Feasibility of Incorporating Voice Technology and Virtual Assistants in Cardiovascular Care and Clinical Trials
Bibliographic record
Abstract
PURPOSE OF REVIEW: With the rising cost of cardiovascular clinical trials, there is interest in determining whether new technologies can increase cost effectiveness. This review focuses on current and potential uses of voice-based technologies, including virtual assistants, in cardiovascular clinical trials. RECENT FINDINGS: Numerous potential uses for voice-based technologies have begun to emerge within cardiovascular medicine. Voice biomarkers, subtle changes in speech parameters, have emerged as a potential tool to diagnose and monitor many cardiovascular conditions, including heart failure, coronary artery disease, and pulmonary hypertension. With the increasing use of virtual assistants, numerous pilot studies have examined whether these devices can supplement initiatives to promote transitional care, physical activity, smoking cessation, and medication adherence with promising initial results. Additionally, these devices have demonstrated the ability to streamline data collection by administering questionnaires accurately and reliably. With the use of these technologies, there are several challenges that must be addressed before wider implementation including respecting patient privacy, maintaining regulatory standards, acceptance by patients and healthcare providers, determining the validity of voice-based biomarkers and endpoints, and increased accessibility. SUMMARY: Voice technology represents a novel and promising tool for cardiovascular clinical trials; however, research is still required to understand how it can be best harnessed.
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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 | 0.046 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.005 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| 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; both teacher heads agree on what is shown here.
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".