An AI-assisted chatbot for radiation safety education in radiotherapy
Bibliographic record
Abstract
Abstract Purpose . We created a virtual assistant chatbot that will serve as a tool for radiation safety training for clinical staff, including radiation oncologist, radiotherapist and medical physicist, in cancer treatment. The Bot can also be used to test their knowledge on radiation safety. Methods . The Bot was constructed using IBM’s Watson Assistant functionalities on the IBM cloud. A layered structure approach was used in the workflow of the Bot to interact with the user. Through answering various questions concerning radiation safety in radiotherapy, the users can learn the essential information to gain knowledge, when working in a cancer centre/hospital. Results . The user interface of the Bot was a front-end window operating on Internet, which could easily be accessed by any Internet-of-things such as smartphone, tablet or laptop. The Bot could communicate with the user for radiation safety Q&A. If the Bot could not identify what the user needed, the Bot would provide a list of options as a guidance. Using the natural language processing in communication, knowledge transfer from the Bot to user could be carried out. Conclusion . It is concluded that the radiation safety chatbot worked as intended, utilizing all the tools provided by the IBM Watson Assistant. The Bot could provide radiation safety information to the radiation staff effectively, and be used in staff training in radiotherapy.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".