AI-Enhanced Healthcare: Integrating ChatGPT-4 in ePROs for Improved Oncology Care and Decision-Making: A Pilot Evaluation
Bibliographic record
Abstract
BACKGROUND: Since 2023, ChatGPT-4 has been impactful across several sectors including healthcare, where it aids in medical information analysis and education. Electronic patient-reported outcomes (ePROs) play a crucial role in monitoring cancer patients' post-treatment symptoms, enabling early interventions. However, managing the voluminous ePRO data presents significant challenges. This study assesses the feasibility of utilizing ChatGPT-4 for analyzing side effect data from ePROs. METHODS: Thirty cancer patients were consecutively collected via a web-based ePRO platform, reporting side effects over 4 weeks. ChatGPT-4, simulating oncologists, dietitians, and nurses, analyzed this data and offered improvement suggestions, which were then reviewed by professionals in those fields. RESULTS: Two oncologists, two dieticians, and two nurses evaluated the AI's performance across roles with 540 reviews. ChatGPT-4 excelled in data accuracy and completeness and was noted for its empathy and support, enhancing communication and reducing caregiver stress. It was potentially effective as a dietician. DISCUSSION: This study offers preliminary insights into the feasibility of integrating AI tools like ChatGPT-4 into ePRO cancer care, highlighting its potential to reduce healthcare provider workload. Key directions for future research include enhancing AI's capabilities in cancer care knowledge validation, emotional support, improving doctor-patient communication, increasing patient health literacy, and minimizing errors in AI-driven clinical processes. As technology advances, AI holds promise for playing a more significant role in ePRO cancer care and supporting shared decision-making between clinicians and patients.
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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 | 0.014 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".