A “Do No Harm” Novel Safety Checklist and Research Approach to Determine Whether to Launch an Artificial Intelligence–Based Medical Technology: Introducing the Biological-Psychological, Economic, and Social (BPES) Framework
Bibliographic record
Abstract
Given the impact artificial intelligence (AI)-based medical technologies (hardware devices, software programs, and mobile apps) can have on society, debates regarding the principles behind their development and deployment are emerging. Using the biopsychosocial model applied in psychiatry and other fields of medicine as our foundation, we propose a novel 3-step framework to guide industry developers of AI-based medical tools as well as health care regulatory agencies on how to decide if a product should be launched-a "Go or No-Go" approach. More specifically, our novel framework places stakeholders' (patients, health care professionals, industry, and government institutions) safety at its core by asking developers to demonstrate the biological-psychological (impact on physical and mental health), economic, and social value of their AI tool before it is launched. We also introduce a novel cost-effective, time-sensitive, and safety-oriented mixed quantitative and qualitative clinical phased trial approach to help industry and government health care regulatory agencies test and deliberate on whether to launch these AI-based medical technologies. To our knowledge, our biological-psychological, economic, and social (BPES) framework and mixed method phased trial approach are the first to place the Hippocratic Oath of "Do No Harm" at the center of developers', implementers', regulators', and users' mindsets when determining whether an AI-based medical technology is safe to launch. Moreover, as the welfare of AI users and developers becomes a greater concern, our framework's novel safety feature will allow it to complement existing and future AI reporting guidelines.
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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 | 0.506 | 0.431 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.011 | 0.004 |
| Science and technology studies | 0.009 | 0.049 |
| Scholarly communication | 0.015 | 0.028 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".