Role of Artificial Intelligence in Global Surgery: A Review of Opportunities and Challenges

Comprehensive overview of artificial intelligence in surgery: a systematic review and perspectives

The rapid integration of artificial intelligence (AI) into surgical practice necessitates a comprehensive evaluation of its applications, challenges, and physiological impact. This systematic review synthesizes current AI applications in surgery, with a particular focus on machine learning (ML) and its role in optimizing preoperative planning, intraoperative decision-making, and postoperative patient management. Using PRISMA guidelines and PICO criteria, we analyzed key studies addressing AI's contributions to surgical precision, outcome prediction, and real-time physiological monitoring. While AI has demonstrated significant promise-from enhancing diagnostics to improving intraoperative safety-many surgeons remain skeptical due to concerns over algorithmic unpredictability, surgeon autonomy, and ethical transparency. This review explores AI's physiological integration into surgery, discussing its role in real-time hemodynamic assessments, AI-guided tissue characterization, and intraoperative physiological modeling. Ethical concerns, including algorithmic opacity and liability in high-stakes scenarios, are critically examined alongside AI's potential to augment surgical expertise. We conclude that longitudinal validation, improved AI explainability, and adaptive regulatory frameworks are essential to ensure safe, effective, and ethically sound integration of AI into surgical decision-making. Future research should focus on bridging AI-driven analytics with real-time physiological feedback to refine precision surgery and patient safety strategies.

Technological Advancements in Augmented, Mixed, and Virtual Reality Technologies for Surgery: A Systematic Review

Recent advancements in artificial intelligence (AI) have shown significant potential in the medical field, although many applications are still in the research phase. This paper provides a comprehensive review of advancements in augmented reality (AR), mixed reality (MR), and virtual reality (VR) for surgical applications from 2019 to 2024 to accelerate the transition of AI from the research to the clinical phase. This paper also provides an overview of proposed databases for further use in extended reality (XR), which includes AR, MR, and VR, as well as a summary of typical research applications involving XR in surgical practices. Additionally, this paper concludes by discussing challenges and proposed solutions for the application of XR in the medical field. Although the areas of focus and specific implementations vary among AR, MR, and VR, current trends in XR focus mainly on reducing workload and minimizing surgical errors through navigation, training, and machine learning-based visualization. Through analyzing these trends, AR and MR have greater advantages for intraoperative surgical functions, whereas VR is limited to preoperative training and surgical preparation. VR faces additional limitations, and its use has been reduced in research since the first applications of XR, which likely suggests the same will happen with further development. Nonetheless, with increased access to technology and the ability to overcome the black box problem, XR's applications in medical fields and surgery will increase to guarantee further accuracy and precision while reducing risk and workload.

Assessing the accuracy of artificial intelligence in the diagnosis and management of orbital fractures: Is this the future of surgical decision-making?

Orbital fractures are common, but their management remains controversial. The aim of the present study was to assess the accuracy of an advanced artificial intelligence (AI) model, ChatGPT-4, in surgical decision-making, with a focus on orbital fracture diagnosis and management. A retrospective observational analysis was conducted by involving a sample of 30 orbital fracture cases diagnosed and managed at the Geneva University Hospital, Switzerland. The process involved creating patient vignettes from anonymised medical records and presenting them to ChatGPT-4 in three stages: initial diagnosis, refinement with radiological reports and surgical intervention decisions. The performance of ChatGPT-4 in providing the appropriate surgical strategy was evaluated through measures of sensitivity, specificity, positive predictive value and negative predictive value, with the actual management used as the benchmark for accuracy. The AI model could correctly diagnose the fracture in 100 % of the cases. It demonstrated a specificity of 100 % and sensitivity of 57 % for treatment recommendation, indicating its effectiveness in recognising patients who truly required an intervention; however, it demonstrated a moderate performance in correctly identifying cases that were better suited for conservative treatment. Cohen's Kappa statistic for interrater reliability of the choice of treatment was 0.44, indicating a weak level of agreement between ChatGPT and the physician's choice of treatment. The study demonstrates that AI tools such as ChatGPT-4 can offer a high degree of accuracy in diagnosing orbital fractures and recognising patients requiring surgical intervention; however, it performed less satisfactorily in correctly identifying patients who were better suited for non-surgical treatment.

Inteligencia artificial como herramienta de la cirugía global en América Latina

Introducción. América Latina presenta un problema de desigualdad en el acceso a los servicios de salud en relación con el contexto sociocultural de la población, que se acentúa en relación con las actividades quirúrgicas. Ante esta situación, la cirugía global busca soluciones que permitan zanjar la brecha. Métodos. Planteamos el uso de la inteligencia artificial (IA) como una herramienta con gran potencial para expandir el alcance de los cirujanos a las poblaciones más desatendidas de esta región. Resultados. Las potenciales aplicaciones de la IA son innumerables. En este contexto, los recursos educacionales (chatbots) y las plataformas de telemedicina podrían acercar al profesional de la salud a donde es más necesario. Los algoritmos de seguimiento postoperatorio podrían alertarnos de factores de riesgo y posibles complicaciones. Los sistemas de análisis de información facilitarían la asignación de recursos humanos y materiales para brindar una atención más oportuna. La digitalización de las labores burocráticas y administrativas reduciría la carga para el cirujano, permitiendo dedicar este tiempo a la atención de los pacientes. Conclusiones. Pese a que existen limitaciones, como el acceso a la tecnología, la inversión requerida y la barrera idiomática, si los gobiernos, los profesionales de la salud y los desarrolladores tecnológicos apuestan por aplicar esta herramienta en el campo de la cirugía, podríamos estar cerca de una revolución de la atención de salud.

