Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM)

VARAS, JULIAN; CORONEL, BRANDON VALENCIA; VILLAGRÁN, IGNACIO; ESCALONA, GABRIEL; HERNANDEZ, ROCIO; SCHUIT, GREGORY; DURÁN, VALENTINA; LAGOS-VILLASECA, ANTONIA; JARRY, CRISTIAN; NEYEM, ANDRES; ACHURRA, PABLO

doi:10.1590/0100-6991e-20233605-en

Cited by 4 publications

(2 citation statements)

References 27 publications

(33 reference statements)

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“…AI presents the capability to furnish personalized feedback for surgical trainees 47 . Employing machine learning, AI can learn from databases and render informed judgments, while deep learning uses multilayer artificial neural networks for fully automated analysis of data, with the ability to learn autonomously 48 . By integrating different AI methods, the results of surgical training can be objectively reflected 49 .…”

Section: Discussionmentioning

confidence: 99%

The transition of surgical simulation training and its learning curve: a bibliometric analysis from 2000 to 2023

Zhang,

Luo,

Zhang

et al. 2024

International Journal of Surgery

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Background: Proficient surgical skills are essential for surgeons, making surgical training an important part of surgical education. The development of technology promotes the diversification of surgical training types. This study analyzes the changes in surgical training patterns from the perspective of bibliometrics, and applies the learning curves as a measure to demonstrate their teaching ability. Method: Related papers were searched in the Web of Science database using the following formula: TS=((training OR simulation) AND (learning curve) AND (surgical)). Two researchers browsed the papers to ensure that the topics of articles were focused on the impact of surgical simulation training on the learning curve. CiteSpace, VOSviewer and R packages were applied to analyze the publication trends, countries, authors, keywords and references of selected articles. Result: Ultimately, 2461 documents were screened and analyzed. The USA is the most productive and influential country in this field. Surgical endoscopy and other interventional techniques publish the most articles, while surgical endoscopy and other interventional techniques is the most cited journal. Aggarwal Rajesh is the most productive and influential author. Keyword and reference analyses reveal that laparoscopic surgery, robotic surgery, virtue reality (VR) and artificial intelligence (AI) were the hotspots in the field. Conclusion: This study provided a global overview of the current state and future trend in the surgical education field. The study surmised the applicability of different surgical simulation types by comparing and analyzing the learning curves, which is helpful for the development of this field.

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Section: Discussionmentioning

confidence: 99%

The transition of surgical simulation training and its learning curve: a bibliometric analysis from 2000 to 2023

Zhang,

Luo,

Zhang

et al. 2024

International Journal of Surgery

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“…The integration of AI promises to revolutionise the learning experience for orthopaedic practitioners with the help of simulation-based training and employing a didactic approach. 25 Multiple AI-enabled visualisation devices can offer three-dimensional presurgical planning and surgical simulation. 26…”

Section: Surgical Trainingmentioning

confidence: 99%

A comprehensive exploration of artificial intelligence in orthopaedics within lower-middle-income countries: a narrative review

Umm E Salma Shabbar Banatwala,

Muhammad Talal Ibrahim,

Reyan Hussain Shaikh

et al. 2024

J Pak Med Assoc

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Integrating Artificial Intelligence (AI) in orthopaedic within lower-middle-income countries (LMICs) promises landmark improvement in patient care. Delving into specific use cases—fracture detection, spine imaging, bone tumour classification, and joint surgery optimisation—the review illuminates the areas where AI can significantly enhance orthopaedic practices. AI could play a pivotal role in improving diagnoses, enabling early detection, and ultimately enhancing patient outcomes— crucial in regions with constrained healthcare services. Challenges to the integration of AI include financial constraints, shortage of skilled professionals, data limitations, and cultural and ethical considerations. Emphasising AI's collaborative role, it can act as a complementary tool working in tandem with physicians, aiming to address gaps in healthcare access and education. We need continued research and a conscientious approach, envisioning AI as a catalyst for equitable, efficient, and accessible orthopaedic healthcare for patients in LMICs. Keywords: Artificial Intelligence, Orthopaedics, Health Services, Patient Care, Bone Neoplasms, Physicians, precision medicine; predictive analysis

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To trust or not to trust: evaluating the reliability and safety of AI responses to laryngeal cancer queries

Ostrowska,

Kacała,

Onolememen

et al. 2024

Eur Arch Otorhinolaryngol

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Purpose As online health information-seeking surges, concerns mount over the quality and safety of accessible content, potentially leading to patient harm through misinformation. On one hand, the emergence of Artificial Intelligence (AI) in healthcare could prevent it; on the other hand, questions raise regarding the quality and safety of the medical information provided. As laryngeal cancer is a prevalent head and neck malignancy, this study aims to evaluate the utility and safety of three large language models (LLMs) as sources of patient information about laryngeal cancer. Methods A cross-sectional study was conducted using three LLMs (ChatGPT 3.5, ChatGPT 4.0, and Bard). A questionnaire comprising 36 inquiries about laryngeal cancer was categorised into diagnosis (11 questions), treatment (9 questions), novelties and upcoming treatments (4 questions), controversies (8 questions), and sources of information (4 questions). The population of reviewers consisted of 3 groups, including ENT specialists, junior physicians, and non-medicals, who graded the responses. Each physician evaluated each question twice for each model, while non-medicals only once. Everyone was blinded to the model type, and the question order was shuffled. Outcome evaluations were based on a safety score (1–3) and a Global Quality Score (GQS, 1–5). Results were compared between LLMs. The study included iterative assessments and statistical validations. Results Analysis revealed that ChatGPT 3.5 scored highest in both safety (mean: 2.70) and GQS (mean: 3.95). ChatGPT 4.0 and Bard had lower safety scores of 2.56 and 2.42, respectively, with corresponding quality scores of 3.65 and 3.38. Inter-rater reliability was consistent, with less than 3% discrepancy. About 4.2% of responses fell into the lowest safety category (1), particularly in the novelty category. Non-medical reviewers' quality assessments correlated moderately (r = 0.67) with response length. Conclusions LLMs can be valuable resources for patients seeking information on laryngeal cancer. ChatGPT 3.5 provided the most reliable and safe responses among the models evaluated.

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Innovations in surgical training: exploring the role of artificial intelligence and large language models (LLM)

Cited by 4 publications

References 27 publications

The transition of surgical simulation training and its learning curve: a bibliometric analysis from 2000 to 2023

The transition of surgical simulation training and its learning curve: a bibliometric analysis from 2000 to 2023

A comprehensive exploration of artificial intelligence in orthopaedics within lower-middle-income countries: a narrative review

To trust or not to trust: evaluating the reliability and safety of AI responses to laryngeal cancer queries

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