When the Virtual Becomes Reality: An Environmental Scan of the Presence of Virtual Reality and Artificial Intelligence in Health and Cancer Care Environments
Bibliographic record
Abstract
The author’s interest in the subject matter of this paper was peaked during a Grand Rounds presentation at the Tom Baker Cancer Centre in September 2017, lead by a junior oncologist practicing at this facility. Despite the seemingly endless opportunities artificial intelligence and virtual reality bring to the cancer care environment, namely in providing assistance during surgery as well as the ability to enhance patient treatment regimens, the Grand Rounds presenter cautioned that considerable debate exists over whether or not an artificial entity can replace or match human cognition, which has resulted in smaller uptake and awareness of this new technology than was hoped for. Further discussions on artificial intelligence and virtual reality with a colleague from the grey literature community resolved the author’s decision to conduct an environmental scan, not only of the existing literature on AI and VR in a cancer care and public health domain, but more importantly seek to understand awareness of these technological marvels among oncologists working at the Tom Baker Cancer Centre in Calgary, Alberta, Canada.Following approval by the executive director at the Tom Baker Cancer Centre, as well as follow-up through the ARECCI Ethics Guideline and Screening Tool, which determined lowest possible risk to participants as a result of participating in this venture, a brief opened-ended one-question survey was sent to all oncologists (~110) working at or affiliated with this cancer care facility on February 21, 2018. The question posed was as follows:Please provide the citations of any papers that you have ever encountered in your practice/research which discuss the use of virtual reality or artificial intelligence (either specifically in cancer or more generally in public health)The survey remained open until April 30, 2018. Despite the extraordinarily lengthy working hours demanded of oncologists working at the Tom Baker, along with several other competing factors, 12 responses were received from the 110 physicians polled across the Calgary Zone, yielding a response rate of 10.9% While some respondents openly admitted that they were not particularly familiar with the subject matter at hand, others willingly shared either complete citations to papers and/or grey literature information (conference, webinars, proceedings, etc.), where additional papers were referenced. All told, responses from this survey culminated in 13 unique papers being identified that foretold of the influence of AI and VR in not only the cancer field, but healthcare in general.This data set is affiliated with GL 20, the 20th International Grey Literature Conference, held at Loyola University, New Orleans from December 3-4, 2018/ The presentation slides were delivered at GL 20 and were published in the GL 20 Conference Book, produced by GreyNet. The accompanying full-text paper will be published by GreyNet in the GL20 Conference Proceedings, scheduled for release in February 2019.
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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 | 0.007 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.008 | 0.022 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".