Bibliographic record
Abstract
I f mobile is already entrenched as the digital technology shaping our everyday lives, Artificial Intelligence (AI) is on the threshold of being the next all-pervasive and transformative development.The automation of health care by using machines that behave "intelligently" is the major application of AI in the health sector, encompassing everything from simple pattern recognition to "smart" objects like an AI-driven insulin pump to predictive data analytics (identifying, for example, patients most at risk for hospital readmission).AI is presently garnering attention and building momentum because the technology requirements are now being met.This includes computing power and storage, cloud computing, and most importantly the availability of big data sets.Add to this the ubiquitous connectivity that enables the Internet of Things (networked devices), which have the potential to act as the mechanical "body" to AI's mechanical "brain."As in the case of mobile connectivity, the addition of "intelligence" brings a new dimension to digitization.A force for long term change in healthcare, AI's impact on both clinical practice and patient experience, whether in methods or access, is both direct--for example, in new automated clinical tools--and indirect, through its centrality to the data-driven research that makes the emerging model of precision medicine possible.To date, much of the expansion of AI-driven health applications has been in "smart" services/products around diagnostics: for example, smart monitors, imaging analysis, and screening tools.Imaging may represent as much as 90% of all medical data.In the news recently have been a number of screening tools using automated analysis of retinal scans, whether to diagnose age-related macular degeneration or diabetic retinopathy or identify patients at risk for heart disease.AI tools that aid in clinical decision-making support are a related area of growth.These use the study of personal health information to inform treatment decisions for individual patients, through predictive models that anticipate how a patient will respond to a particular therapy.One relevant example is Microsoft's international collaborative eye care project (MINE), which has ongoing projects that apply machine learning to the rate of change of myopia in children or predicting outcomes of refractive surgery.AI is also driving the expansion of telehealth and telehomecare.Ranging from simple bots for triage and patient education to intelligent assistants (think Amazon's Alexa) that offer homecare/caregiver support to smart homes of the near-future that incorporate continuous remote monitoring of the health of the elderly, the automation of simple tasks fills gaps in care that can occur when the demand for services--as is all but inevitable in an aging population--far outstrips the supply of care providers.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.007 |
| Insufficient payload (model declined to judge) | 0.017 | 0.004 |
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".