Development and validation of a novel AI framework using NLP with LLM integration for relevant clinical data extraction through automated chart review

The accurate extraction of surgical data from electronic health records (EHRs), particularly operative notes through manual chart review (MCR), is complex, crucial, and time-intensive, limited by human error due to fatigue and the level of training. This study aimed to develop and validate a novel Natural Language Processing (NLP) algorithm integrated with a Large Language Model (LLM; GPT4-Turbo) to automate the extraction of spinal surgery data from EHRs. The algorithm employed a two-stage approach. Initially, a rule-based NLP framework reviewed and classified candidate segments from the text, preserving their reference segments. These segments were then verified in the second stage through the LLM. The primary outcomes of this study were the accurate extraction of surgical data, including the type of surgery, levels operated, number of disks removed, and presence of intraoperative incidental durotomies. Secondary objectives explored time efficiency, tokenization lengths, and costs. The performance of the algorithm was assessed across two validation databases, analyzing metrics such as accuracy, sensitivity, discrimination, F1-score, and precision, with 95% confidence intervals calculated using percentile-based bootstrapping. The NLP + LLM algorithm markedly outperformed all performance metrics, demonstrating significant improvements in time and cost efficiency. These results suggest the potential for widespread adoption of this technology.

Perceptions of undergraduate medical students on artificial intelligence in medicine: mixed-methods survey study from Palestine

BACKGROUND

The current applications of artificial intelligence (AI) in medicine continue to attract the attention of medical students. This study aimed to identify undergraduate medical students' attitudes toward AI in medicine, explore present AI-related training opportunities, investigate the need for AI inclusion in medical curricula, and determine preferred methods for teaching AI curricula.

METHODS

This study uses a mixed-method cross-sectional design, including a quantitative study and a qualitative study, targeting Palestinian undergraduate medical students in the academic year 2022-2023. In the quantitative part, we recruited a convenience sample of undergraduate medical students from universities in Palestine from June 15, 2022, to May 30, 2023. We collected data by using an online, well-structured, and self-administered questionnaire with 49 items. In the qualitative part, 15 undergraduate medical students were interviewed by trained researchers. Descriptive statistics and an inductive content analysis approach were used to analyze quantitative and qualitative data, respectively.

RESULTS

From a total of 371 invitations sent, 362 responses were received (response rate = 97.5%), and 349 were included in the analysis. The mean age of participants was 20.38 ± 1.97, with 40.11% (140) in their second year of medical school. Most participants (268, 76.79%) did not receive formal education on AI before or during medical study. About two-thirds of students strongly agreed or agreed that AI would become common in the future (67.9%, 237) and would revolutionize medical fields (68.7%, 240). Participants stated that they had not previously acquired training in the use of AI in medicine during formal medical education (260, 74.5%), confirming a dire need to include AI training in medical curricula (247, 70.8%). Most participants (264, 75.7%) think that learning opportunities for AI in medicine have not been adequate; therefore, it is very important to study more about employing AI in medicine (228, 65.3%). Male students (3.15 ± 0.87) had higher perception scores than female students (2.81 ± 0.86) (p < 0.001). The main themes that resulted from the qualitative analysis of the interview questions were an absence of AI learning opportunities, the necessity of including AI in medical curricula, optimism towards the future of AI in medicine, and expected challenges related to AI in medical fields.

CONCLUSION

Medical students lack access to educational opportunities for AI in medicine; therefore, AI should be included in formal medical curricula in Palestine.

Enhancing microsurgical skills in neurosurgery residents of low-income countries: A comprehensive guide

Background

The main objectives of this paper are to outline the essential tools, instruments, and equipment needed to set up a functional microsurgery laboratory that is affordable for low-income hospitals and to identify cost-effective alternatives for acquiring microsurgical equipment, such as refurbished or donated instruments, collaborating with medical device manufacturers for discounted rates, or exploring local suppliers.

Methods

Step-by-step instructions were provided on setting up the microsurgery laboratory, including recommendations for the layout, ergonomic considerations, lighting, and sterilization processes while ensuring cost-effectiveness, as well as comprehensive training protocols and a curriculum specifically tailored to enhance microsurgical skills in neurosurgery residents.

Results

We explored cost-effective options for obtaining microsurgery simulators and utilizing open-source or low-cost virtual training platforms. We also included guidelines for regular equipment maintenance, instrument sterilization, and establishing protocols for infection control to ensure a safe and hygienic learning environment. To foster collaboration between low-income hospitals and external organizations or institutions that can provide support, resources, or mentorship, this paper shows strategies for networking, knowledge exchange, and establishing partnerships to enhance microsurgical training opportunities further. We evaluated the impact and effectiveness of the low-cost microsurgery laboratory by assessing the impact and effectiveness of the established microsurgery laboratory in improving the microsurgical skills of neurosurgery residents. About microsutures and microanastomosis, after three weeks of training, residents showed improvement in "surgical time" for ten separate simple stitches (30.06 vs. 8.65 min) and ten continuous single stitches (19.84 vs. 6.51 min). Similarly, there was an increase in the "good quality" of the stitches and the suture pattern from 36.36% to 63.63%.

Conclusion

By achieving these objectives, this guide aims to empower low-income hospitals and neurosurgery residents with the necessary resources and knowledge to establish and operate an affordable microsurgery laboratory, ultimately enhancing the quality of microsurgical training and patient care in low-income countries.