Improving surgery training in gynaecology and obstetrics has been identified as a Presidential priority activity for the 2024-2026 period for the Royal College of Obstetricians and Gynaecologists.
The objective is to tackle the challenges currently faced within the specialty such as addressing deficiencies in how surgical skills are provided, the lack of uniformity with simulated training and the future of obstetric and gynaecological operating. Furthermore, the landscape of surgical training is rapidly evolving with the advent of artificial intelligence (AI) and the project wishes to focus on how this innovative technology can be integrated into education into surgical training.
AI is a multidisciplinary field of computer science whereby intelligent agents are capable of performing tasks that typically require human-like cognition. AI systems employ techniques such as machine learning, deep learning, natural language processing, computer vision, and expert systems to perceive, reason, learn, and adapt to new information. Machine learning enables computers to learn from data and make predictions or decisions without explicit programming, while deep learning uses artificial neural networks for sophisticated pattern recognition.
To support research in this field, the project is offering a research grant for a doctor or potentially, two doctors (working in O&G or with a special interest in AI/simulation) to join the team. A grant of up to £6,000 is available to aid research in the implementation of AI into surgical skill training, highlighting the differences AI could offer for development of surgical skills, its role in progression feedback and how AI can be embedded in the generation of trainee doctors rotas. The successful applicant(s) will be supported by the wider surgical skills project team to design and implement their idea.
The expectation would be that after a maximum six-month period starting from March 2025, the research grant would enable the applicant to fulfil the aims of their AI project, which would form a cornerstone of the project.
For the application, please include a summary (maximum 750 words) of your proposal to include title, aims, how this research will improve surgical skills training, planned methodology, predicted timeframe and potential costs for research.
Please include a two-page CV (anonymised) and a brief personal statement to highlight any relevant experience and how you aim to manage the project alongside any existing training or working commitments.
Eligibility
To be eligible to apply for this bursary you must be a current UK based doctor (with full GMC registration) working in obstetrics and gynaecology, or with an associated interest.
How to apply
Applications will be hosted on the Oxford Abstracts platform below.
Closing date is Monday 27 January 2025.
Please register for an account using your email address. For further guidance on how to submit your application please see Oxford Abstracts' guidance on making a submission.
Applications will be processed in accordance with the RCOG Data Protection Policy. For more information please visit RCOG Privacy Policy.
How will your submission be judged?
Your submission will be reviewed against the following criteria:
- Impact: Evaluate the significance and magnitude of gynaecology training issues and potential solutions. Consider how AI can be implemented to improve opportunities for the maximum amount of O&G Residents to ultimately change our current training opportunities.
- Deliverability: Assess the practicality and achievability of implementing the solution. In your proposal address factors such as technological feasibility, resource requirements, training implications, and potential barriers to implementation.
- Innovation: Evaluate the level of novelty and advancement offered by the solution. Consider whether the approach represents a significant departure from current practices.
- Accessibility: Consider the extent to which the solution or approach can be accessible to every trainee irrespective of grade or location.
If you have any questions please email specialtytrainingCCT@rcog.org.uk.
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