The goal of this clinical trial is to learn whether an AI-enhanced, game-based laparoscopic simulation tool can improve laparoscopic skills training and help increase surgical capacity in surgical trainees and other healthcare professionals learning laparoscopic surgery. The main questions it aims to answer are:

• Does an AI-enhanced game-based simulator lead to faster and/or higher quality acquisition of laparoscopic technical skills than a standard box trainer?
• Is AI-enhanced game-based simulation a feasible and scalable model for laparoscopic skills training across diverse healthcare settings?
• Researchers will compare training with the Laptitude AI-enhanced game-based simulator to training with a standard laparoscopic box trainer to see if the AI-enhanced approach results in better performance on validated laparoscopic skills assessments and more efficient training.

Participants will:

• Be randomly assigned to train using either the Laptitude AI-enhanced game-based simulator or a standard box trainer.
• Complete a structured programme of laparoscopic training tasks.
• Undergo standardized assessments of laparoscopic skills performance during and/or after the training period.

Surgery is an indispensable part of healthcare but it is lacking resources. It is estimated that an additional 143 million additional surgical operations are needed each year and that 1.5 million deaths would be prevented if these operations were available. Over the next decade, the lack of surgery is projected to cost $10 billion in lost global gross domestic product (GDP).

There is an urgent need to train surgeons as this expertise is scarce. However, experience is unpredictable, skills remain unquantified, trainees require supervision, and surgeons undergo long periods of training following medical school. Surgical training which is based on graduated responsibility, defined as the progressive accumulation of skill by surgical residents that allows for the granting of greater involvement and independence by senior surgeons, forms much of the groundwork for surgical residency training.

Laparoscopic techniques have transformed surgery, being associated with less pain, lower infection rates and shorter length of stay. Of the 143 million additional operations required to meet basic needs and save lives, 28 million (20%) could be done using minimally invasive techniques. Surgeons need comprehensive training in this area through skill-based models and measurable assessment of skill acquisition to effectively track and understand the development of competencies. There is an added interest in understanding if non-MDs (non-medical doctors), undergoing similar training in laparoscopic procedures can reproduce the skills of conventionally trained surgeons.

Simulator training is a skills-based model for developing effective laparoscopic surgical skills, but existing simulators may not accurately represent real life conditions. There is a need to identify and develop high-fidelity simulators which can substitute for operative time in skill-acquisition.

The creation and deployment of complex interventions like laparoscopic skills training is challenging. Technological innovations are often complex in themselves, and they necessitate expertise and backing from a broad group of stakeholders. Furthermore, these interventions must be sufficiently flexible to meet needs across a diverse range of healthcare settings. The successful delivery of health technology programs, including laparoscopic skills training, necessitates the strong and early engagement of patients, practitioners, and policy makers. This shifts the focus from a binary question of effectiveness, to whether interventions can be acceptable, implementable, cost-effective, scalable, and transportable across settings.

This study will therefore evaluate approaches to increase surgical capacity based on the creation of new models for laparoscopic surgical skills acquisition, including this randomised controlled trial comparing of an AI-enhanced game-based simulation tool (Laptitude, Grendel Games) compared with a standard laparoscopic simulator box trainer.

Inclusion Criteria:

• Novice medical students: no previous experience previous simulation training and/or operative experience.
• Novice medically-qualified doctors (MDs): no previous experience previous simulation training and/or operative experience.
• Intermediate experience medical students: previously used laparoscopic simulator box trainer.
• Intermediate experience medically-qualified doctors (MDs): previously used laparoscopic simulator box trainer.

Exclusion Criteria:

• Inability to provide informed consent.
• Inability to attend for training and assessment.

Full anonymised individual participant data will be available for sharing with bona fide research teams upon application to the Principal Investigator.

Data will be made available 6 months after completion of the study and will be available indefinitely.