EPIDEMIOLOGY AND HEALTH DATA INSIGHTS
Review Article
Jun 02, 2026 3 2

Artificial Intelligence in Surgery: Current Applications and Future Prospects

Peter Aduvie Josiah 1 * ,
Chinedu Nwosu-Ijiomah 2,
Ndidi Atasie Eboh 3
1 Department of Medicine and Surgery, College of Health Sciences, Niger Delta University, Bayelsa State, Nigeria2 Department of Health Informatics, School of Computing, University of West London, UK3 Department of Nursing Science, Stark State College, North Canton, OH, USA* Corresponding Author
Epidemiology and Health Data Insights, 2(4), 2026, ehdi043, https://doi.org/10.63946/ehdi/18696
Publication date: Jun 02, 2026
Full Text (PDF)

ABSTRACT

Artificial intelligence (AI) is transforming the practice of surgery through the support of clinical decision-making, improved accuracy and efficiency, and better patient outcomes across the perioperative period. This review evaluates and critically analyzes current, technological and evidence-based applications of AI in surgery. Machine learning, deep learning, computer vision and natural language processing are some of the core AI technologies that are enabling developments in all phases of surgery. Some of these applications include, improved preoperative diagnosis, intraoperative real-time imaging, workflow analysis, robotic surgery assistance and intraoperative clinical decision support. Postoperatively, the potential of AI is also vast. Through the use of predictive models for surveillance and early complications detection, as well as remote management of patients, AI is optimizing postoperative care. 
 Although the findings suggest promise for the future, the clinical adoption of AI in surgery is limited by a number of issues, including data quality and heterogeneity, lack of validation in large prospective studies, potential for bias, ethical concerns and high costs of implementation. Other factors including clinician acceptance, data privacy, regulatory approval and medico-legal implications need to be addressed before any new technology is widely adopted.
 Future directions of AI in surgery includes progression to semi-autonomous systems that augments the efforts of the surgeon, integration with emerging technologies such as genomics and digital twins, and increased use in low resource settings to help address existing global inequities in surgical care. For successful AI adoption in surgery, AI systems will need to undergo robust validation, require interdisciplinary approaches, and all systems must be developed in a manner that is not only transparent but also clinically appropriate for surgeons, so that it can maximise the benefits of human talent in the operating room while maintaining patient safety.
 

KEYWORDS

Artificial Intelligence Robotic Surgery Machine Learning Perioperative Care Clinical Decision Support

CITATION (Vancouver)

Josiah PA, Nwosu-Ijiomah C, Eboh NA. Artificial Intelligence in Surgery: Current Applications and Future Prospects. Epidemiology and Health Data Insights. 2026;2(4):ehdi043. https://doi.org/10.63946/ehdi/18696
APA
Josiah, P. A., Nwosu-Ijiomah, C., & Eboh, N. A. (2026). Artificial Intelligence in Surgery: Current Applications and Future Prospects. Epidemiology and Health Data Insights, 2(4), ehdi043. https://doi.org/10.63946/ehdi/18696
Harvard
