Model Driven Development for AI-Based Healthcare Systems: A Review
Bibliographic record
Abstract
Abstract We review our experience with integrating Artificial Intelligence (AI) into healthcare systems following the Model-Driven Development (MDD) approach. At a time when AI has the potential to instigate a paradigm shift in the health sector, better integrating healthcare experts in the development of these technologies is of paramount importance. We see MDD as a useful way to better embed non-technical stakeholders in the development process. The main goal of this review is to reflect on our experiences to date with MDD and AI in the context of developing healthcare systems. Four case studies that fall within that scope but have different profiles are introduced and summarised: the MyMM application for Multiple Myeloma diagnosis; CNN-HAR, that studies the ability to do AI on the edge for IoT-supported human activity recognition; the HIPPP web based portal for patient information in public health; and Cinco de Bio, a new model driven platform used for the first time to support a better cell-level understanding of diseases. Based on the aforementioned case studies we discuss the characteristics, the challenges faced and the postive outcomes achieved.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".