A Clinical Perspective on Melanoma Overdiagnosis.

Overdiagnosis in melanoma is a nuanced and evolutionary issue. It may appear to be an epidemic from an epidemiological point of view; however, it is important to appreciate all perspectives. We consider overdiagnosis a necessary challenge-which drives clinicians to improve diagnostic accuracy and reduce overtreatment-pathologists to recalibrate histopathological thresholds and reduce overcalling-and researchers to develop markers of biological activity for melanoma.

Pre- and post-treatment care for actinic keratoses: An Australian and New Zealand perspective.

Background: Actinic keratoses (AKs), also known as solar keratoses, are precancerous skin lesions that typically occur following long-term exposure to ultraviolet radiation. AKs can evolve into cutaneous squamous cell carcinoma (SCC) if left untreated. Treatments for AKs typically cause temporary, irritable erythematous skin barrier disruption.

Radiotherapy versus imiquimod for complex lentigo maligna: A phase 3 randomized clinical trial.

Background: For patients with lentigo maligna who are not suitable for surgery, radiotherapy, or topical imiquimod are alternative nonsurgical treatments.

Objective: This trial aimed to assess the efficacy, safety, and patient-reported health-related quality of life (HRQL).

Methods: A multiinstitutional, phase 3, randomized trial conducted between August 2015 and November 2021.

Informing a Position Statement on the Use of Large Language Models and AI Scribes in Dermatology in Australia.

Artificial Intelligence (AI) refers to the ability of computers to mimic human intelligence. In response to the growing interest and impact of AI, the Australasian College of Dermatologists released its first Position Statement on AI in dermatology in 2022. This Position Statement provided guidance for dermatologists on the appropriate use of AI.

A systematic review of microsimulation models for skin cancer.

Background: Simulation modelling can assist with health care decision making. To inform the development and improvement of skin cancer focussed microsimulation models, we conducted a systematic review and narrative synthesis of published skin cancer models to assess the structure, parameterisation, and assumptions.

Methods: The electronic databases OVIDMedline including Embase and the Cost-Effectiveness Analysis (CEA) Registry were searched up to 7 May 2025.

A multimodal vision foundation model for clinical dermatology.

Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep learning models excel at specific tasks such as skin cancer diagnosis from dermoscopic images, they struggle to meet the complex, multimodal requirements of clinical practice. Here we introduce PanDerm, a multimodal dermatology foundation model pretrained through self-supervised learning on over 2 million real-world skin disease images from 11 clinical institutions across 4 imaging modalities.

Management of Local Skin Reactions Caused by 5-FU 4% Cream for the Treatment of Actinic Keratosis: A Delphi Consensus.

Introduction: Treatments such as 4% 5-fluorouracil (5-FU) cream have demonstrated strong efficacy in lesion clearance of actinic keratosis; however, local skin reactions (LSR) during treatment remain a significant challenge, potentially affecting patient adherence.

Objective: We sought to build consensus on management of LSR associated with 4% 5-FU using the Delphi methodology.

Methods: Twenty-eight expert dermatologists participated in a 3-round Delphi process.

Reflectance confocal microscopy terminology for nonmelanocytic skin lesions: A Delphi consensus of experts.

Background: There is lack of uniformity in reflectance confocal microscopy (RCM) terminology.

Objective: To establish expert consensus on a standardized set of RCM terms that describe non-melanocytic lesions (NMLs).

Methods: We invited RCM experts to participate in a Delphi-consensus study.

Variation in initial biopsy technique for primary melanoma diagnosis: A population-based cohort study in New South Wales, Australia.

Background: Factors associated with nonadherence to guideline-recommended complete excision of suspicious cutaneous lesions are unclear.

Objective: The purpose of this study was to analyze patient, melanoma, and clinician factors associated with initial diagnostic biopsy type and determine whether unwarranted variation from guidelines occurred.

Methods: This population-based, cohort study involved the analysis of data from questionnaires completed by clinicians who managed patients with newly diagnosed, histopathologically confirmed primary invasive cutaneous melanomas reported to the New South Wales Cancer Registry between 2006 and 2007.