Josiah, P. A., Nwosu-Ijiomah, C., and Eboh, N. A. (2026). Artificial Intelligence in Surgery: Current Applications and Future Prospects. Epidemiology and Health Data Insights, 2(4), ehdi043. https://doi.org/10.63946/ehdi/18696
AMA
Josiah PA, Nwosu-Ijiomah C, Eboh NA. Artificial Intelligence in Surgery: Current Applications and Future Prospects. Epidemiology and Health Data Insights. 2026;2(4), ehdi043. https://doi.org/10.63946/ehdi/18696
Chicago
Josiah, Peter Aduvie, Chinedu Nwosu-Ijiomah, and Ndidi Atasie Eboh. "Artificial Intelligence in Surgery: Current Applications and Future Prospects". Epidemiology and Health Data Insights 2026 2 no. 4 (2026): ehdi043. https://doi.org/10.63946/ehdi/18696
MLA
Josiah, Peter Aduvie et al. "Artificial Intelligence in Surgery: Current Applications and Future Prospects". Epidemiology and Health Data Insights, vol. 2, no. 4, 2026, ehdi043. https://doi.org/10.63946/ehdi/18696

REFERENCES

  1. Wu J, Zhang H, Shao J, Chen D, Xue E, Huang S, et al. Healthcare for older adults with multimorbidity: a scoping review of reviews. Clin Interv Aging. 2023;18:1723–1735. doi:10.2147/CIA.S425576
  2. Alharbi S, Aljohani B, Elmasry L, Baldovino F, Raviz K, Altowairqi L, et al. Reduction of hospital bed cost for inpatient overstay through optimisation of patient flow. BMJ Open Qual. 2023;12(2):e002142. doi:10.1136/bmjoq-2022-002142
  3. Batiha AM. Cost‑Reduction Strategies and Patient Care Quality: Insights From Hospital Leaders. J Eval Clin Pract. 2026;32(2):e70351. doi:10.1111/jep.70351
  4. Loftus TJ, Tighe PJ, Filiberto AC, Efron PA, Brakenridge SC, Mohr AM, et al. Artificial Intelligence and Surgical Decision-making. JAMA Surg. 2020;155(2):148–158. doi:10.1001/jamasurg.2019.4917
  5. Udyavar R, Cornwell EE 3rd, Havens JM, Hashmi ZG, Cooper Z, Askari R, et al. Surgeon-driven variability in emergency general surgery outcomes: Does it matter who is on call? Surgery. 2018;164(5):1109–1116. doi:10.1016/j.surg.2018.07.008
  6. Reijmerink IM, van der Laan MJ, Wietasch JKG, Hooft L, Cnossen F. Impact of fatigue in surgeons on performance and patient outcome: systematic review. Br J Surg. 2024;111(1):znad397. doi:10.1093/bjs/znad397
  7. Wah JNK. The rise of robotics and AI-assisted surgery in modern healthcare. J Robot Surg. 2025;19:311. doi:10.1007/s11701-025-02485-0
  8. Soori M, Arezoo B, Dastres R. Artificial intelligence, machine learning and deep learning in advanced robotics: a review. Cognit Robot. 2023;3:54–70. doi:10.1016/j.cogr.2023.04.001
  9. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med. 2019;25(1):44–56. doi:10.1038/s41591-018-0300-7
  10. Guo C, He Y, Shi Z, Wang L. Artificial intelligence in surgical medicine: a brief review. Ann Med Surg (Lond). 2025;87(4):2180–2186. doi:10.1097/MS9.0000000000003115
  11. Iftikhar M, Saqib M, Zareen M, Mumtaz H. Artificial intelligence: revolutionizing robotic surgery: review. Ann Med Surg (Lond). 2024;86(9):5401–5409. doi:10.1097/MS9.0000000000002426
  12. Satapathy P, Pradhan KB, Rustagi S, Suresh V, Al-Tameemi ZHA. Application of machine learning in surgery research: current uses and future directions – editorial. Int J Surg. 2023;109(6):1550–1551. doi:10.1097/JS9.0000000000000421
  13. Thankappan K. Artificial Intelligence and Machine Learning in Head and Neck Oncology. J Head Neck Physicians Surg. 2022;10(2):117–120. doi:10.4103/jhnps.jhnps_81_22
