Bibliographic record
Abstract
The modern era has ushered the proliferation of new technologies, especially witnessed in the emergence of the nascent artificial intelligence (AI) sector. The use of AI is largely multifaceted, proving useful in various industries such as healthcare - however, it may also allow for deleterious effects to occur. The use of AI in healthcare settings can work to extend and augment the quality of patients’ lives. Notwithstanding this, health AI enshrines various perils including the lack of patient privacy, algorithm bias - particularly on marginalized and racialized communities. This is ultimately compounded by the absence of ethical framework governing the usage of AI in healthcare settings. Specifically, this article seeks to explore whether or not the use of health AI is a potential prospect or peril; considering its duality. To investigate this topic, this article will utilize an interdisciplinary approach – drawing from domains such as: sociology, socio-legal and socio-medical climates. Secondary data will be primarily sourced via peer-reviewed journal articles, textbooks, and reliable contemporary websites. This study finds that health AI remains a greater prospect - reinforcing the quality and elongates the duration of the human lifespan. It concludes with a call to action to inform the success of health AI in praxis: namely, the need to incorporate the aforementioned topics within medical pedagogy and ethical frameworks.
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.
How this classification was reachedexpand
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".