Short-Term Effectiveness of a Stepped-Care Model to Address Fear of Cancer Recurrence in Patients With Early-Stage Melanoma.

Objective: To investigate the effectiveness of the Melanoma Care Programme when implemented into routine clinical practice coupled with fear of cancer recurrence (FCR) screening and a stepped-care model of intervention delivery.

Methods: Using a Type-I hybrid effectiveness-implementation design, individuals with stage 0-II melanoma and a Fear of Cancer Recurrence Inventory FCR severity score of ≥ 13 were offered the Melanoma Care Programme. The programme included a psychoeducational booklet and 3 to 5 psychotherapeutic telehealth sessions with a clinical psychologist, timed around routine dermatological appointments.

Effect of patient-contextual skin images in human- and artificial intelligence-based diagnosis of melanoma: Results from the 2020 SIIM-ISIC melanoma classification challenge.

Background: While the high accuracy of reported AI tools for melanoma detection is promising, the lack of holistic consideration of the patient is often criticized. Along with medical history, a dermatologist would also consider intra-patient nevi patterns, such that nevi that are different from others on a given patient are treated with suspicion.

Objective: To evaluate whether patient-contextual lesion-images improves diagnostic accuracy for melanoma in a dermoscopic image-based AI competition and a human reader study.

In vivo reflectance confocal microscopy role for early to advanced lentigo maligna melanoma spectrum: A systematic review and pooled analysis.

Background: Lentigo maligna (LM) is a growing problem worldwide and the main type of melanoma in situ in some Caucasian populations. It presents as a spectrum from atypical intraepidermal melanocytic proliferation (AIMP) to invasive lentigo maligna melanoma (LMM). Accurate diagnosis and staging are crucial for determining appropriate management strategies.

Adherence to melanoma screening and surveillance skin check schedules tailored to personal risk.

Population-wide skin cancer screening is not currently recommended in most countries. Instead, most clinical guidelines incorporate risk-based recommendations for skin checks, despite limited evidence around implementation and adherence to recommendations in practice. We aimed to determine adherence to personal risk-tailored melanoma skin check schedules and explore reasons influencing adherence.

The SLICE-3D dataset: 400,000 skin lesion image crops extracted from 3D TBP for skin cancer detection.

AI image classification algorithms have shown promising results when applied to skin cancer detection. Most public skin cancer image datasets are comprised of dermoscopic photos and are limited by selection bias, lack of standardization, and lend themselves to development of algorithms that can only be used by skilled clinicians. The SLICE-3D ("Skin Lesion Image Crops Extracted from 3D TBP") dataset described here addresses those concerns and contains images of over 400,000 distinct skin lesions from seven dermatologic centers from around the world.

Patient demographic characteristics and risk factors associated with sun protection behaviours in specialist melanoma clinics.

Objective: We investigated the association between sun protection behaviours and demographic and melanoma risk characteristics of patients attending Australian melanoma specialist clinics. This may assist in targeting and tailoring melanoma prevention patient education for people at high-risk and specific population subgroups.

Methods: A cross-sectional analysis of questionnaire data collected from participants attending the dermatology clinics at two major melanoma centres in Sydney, Australia between February 2021 and September 2023.

Examining labelling guidelines for AI-based software as a medical device: A review and analysis of dermatology mobile applications in Australia.

In recent years, there has been a surge in the development of AI-based Software as a Medical Device (SaMD), particularly in visual specialties such as dermatology. In Australia, the Therapeutic Goods Administration (TGA) regulates AI-based SaMD to ensure its safe use. Proper labelling of these devices is crucial to ensure that healthcare professionals and the general public understand how to use them and interpret results accurately.

Skin cancer referrals by nonmedical practitioners: a prospective observational study.

The aim of this study was to investigate the appropriateness of suspected skin cancer referrals made by nonmedical practitioners (NMPs) and compare this with referrals made by local general practitioners (GPs). Data were collected prospectively from patients referred from primary care to a UK hospital dermatology department. The profession of the referrer was ascertained from review of referral letters and direct questioning.