Accelerating innovation by integrating artificial intelligence into a global surgery hackathon
Bibliographic record
Abstract
Background: Investigating in AI’s utility in diverse learning environments can provide insights into its broader applicability in healthcare innovation. Objectives: This study aims to assess the impact on idea generation and implications of incorporating generative AI technology into the framework of a global surgery hackathon, focusing on its use with surgical care providers in Sub-Saharan Africa. Method: A 120-minute interdisciplinary hackathon in Kenya was organised. The event featured the use of ChatGPT, a large language AI model from OpenAI, to facilitate and guide team discussions and solution development. Data was collected through direct observations and discussions among participants. Results and Conclusions: The hackathon saw active participation from ninety attendees, who were divided into ten teams of 8-12 members each. These groups utilised AI to seek information, derive inspiration, and refine their ideas. Notable challenges identified included issues related to AI-generated biases and the accuracy of information provided. The study serves as a proof-of-concept that generative AI can effectively be integrated into hackathons to foster innovation, with the caveat that future implementations should focus on developing unbiased and accurate AI models. This approach has significant potential to improve educational strategies and operational efficiency in the healthcare and technology sectors.
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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 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".