Digital health and artificial intelligence in kidney research: a report from the 2020 Kidney Disease Clinical Trialists (KDCT) meeting
Bibliographic record
Abstract
The exponential growth in digital technology coupled with the global coronavirus disease 2019 pandemic is driving a profound change in the delivery of medical care and research conduct. The growing availability of electronic monitoring, electronic health records, smartphones and other devices and access to ever greater computational power provides not only new opportunities, but also new challenges. Artificial intelligence (AI) exemplifies the potential of this digital revolution, which also includes other tools such as mobile health (mHealth) services and wearables. Despite digital technology becoming commonplace, its use in medicine and medical research is still in its infancy, with many clinicians and researchers having limited experience with such tools in their usual practice. This article, derived from the 'Digital Health and Artificial Intelligence' session of the Kidney Disease Clinical Trialists virtual workshop held in September 2020, aims to illustrate the breadth of applications to which digital tools and AI can be applied in clinical medicine and research. It highlights several innovative projects incorporating digital technology that range from streamlining medical care of those with acute kidney injury to the use of AI to navigate the vast genomic and proteomic data gathered in kidney disease. Important considerations relating to any new digital health project are presented, with a view to encouraging the further evolution and refinement of these new tools in a manner that fosters collaboration and the generation of robust evidence.
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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 | 0.096 | 0.090 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.013 | 0.009 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.012 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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".