When the Virtual Becomes Reality: An Environmental Scan of the Presence of Virtual Reality and Artificial Intelligence in Health and Cancer Care Environments
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,007 | 0,026 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,001 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,003 | 0,004 |
| Études des sciences et des technologies | 0,008 | 0,022 |
| Communication savante | 0,014 | 0,019 |
| Science ouverte | 0,001 | 0,009 |
| Intégrité de la recherche | 0,003 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,006 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».