linked to antigen-experienced T-cell states and myoCAFs linked to immune exclusion. In vitro, patient-derived CAF cultures from myoCAF-rich biopsies showed enhanced collagen-gel contraction, with cultures enriched for MCAM + CAFs displaying increased contractile capacity. Aggressive tumor variants displayed increased stromal nuclear YAP/TAZ, while complementary single-cell pathway analysis supported a mechanically remodeled stromal microenvironment in which mCAFs contribute ECM/matrix-remodeling programs and RGS5⁺/myoCAF-like populations show enhanced mechanotransduction-associated signaling, rather than a uniform CAF-wide increase in canonical YAP/TAZ transcriptional output. Together, these findings define spatially organized CAF programs in cutaneous carcinomas and identify myoCAF-rich stromal niches as a recurrent feature of aggressive, immune-repressed tumor architecture. These results nominate CAF composition as a biomarker of immune architecture and a potential determinant of therapeutic response.

Abstract Inflammatory skin diseases (ISDs) affect up to 25% of the global population. Yet, large-scale comparative single-cell RNA-sequencing (scRNA-seq) analyses between ISDs are still missing. Here, we integrated scRNA-seq datasets spanning 27 skin diseases from 50 studies, comprising over 2 million cells from 441 samples. Using the healthy skin cell atlas as reference, we could build a robust ISD atlas that enabled us to differentiate universal inflammatory signatures and disease-specific ones. This highlighted, for example, a shared gene program between keratinocytes in atopic dermatitis and parapsoriasis, not present in cutaneous T-cell lymphoma, confirms the plasticity of Th17 cells throughout ISDs, defines specific macrophage signatures in acne, and reveals a yet undescribed role of mural cells in ISDs. This demonstrates the power of the ISD atlas as a resource to resolve disease-specific immune mechanisms. The complete atlas is available through an interactive online portal at https://isd-atlas.derma.meduniwien.ac.at .

Introduction Data about the role of dermatoscopy-based artificial intelligence (AI) models on melanoma prognosis remain scant. Methods A comprehensive literature search was conducted in electronic databases until June 2025. Studies referring to machine learning algorithms (MLAs) trained on dermatoscopic images and analyzing the risk of melanoma metastasis were included. Also, due to scarcity of data regarding direct metastasis prediction, studies assessing melanoma prognostic factors, including Breslow thickness (BT), ulceration and histopathological subtype were analyzed. Results 18 studies met the inclusion criteria. Two studies focused on metastasis prediction: a pre-trained ResNet50 demonstrated comparable accuracy to tumor prognostic factors, while a Foundation model achieved AUC 0.96 (95 %CI 0.93 – 0.99). 13 studies assessed MLAs for BT prediction, using clinically meaningful thresholds (0.8 or 2.0 mm). MLAs showed substantial accuracy in binary tasks, particularly when advanced models (i.e. semi-supervised learning), or sophisticated loss of function techniques were employed. However, the inherent complexity of multiclass tasks reduced performance, due to class imbalance and overlapping image features. Furthermore, despite explainable AI highlighted blue pigmentation and atypical vascular pattern indicative of thick melanomas, models’ performance constraints were also noticed. Across BT continuum, models struggled to classify correctly lesions with intermediate (0.4–1.1 mm) or very thick melanomas. Finally, 3 studies evaluated MLAs for histopathological subtype recognition, with transfer learning achieving high accuracy across superficial spreading, nodular and acral subtypes. Outcomes MLAs based on dermatoscopy demonstrated feasibility for predicting metastasis and prognostic factors of melanoma preoperatively. Future studies integrating this into multimodal frameworks, including digital pathology and gene signatures, seems important.

