Dermatology 3.0: New Technologies Transforming the Management of Skin Conditions
Bibliographic record
Abstract
ABSTRACT Integration of digital technologies in dermatology is revolutionising patient care by increasing accessibility, accuracy and personalisation. This review explores the impact of emerging digital technologies in dermatology, including teledermatology, artificial intelligence (AI), mobile applications, wearable devices and 3D imaging and printing. Teledermatology, using real‐time videoconferencing and store‐and‐forward imaging, has expanded since the COVID‐19 pandemic, improving access to dermatologic care in underserved areas. AI‐powered algorithms are being increasingly used, particularly in skin cancer detection, by helping clinicians make faster and more accurate diagnosis and treatment decisions in diverse clinical settings. AI is also improving clinical workflows, increasing automation and reducing documentation burden. Mobile health applications, including AI‐based tools, are transforming patient self‐management and monitoring. Wearable devices enable continuous monitoring of skin health and environmental factors, providing real‐time insights into conditions like atopic dermatitis and melanoma. In addition, advances in 3D imaging and printing technologies are enabling for more precise grafts and early detection of skin cancer, leading to improved clinical outcomes. Despite these advancements, significant challenges remain, including automation bias, the need for standardised validation protocols and equitable access across diverse populations. Successful integration of these technologies into clinical practice will require addressing these issues and ensuring data security, improved digital literacy and clear guidelines for their use. Future research should focus on assessing the real‐world effectiveness of these technologies and ensuring their equitable use in diverse geographies and patient populations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".