Predicting psoriasis severity using machine learning: a systematic review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: In dermatology, the applications of machine learning (ML), an artificial intelligence (AI) subset that enables machines to learn from experience, have progressed past the diagnosis and classification of skin lesions. A lack of systematic reviews exists to explore the role of ML in predicting the severity of psoriasis. OBJECTIVES: To identify and summarize the existing literature on predicting psoriasis severity using ML algorithms and to identify gaps in -current clinical applications of these tools. METHODS: OVID Embase, OVID MEDLINE, ACM Digital Library, Scopus and IEEE Xplore were searched from inception to August 2024. RESULTS: In total, 30 articles met our inclusion criteria and were included in this review. One article used serum biomarkers, while the remaining 29 used image-based models. The most common severity assessment score employed by these ML models was the Psoriasis Area and Severity Index score, followed by body surface area, with 15 and 5 articles, respectively. CONCLUSIONS: The small size and heterogeneity of the existing body of literature are the primary limitations of this review. Progress in assessing skin lesion severity through ML in dermatology has advanced, but prospective clinical applications remain limited. ML and AI promise to improve psoriasis management, especially in nonimage-based applications requiring further exploration. Large-scale prospective trials using diverse image datasets are necessary to evaluate and predict the clinical value of these predictive AI models.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it