Safety and Precision AI for a Modern Digital Health System
Bibliographic record
Abstract
Artificial intelligence (AI) promises to revolutionize healthcare. Currently there is a proliferation of new AI applications that are being developed and beginning to be deployed across many areas in healthcare to streamline and make healthcare processes more efficient. In addition, AI has the potential to support personalized and customized precision healthcare by providing intelligent interaction with end users. However, to achieve the goal of precision AI issues and concerns related to the safety of AI, as with any new technology, must be addressed. In this article we first describe the link between AI and safety and then describe the relation of AI to the emerging study of technology-induced error. An overview of published safety issues that have been associated with introduction of AI are described and categorized. These include potential for error to arise from varied sources, including the data used to drive AI applications, and the design process of AI applications itself. In addition, lack of appropriate and rigorous testing and limited analysis of AI applications during procurement processes has also been reported. Recommendations for ensuring the safe adoption of AI technology in healthcare are discussed, focusing on the need for more rigorous testing and evaluation of AI applications, ranging from laboratory testing through to naturalistic evaluation. The application of such approaches will support safety and precision AI for a modern digital health system.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".