  14. Yang X. Comparison of Supervised Learning and Unsupervised Learning in the Diagnosis of Medical Imaging Diseases. Appl Comput Eng. 2025;145:164–169. doi:10.54254/2755-2721/2025.22239
  15. Iqbal T, Elahi A, Wijns W, Shahzad A. Exploring unsupervised machine learning classification methods for physiological stress detection. Front Med Technol. 2022;4:782756. doi:10.3389/fmedt.2022.782756
  16. Datta S. Reinforcement learning in surgery. Surgery. 2021;170(1):329–332. doi:10.1016/j.surg.2020.11.040
  17. LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521(7553):436–444. doi:10.1038/nature14539
  18. Mienye ID, Swart TG, Obaido G, Jordan M, Ilono P. Deep Convolutional Neural Networks in Medical Image Analysis: A Review. Information. 2025;16(3):195. doi:10.3390/info16030195
  19. Liu L, Blake V, Barman M, Gallego B, Churches T, Kennedy G, et al. Using natural language processing to extract information from clinical text in electronic medical records for populating clinical registries: a systematic review. J Am Med Inform Assoc. 2026;33(2):484–499. doi:10.1093/jamia/ocaf176
  20. Carstens M, Vasisht S, Zhang Z, Barbur I, Kolbinger FR. Artificial intelligence for surgical scene understanding: a systematic review and reporting quality meta-analysis. NPJ Digit Med. 2026;9:59. doi:10.1038/s41746-025-02227-4
  21. Kasimieh O, Ahmed MM, Othman ZK, Ali I, Hassan MM, Maulion PM, et al. Deep learning in real-time image-guided surgery: a systematic review of applications, methodologies, and clinical relevance. Art Int Surg. 2025;5:557-571. doi:10.20517/ais.2025.92
  22. Gharios M, El-Hajj VG, Frisk H, Ohlsson M, Omar A, Edström E, et al. The use of hybrid operating rooms in neurosurgery, advantages, disadvantages, and future perspectives: a systematic review. Acta Neurochir (Wien). 2023;165(9):2343-2358. doi:10.1007/s00701-023-05756-7
  23. Knudsen JE, Ghaffar U, Ma R, Hung AJ. Clinical applications of artificial intelligence in robotic surgery. J Robot Surg. 2024;18:102. doi:10.1007/s11701-024-01867-0
  24. Ogut E. Artificial Intelligence in Clinical Medicine: Challenges Across Diagnostic Imaging, Clinical Decision Support, Surgery, Pathology, and Drug Discovery. Clin Pract. 2025;15(9):169. doi:10.3390/clinpract15090169
  25. van Smeden M, Heinze G, Van Calster B, Asselbergs FW, Vardas PE, Bruining N, et al. Critical appraisal of artificial intelligence-based prediction models for cardiovascular disease. Eur Heart J. 2022;43(31):2921–2930. doi:10.1093/eurheartj/ehac238
  26. Charilaou P, Battat R. Machine learning models and over-fitting considerations. World J Gastroenterol. 2022;28(5):605–607. doi:10.3748/wjg.v28.i5.605
  27. Pennestrì F, Cabitza F, Picerno N, Banfi G. Sharing reliable information worldwide: healthcare strategies based on artificial intelligence need external validation. Position paper. BMC Med Inform Decis Mak. 2025;25:56. doi:10.1186/s12911-025-02883-2
  28. Hulsen T. Explainable Artificial Intelligence (XAI): Concepts and Challenges in Healthcare. AI. 2023;4(3):652–666. doi:10.3390/ai4030034
  29. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115–118. doi:10.1038/nature21056
  30. Litjens G, Kooi T, Bejnordi BE, Setio AAA, Ciompi F, Ghafoorian M, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60–88. doi:10.1016/j.media.2017.07.005