In this study, we introduce MILK10k, a multimodal image dataset designed to enhance machine learning and artificial intelligence-driven applications for diagnosing skin lesions suspected to be malignant neoplasms. MILK10k includes both close-up and dermatoscopy images expanding upon existing datasets with several enhancements. It expands disease coverage to 48 International Skin Imaging Collaboration-Designated Diagnoses-matched diagnoses, encompassing both neoplastic and non-neoplastic as well as pigmented and nonpigmented skin lesions. In addition, MILK10k incorporates images from individuals with diverse skin tones as well as rare skin diseases. The dataset includes 10,480 images from 5240 cases, retrospectively collected across 5 different centers. Of these, 95.7% (n = 5016) have been biopsied or excised, with histopathology serving as the ground truth. Accompanying metadata includes information on age, sex, skin tone, anatomic site, and diagnosis with varying levels of granularity. In addition to the dataset, we provide results from a machine-learning pipeline that evaluates images on the basis of common, human-interpretable concepts such as pigmentation, ulceration, hair, and skin markings. Furthermore, we provide a benchmark test set of 948 multimodal images from the same sources and an online tool for assessing key metrics on this set, creating a platform for evaluating both machine-learning models and human reader studies.

Artificial intelligence (AI) is increasingly permeating healthcare, from physician assistants to consumer applications. Since AI algorithm's opacity challenges human interaction, explainable AI (XAI) addresses this by providing AI decision-making insight, but evidence suggests XAI can paradoxically induce over-reliance or bias. We present results from two large-scale experiments (623 lay people; 153 primary care physicians, PCPs) combining a fairness-based diagnosis AI model and different XAI explanations to examine how XAI assistance, particularly multimodal large language models (LLMs), influences diagnostic performance. AI assistance balanced across skin tones improved accuracy and reduced diagnostic disparities. However, LLM explanations yielded divergent effects: lay users showed higher automation bias - accuracy boosted when AI was correct, reduced when AI erred - while experienced PCPs remained resilient, benefiting irrespective of AI accuracy. Presenting AI suggestions first also led to worse outcomes when the AI was incorrect for both groups. These findings highlight XAI's varying impact based on expertise and timing, underscoring LLMs as a "double-edged sword" in medical AI and informing future human-AI collaborative system design.

Artificial intelligence (AI) systems substantially improve dermatologists' diagnostic accuracy for melanoma, with explainable AI (XAI) systems further enhancing their confidence and trust in AI-driven decisions. Despite these advancements, there remains a critical need for objective evaluation of how dermatologists engage with both AI and XAI tools. In this study, 76 dermatologists participate in a reader study, diagnosing 16 dermoscopic images of melanomas and nevi using an XAI system that provides detailed, domain-specific explanations, while eye-tracking technology assesses their interactions. Diagnostic performance is compared with that of a standard AI system lacking explanatory features. Here we show that XAI significantly improves dermatologists' diagnostic balanced accuracy by 2.8 percentage points compared to standard AI. Moreover, diagnostic disagreements with AI/XAI systems and complex lesions are associated with elevated cognitive load, as evidenced by increased ocular fixations. These insights have significant implications for the design of AI/XAI tools for visual tasks in dermatology and the broader development of XAI in medical diagnostics.

Psoriasis is a common chronic inflammatory skin disease characterized by a thickened epidermis with elongated rete ridges and massive immune cell infiltration. It is currently unclear what impact mechanoregulatory aspects may have on disease progression. Using multiphoton second harmonic generation microscopy, we found that the extracellular matrix was profoundly reorganized within psoriatic dermis. Collagen fibers were highly aligned and assembled into thick, long collagen bundles, whereas the overall fiber density was reduced. This was particularly pronounced within dermal papillae extending into the epidermis. Furthermore, the extracellular matrix-modifying enzyme lysyl oxidase was highly upregulated in the dermis of patients with psoriasis. In vitro experiments identified a previously unreported link between hypoxia-inducing factor 1 stabilization and lysyl oxidase protein regulation in mechanosensitive skin fibroblasts. Lysyl oxidase secretion and activity directly correlated with substrate stiffness and were independent of hypoxia and IL-17. Finally, single-cell RNA-sequencing analysis identified skin fibroblasts expressing high amounts of lysyl oxidase and confirmed elevated hypoxia-inducing factor 1 expression in psoriasis. Our findings suggest a potential yet undescribed mechanical aspect of psoriasis. Deregulated mechanical forces hence may be involved in initiating or maintaining of a positive feedback loop in fibroblasts and contribute to tissue stiffening and diminished skin elasticity in psoriasis, potentially exacerbating disease pathogenesis.

