A Q&A with the Qualcomm Tricorder XPRIZE Winners
Bibliographic record
Abstract
In recent years, there has been an increasing demand for “health consumer technologies”—small, user-friendly, point-of-care devices that can be operated by untrained individuals to evaluate health and disease. The Qualcomm Tricorder XPRIZE was a $10 million global competition to stimulate innovation and integration of advanced technologies, enabling reliable health diagnoses anywhere and anytime (https://tricorder.xprize.org/). The competition called for the development of a device that could diagnose 12 diseases (and the absence of disease) and capture 5 real-time health vital signs independent of a healthcare professional or facility. Devices also could not weigh >5 pounds and required the capability to transmit data to a cloud storage and computing system. Although diagnostic accuracy was a key component, the competition was unique in its strong emphasis on user adoption and experience. In fact, only teams scoring the highest on the consumer experience evaluations were eligible to win the overall competition. After the competition's launch in 2012, >300 teams joined. In April 2017, 3 winners were announced. Final Frontier Medical Devices, led by Basil Harris, was announced the highest performing team and received $2.6 million for their achievement. Dynamical Biomarkers Group, led by Chung Kang Peng, received $1 million for second place. Cloud DX, led by Sonny Kohli, was also recognized as XPRIZE's first Bold Epic Innovator and received $100000. Here we learn more about these devices and their inventors, including what inspired their teams and what hurdles they were able to overcome. Describe your device and what it does. Basil Harris: Our tricorder is an autonomous medical diagnostic device that leverages technological advances in wireless monitoring, artificial intelligence, and affordable point-of-care biomedical processes. Our prototype tricorder, called DxtER, comprises several innovative sensors that together form a comprehensive healthcare kit. The diagnostic engine, or the brain of the device, is an app on a …
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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; 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".