  31. Campanella G, Hanna MG, Geneslaw L, Miraflor A, Werneck Krauss Silva V, Busam KJ, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301–1309. doi:10.1038/s41591-019-0508-1
  32. Ardila D, Kiraly AP, Bharadwaj S, Choi B, Reicher JJ, Peng L, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954–961. doi:10.1038/s41591-019-0447-x
  33. Panossian VS, Argandykov D, Arnold SC, Gebran A, Paranjape CN, Hwabejire JO, et al. Validation of Artificial Intelligence-Based POTTER Calculator in Emergency General Surgery Patients Undergoing Laparotomy: Prospective, Bi-Institutional Study. J Am Coll Surg. 2025;240(3):254-262. doi:10.1097/XCS.0000000000001234
  34. Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–76. doi:10.1097/SLA.0000000000002693
  35. Bihorac A, Ozrazgat-Baslanti T, Ebadi A, Motaei A, Madkour M, Pardalos PM, et al. MySurgeryRisk: Development and validation of a machine‑learning risk algorithm for major complications and death after surgery. Ann Surg. 2019;269(4):652–662. doi:10.1097/SLA.0000000000002706
  36. Lee M, Zakhari V, Shanmugaraj A, Sivasundaram L, Horner NS. Machine learning for predicting outcomes, complications and resource utilisation after hip arthroscopy: a systematic review. Knee Surg Sports Traumatol Arthrosc. 2026; doi:10.1002/ksa.70304
  37. Elhaddad M, Hamam S. AI-Driven Clinical Decision Support Systems: An Ongoing Pursuit of Potential. Cureus. 2024;16(4):e57728. doi:10.7759/cureus.57728
  38. Piazzolla P, Volpi G, Piana A, Checcucci E, Amparore D. Artificial intelligence guidance for 3D augmented reality robotic surgery: When the machine falls the human assistance is still alive. Urol Video J. 2025;25:100307. doi:10.1016/j.urolvj.2024.100307
  39. Shen D, Wu G, Suk HI. Deep Learning in Medical Image Analysis. Annu Rev Biomed Eng. 2017;19:221–248. doi:10.1146/annurev-bioeng-071516-044442
  40. Kersten-Oertel M, Jannin P, Collins DL. The state of the art of visualization in mixed reality image guided surgery. Comput Med Imaging Graph. 2013;37(2):98–112. doi:10.1016/j.compmedimag.2013.01.009
  41. Khor WS, Baker B, Amin K, Chan A, Patel K, Wong J. Augmented and virtual reality in surgery---the digital surgical environment: applications, limitations and legal pitfalls. Ann Transl Med. 2016;4(23):454. doi:10.21037/atm.2016.12.23
  42. Longo UG, De Salvatore S, Candela V, Zollo G. Augmented Reality, Virtual Reality and Artificial Intelligence in Orthopedic Surgery: A Systematic Review. Appl Sci (Basel). 2021;11(7):3253. doi:10.3390/app11073253
  43. Padoy N. Machine and deep learning for workflow recognition during surgery. Minim Invasive Ther Allied Technol. 2019;28(2):82–90. doi:10.1080/13645706.2019.1584116
  44. Twinanda AP, Shehata S, Mutter D, Marescaux J, de Mathelin M, Padoy N. EndoNet: A Deep Architecture for Recognition Tasks on Laparoscopic Videos. IEEE Trans Med Imaging. 2017;36(1):86–97. doi:10.1109/TMI.2016.2593957
  45. Mascagni P, Alapatt D, Sestini L, Altieri MS, Madani A, Watanabe Y, et al. Computer vision in surgery: from potential to clinical value. NPJ Digit Med. 2022;5(1):163. doi:10.1038/s41746-022-00707-5
  46. Wijnberge M, Geerts BF, Hol L, Lemmers N, Mulder MP, Berge P, et al. Effect of a Machine Learning-Derived Early Warning System for Intraoperative Hypotension vs Standard Care on Depth and Duration of Intraoperative Hypotension During Elective Noncardiac Surgery: The HYPE Randomized Clinical Trial. JAMA. 2020;323(11):1052-1060. doi:10.1001/jama.2020.0592
