Hot Topics: Exploring Artificial Intelligence and Inflammatory Memory in the Management of Psoriatic Diseases
Bibliographic record
Abstract
The "hot topics" session of the Group for Research and Assessment of Psoriasis and Psoriatic Arthritis (GRAPPA) 2024 annual meeting and trainee symposium explored the integration of artificial intelligence (AI) in managing psoriatic diseases (PsD) and the underlying mechanisms of inflammatory memory that drive recurrence in psoriasis and psoriatic arthritis. Drs. April Armstrong and Denis Poddubnyy discussed the transformative role of AI in enhancing diagnostic accuracy, assessing disease severity, and predicting treatment responses, particularly through deep learning models such as convolutional neural networks. AI systems have shown promise in providing objective, standardized assessments for psoriasis, with applications expanding across dermatology and rheumatology. Dr. Liv Eidsmo presented insights into inflammatory memory, a phenomenon sustained by both immune and nonimmune cells, including tissue-resident memory cells and epigenetically altered keratinocytes. Eidsmo emphasized the importance of understanding the cellular and molecular pathways that contribute to disease persistence. Both AI and inflammatory memory highlight key challenges and opportunities in PsD management; future research is needed to integrate technological advancements, with a deeper understanding of the biological processes affecting treatment outcomes.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".