Background/Objectives: Demand for dermatologic services exceeds specialist capacity, with average wait times of 26–50 days in the United States. Current triage methods rely on subjective judgment or disease-specific indices that do not generalize across diagnoses or translate to operational decisions. We developed and validated the Dermatologic Complexity Score (DCS), a standardized instrument to guide case prioritization across dermatology care settings and evaluate DCS as a workload-reduction filter, enabling safe delegation of approximately half of routine teledermatology cases (DCS ≤ 40) away from specialist review. Methods: We conducted a prospective validation study of the DCS using 100 consecutive teledermatology cases spanning 30 common conditions. The DCS decomposes complexity into five domains (Diagnostic, Treatment, Risk, Patient Complexity, Monitoring) summed to a 0–100 total with prespecified bands: ≤40 (low) (41–70), (moderate) (71–89), (high), ≥90 (extreme). Five board-certified dermatologists and an automated module independently scored all cases. Two primary care physicians completed all ≤40 cases to assess feasibility. Primary outcomes were interrater reliability using ICC (2,1) and agreement with automation. Secondary outcomes included time-to-decision, referral rates, and primary care feasibility. Results: Mean patient age was 46.2 years; 47% of cases scored ≤40, 33% scored 41–70, 18% scored 71–89, and 2% scored ≥90. Interrater reliability was excellent (ICC (1,2)) = 0.979; 95% CI 0.974–0.983), with near-perfect agreement between automated and mean dermatologist scores (r = 0.998). Time-to-decision increased monotonically across DCS bands from 2.11 min (≤40) to 5 (90) min (≥90) (p = 1.36 × 10⁻¹⁴). Referral rates were 0% for ≤40, 3% for 41–70, 27.8% for 71–89, and 100% for ≥90 cases. DCS strongly predicted referral decisions (AUC = 0.919). Primary care physicians successfully managed all ≤40 cases but required 6–8 additional minutes per case compared to dermatologists. Conclusions: The DCS demonstrates excellent reliability and strong construct validity, mapping systematically to clinically relevant outcomes, including decision time and referral patterns. The instrument enables standardized, reproducible triage decisions that can optimize resource allocation across teledermatology, clinic, procedural, and inpatient settings. Implementation could improve access to dermatologic care by supporting appropriate delegation of low-complexity cases to primary care while ensuring timely specialist evaluation for high-complexity conditions.

Background and Objectives: Shortages of dermatologists create significant barriers to care, particularly for inflammatory and history-dependent conditions where image-only artificial intelligence (AI) classifiers have limited applicability. Current teledermatology solutions largely focus on single-task, morphology-based neoplasm classifiers, leaving the vast majority of dermatologic presentations underserved. This study evaluated the diagnostic accuracy and management plan quality of Dermflow (Prava Medical, Delaware, USA), a proprietary dermatology-focused Multimodal Image-and-Data Assistant (MIDA) that autonomously gathers dermatology-specific history, integrates data with patient-submitted images, and outputs structured differential diagnoses and management summaries. Materials and Methods: Two AI systems, Dermflow and Claude Sonnet 4 (Claude, a leading vision–language model), analyzed 87 clinical images from the Skin Condition Image Network and Diverse Dermatology Images databases, representing 10 inflammatory dermatoses and 9 neoplastic conditions stratified across Fitzpatrick Skin Tone (FST) categories (I–II, III–IV, V–VI). For the diagnostic comparison, Dermflow received images and autonomously gathered clinical history, while Claude received identical images without history. For the management plan comparison, both systems received the correct diagnosis and the clinical histories gathered by Dermflow. The primary outcome was diagnostic accuracy. The secondary outcome was management plan quality, assessed by two blinded dermatologists across eight clinical dimensions using 5-point Likert scales. Chi-square tests compared diagnostic accuracy between models; t-tests and ANOVA compared management quality scores. Results: Dermflow achieved markedly superior diagnostic accuracy compared to Claude (86.2% vs. 24.1%, p < 0.001). Both models maintained consistent diagnostic performance across FST categories without significant within-model differences (Dermflow p = 0.924; Claude p = 0.828). Management plan quality showed no significant overall differences between models. However, composite management quality scores declined significantly for darker skin tones across both systems: Dermflow scored 4.20 (FST I–II), 3.99 (FST III–IV), and 3.47 (FST V–VI); Claude scored 4.35, 3.97, and 3.44, respectively (p < 0.001 for most pairwise FST comparisons within each model). Conclusions: Multimodal AI integrating targeted history with image analysis achieves substantially higher diagnostic accuracy than image-only approaches across both inflammatory and neoplastic dermatologic conditions. Autonomous history gathering addresses fundamental limitations of morphology-only classifiers and enables scalable, patient-facing triage across the full spectrum of dermatologic disease. However, both models demonstrated reduced management plan quality for darker skin tones despite receiving the correct diagnosis, suggesting persistent training data limitations that require targeted bias-mitigation strategies beyond domain-specific instruction.

Recognizing inflammation in darker skin tones has important implications for diagnosis and management of skin disease, particularly in patients with skin of color.

Tirzepatide, a dual glucose-dependent insulinotropic polypeptide (GIP) and glucagon-like peptide-1 (GLP-1) receptor agonist, is used to treat type 2 diabetes mellitus. While gastrointestinal adverse effects are well established, cutaneous manifestations are less characterized. We report a case of a biopsy-confirmed exanthematous drug eruption associated with tirzepatide. A 74-year-old woman developed profuse watery diarrhea and an intensely pruritic rash shortly after initiating tirzepatide. Over three weeks, the eruption involved the bilateral upper extremities and proximal lower extremities. Histopathologic evaluation revealed superficial dermal perivascular dermatitis with eosinophils and mild epidermal spongiosis. Discontinuation of tirzepatide and topical corticosteroids led to clinical improvement. This case expands the limited literature on systemic dermatologic adverse effects of tirzepatide by demonstrating a biopsy-confirmed exanthematous drug eruption. Recognition of such reactions allows for prompt diagnosis and enhanced patient counseling. This represents one of the first reported biopsy-confirmed exanthematous drug eruptions associated with tirzepatide. Key words: Tirzepatide, GLP-1 receptor agonist, Cutaneous adverse drug reaction, Obesity pharmacotherapy, Dermatologic toxicity