  47. Golany T, Aides A, Freedman D, Rabani N, Liu Y, Rivlin E, et al. Artificial intelligence for phase recognition in complex laparoscopic cholecystectomy. Surg Endosc. 2022;36(12):9215-9223. doi:10.1007/s00464-022-09405-5
  48. Shin Y. Artificial Intelligence-Powered Quality Assurance: Transforming Diagnostics, Surgery, and Patient Care—Innovations, Limitations, and Future Directions. Life (Basel). 2025;15(4):654. doi:10.3390/life15040654
  49. Xiao X, Wang X, Meng B, Pan X, Zhao H. Comparison of robotic AI-assisted and manual pedicle screw fixation for treating thoracolumbar fractures: a retrospective controlled trial. Front Bioeng Biotechnol. 2025;13:1491775. doi:10.3389/fbioe.2025.1491775
  50. Golas SB, Shibahara T, Agboola S, Otaki H, Sato J, Nakae T, et al. A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data. BMC Med Inform Decis Mak. 2018;18(1):44. doi:10.1186/s12911-018-0620-z
  51. Heard JR, Deo A, Ghaffar U, et al. AI model predicts patient outcomes from surgical gestures and provides insights into explainability. npj Digit Surg. 2026;1:4. doi:10.1038/s44484-025-00006-y
  52. Meyer A, Zverinski D, Pfahringer B, Kempfert J, Kuehne T, et al. Machine learning for real-time prediction of complications in critical care: a retrospective study. Lancet Respir Med. 2018;6(12):905–914. doi:10.1016/S2213-2600(18)30300-X
  53. Balian J, Cho NY, Vadlakonda A, Choinski K, Fazl Alizadeh M, Lyou Y, et al. Failure to rescue following emergency general surgery: A national analysis. Surg Open Sci. 2024;20:77–81. doi:10.1016/j.sopen.2024.05.013
  54. Kehlet H, Wilmore DW. Evidence-based surgical care and the evolution of fast-track surgery. Ann Surg. 2008;248(2):189–198. doi:10.1097/SLA.0b013e31817f2c1a
  55. Zain Z, Almadhoun MKI, Alsadoun L, Bokhari SF. Leveraging Artificial Intelligence and Machine Learning to Optimize Enhanced Recovery After Surgery (ERAS) Protocols. Cureus. 2024;16(3):e56668. doi:10.7759/cureus.56668
  56. Khan MM, Shah N. AI-driven wearable sensors for postoperative monitoring in surgical patients: A systematic review. Comput Biol Med. 2025;196(Pt B):110783. doi:10.1016/j.compbiomed.2025.110783
  57. Yanagida Y, Takenaka S, Kitaguchi D, Hamano S, Tanaka A, Mitarai H, et al. Surgical skill assessment using an AI-based surgical phase recognition model for laparoscopic cholecystectomy. Surg Endosc. 2025;39(8):5018–5026. doi:10.1007/s00464-025-11903-1
  58. Escobar-Castillejos D, Barrera-Animas AY, Noguez J, Cárdenas-Robledo LA, Rosas-Fernández JB. Transforming Surgical Training With AI Techniques for Training, Assessment, and Evaluation: Scoping Review. J Med Internet Res. 2025;27:e58966. doi:10.2196/58966
  59. Seymour NE, Gallagher AG, Roman SA, O'Brien MK, Bansal VK, Andersen DK, et al. Virtual reality training improves operating room performance: results of a randomized, double-blinded study. Ann Surg. 2002;236(4):458–464; discussion 463–464. doi:10.1097/00000658-200210000-00008
  60. Lakshmi AA, Vijayaraj A, Sruthi Shree CK, Kumar SR, Santhosh V, Tharun Kumar S. Artificial intelligence enhanced virtual reality surgery training. In: 2025 2nd International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication Engineering (RMKMATE); 2025; Chennai, India. p. 1–7. doi:10.1109/RMKMATE64874.2025.11042515
