Professional implications of introducing artificial intelligence in healthcare: an evaluation using radiation medicine as a testing ground
Bibliographic record
Abstract
Abstract Aim This study will evaluate radiation medicine professionals’ perceptions of clinical and professional risks and benefits, and the evolving roles and responsibilities with artificial intelligence (AI). Methods Radiation oncologists (ROs), medical physicists (MPs), treatment planners (TP-RTTs) and treatment delivery radiation therapists (TD-RTTs) at a cancer centre in preliminary stages of implementing an AI-enabled treatment planning system were invited to participate in uniprofessional focus groups. Semi-structured scripts addressed the perceptions of AI, including thoughts regarding changing roles and competencies. Sessions were audiorecorded, transcribed and coded thematically through consensus-building. Results A total of 24 participants (four ROs, five MPs, seven TP-RTTs and eight TD-RTTs) were engaged in four focus groups of 58 minutes average duration (range 54–61 minutes). Emergent themes addressed AI’s impact on quality of care, changing professional tasks and changing competency requirements. Time-consuming repetitive tasks such as delineating targets, generating treatment plans and quality assurance were thought conducive to offloading to AI. Outcomes data and adaptive planning would be incorporated into clinical decision-making. Changing workload would necessitate changing skills, prioritising plan evaluation over generation and increasing interprofessional communication. All groups discussed AI reducing the need for TP-RTTs, though displacement was thought more likely than replacement. Conclusions It is important to consider how professionals perceive AI to be proactive in informing change, as gains in quality and efficiency will require new workflows, skills and education.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; a candidate call from one teacher head, 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".