Preparing for the future: How organizations can prepare boards, leaders, and risk managers for artificial intelligence
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
Artificial Intelligence (AI) is the notion of machines mimicking complex cognitive functions usually associated with humans, such as reasoning, predicting, planning, and problem-solving. With constantly growing repositories of data, improving algorithmic sophistication and faster computing resources, AI is becoming increasingly integrated into everyday use. In healthcare, AI represents an opportunity to increase safety, improve quality, and reduce the burden on increasingly overstretched systems. As applications expand, the need for responsible oversight and governance becomes even more important. Artificial intelligence in the delivery of healthcare carries new opportunities and challenges, including the need for greater transparency, the impact AI tools may have on a larger number of patients and families, and potential biases that may be introduced by the way an AI platform was developed and built. This study provides practical guidance in the development and implementation of AI applications in healthcare, with a focus on risk identification, management, and mitigation.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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