  61. Ericsson KA. Deliberate practice and acquisition of expert performance: a general overview. Acad Emerg Med. 2008;15(11):988–994. doi:10.1111/j.1553-2712.2008.00227.x
  62. Aggarwal R, Mytton OT, Derbrew M, Hananel D, Heydenburg M, Issenberg B, et al. Training and simulation for patient safety. Qual Saf Health Care. 2010;19(Suppl 2):i34–i43. doi:10.1136/qshc.2009.038562
  63. Yilmaz R, Alsayegh A, Bakhaidar M, Fazlollahi AM, Abou Hamdan N, Tee T, et al. Combining real-time AI and in-person expert instruction in simulated surgical skills training – Randomized crossover trial. npj Artif Intell. 2025;1:36. doi:10.1038/s44387-025-00032-8
  64. Reinke A, Li ZO, Tizabi MD, André P, Knopp M, Rother MM, et al. Current validation practice undermines surgical AI development. arXiv [Preprint]. 2025 Nov 6:arXiv:2511.03769. doi:10.48550/arXiv.2511.03769
  65. Lin JC, Jain B, Iyer JM, et al. Benefit-risk reporting for FDA-cleared artificial intelligence–enabled medical devices. JAMA Health Forum. 2025;6(9):e253351. doi:10.1001/jamahealthforum.2025.3351
  66. Lee B, Jay M, Fox H, Padley J, Dai T, Levin AS. FDA-cleared artificial intelligence medical devices in orthopaedic surgery. J Am Acad Orthop Surg Glob Res Rev. 2026;10(2):e25.00170. doi:10.5435/JAAOSGlobal-D-25-00170
  67. Eidlisz J, Ajmal E, Awan B. FDA-approved artificial intelligence and machine learning–enabled medical devices in general surgery: A review based on regulatory panel classification. Surgery. 2026;190:109846. doi:10.1016/j.surg.2025.109846
  68. Dhawan R, Shauly O, Shay D, Brooks K, Losken A. Growth in FDA-Approved Artificial Intelligence Devices in Plastic Surgery: A Key Look Into the Future. Aesthet Surg J. 2025;45(1):108-111. doi:10.1093/asj/sjae209
  69. Sivakumar R, Lue B, Kundu S. FDA approval of artificial intelligence and machine learning devices in radiology: a systematic review. JAMA Netw Open. 2025;8(11):e2542338. doi:10.1001/jamanetworkopen.2025.42338
  70. Clark P, Kim J, Aphinyanaphongs Y. Marketing and US Food and Drug Administration clearance of artificial intelligence and machine learning–enabled software in and as medical devices: a systematic review. JAMA Netw Open. 2023;6(7):e2321792. doi:10.1001/jamanetworkopen.2023.21792
  71. Windecker D, Baj G, Shiri I, Mohammadi Kazaj P, Kaesmacher J, Gräni C, et al. Generalizability of FDA-approved artificial intelligence–enabled medical devices for clinical use. JAMA Netw Open. 2025;8(4):e258052. doi:10.1001/jamanetworkopen.2025.8052
  72. Mohammed ZK. Explainable AI in Health Care: Trust and Transparency in AI-Powered Medical Diagnosis. In: Domínguez-Morales M, Luna-Perejón F, editors. The Latest Advances in the Field of Intelligent Systems. Rijeka: IntechOpen; 2025. doi:10.5772/intechopen.1011279
  73. Arjomandi Rad A, Vardanyan R, Athanasiou T, Maessen J, Sardari Nia P. The ethical considerations of integrating artificial intelligence into surgery: a review. Interdiscip CardioVasc Thorac Surg. 2025;40(3):ivae192. doi:10.1093/icvts/ivae192
  74. Barrios PA, Meredyth N, Morris R, Haines K, Kim GJ, Peterson CY. Ethical considerations in the deployment of artificial intelligence in surgery. J Surg Res. 2025;315:268-274. doi:10.1016/j.jss.2025.08.019
  75. Mittermaier M, Raza MM, Kvedar JC. