A comprehensive review of artificial intelligence methods and applications in skin cancer diagnosis and treatment: Emerging trends and challenges
Bibliographic record
Abstract
A substantial body of research has been published in artificial intelligence due to the rising incidence of skin cancer, the scarcity of specialized healthcare professionals, and rapid advancements in automated diagnosis and treatment methods. We present a comprehensive review using text mining to identify key themes of artificial intelligence in skin cancer diagnosis and treatment research. Our text mining model uncovers nine topics, including dermatological data, machine and deep learning methods, segmentation, data generation, melanoma, basal cell carcinoma, model validation, and treatment. We extensively review the literature on each topic to offer valuable insights and highlight research gaps. Our findings indicate a need for a comprehensive and diverse dataset that includes lesion images, clinical data, and treatment information. In addition, our topic analysis ranks deep learning-based diagnosis as the top topic, followed by data generation and melanoma diagnosis. These insights demonstrate the bias towards deep learning methods and the shortage of studies on rare and precancerous skin lesions. Despite the gaps defined, artificial intelligence can be utilized for triage, initial screening, a second opinion in diagnosing complex cases, and an educational resource. Additionally, artificial intelligence models can enhance patient outcomes through early diagnosis, treatment recommendation, and treatment response prediction.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".