With an estimated 3 billion people globally lacking access to dermatological care, technological solutions leveraging artificial intelligence (AI) have been proposed to improve access. Diagnostic AI algorithms, however, require high-quality datasets to allow development and testing, particularly those that enable evaluation of both unimodal and multimodal approaches. Currently, the majority of dermatology AI algorithms are built and tested on proprietary, siloed data, often from a single site and with only a single image type (i.e., clinical or dermoscopic). To address this, we developed and released the Melanoma Research Alliance Multimodal Image Dataset for AI-based Skin Cancer (MIDAS) dataset, the largest publicly available, prospectively-recruited, paired dermoscopic- and clinical image-based dataset of biopsy-proven and dermatopathology-labeled skin lesions. We explored model performance on real-world cases using four previously published state-of-the-art (SOTA) models and compared model-to-clinician diagnostic performance. We also assessed algorithm performance using clinical photography taken at different distances from the lesion to assess its influence across diagnostic categories. We prospectively enrolled 796 patients through an IRB-approved protocol with informed consent representing 1290 unique lesions and 3830 total images (including dermoscopic and clinical images taken at 15-cm and 30-cm distance). Images represented the diagnostic diversity of lesions seen in general dermatology, with malignant, benign, and inflammatory lesions that included melanocytic nevi (22%; n=234), invasive cutaneous melanomas (4%; n=46), and melanoma in situ (4%; n=47). When evaluating SOTA models using the MIDAS dataset, we observed performance reduction across all models compared to their previously published performance metrics, indicating challenges to generalizability of current SOTA algorithms. As a comparative baseline, the dermatologists performing biopsies were 79% accurate with their top-1 diagnosis at differentiating a malignant from benign lesion. For malignant lesions, algorithms performed better on images acquired at 15-cm compared to 30-cm distance while dermoscopic images yielded higher sensitivity compared to clinical images. Improving our understanding of the strengths and weaknesses of AI diagnostic algorithms is critical as these tools advance towards widespread clinical deployment. While many algorithms may report high performance metrics, caution should be taken due to the potential for overfitting to localized datasets. MIDAS's robust, multimodal, and diverse dataset allows researchers to evaluate algorithms on our real-world images and better assess their generalizability. ### Competing Interest Statement AC was an investigator for Skin Analytics. JK is consultant to Hims, Enspectra Health, research collaborator with Google Research, and on advisory board for Skin Analytics. PT received honoraria from Silverchair, unrestricted grants for education projects from Lilly, and honoraria for lectures from AbbVie, Lilly, FotoFinder and Novartis. SSH is the founder, chief executive officer, and chief technical officer of IDerma, Inc. RD has served as an advisor to MDAlgorithms and Revea and received consulting fees from Pfizer, L'Oreal, Frazier Healthcare Partners, and DWA, and research funding from Union Chimique Belge (UCB), and was an investigator for Skin Analytics. RN is a consultant to Enspectra Health and Sanctum, LLC and was an investigator for Skin Analytics. ### Funding Statement This publication is based on research supported by the Melanoma Research Alliance (MRA)- L'Oreal Dermatological Beauty Brands Team Science Award, along with philanthropic funding from the David Mair and Vanessa Vu-Mair Artificial Intelligence in Skin Cancer Fund and the Tal & Cinthia Simon Melanoma Research Fund at Stanford Medicine. We also received a Stanford Human-Centered Artificial Intelligence Google Cloud Credits Grant to support compute efforts. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: Institutional Review Board of Stanford University under IRB#36050 gave ethical approval for this work. Institutional Review Board of Cleveland Clinic Foundation under IRB#20-666 gave ethical approval for this work. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes Data produced are available online at https://stanfordaimi.azurewebsites.net/datasets/f4c2020f-801a-42dd-a477-a1a8357ef2a5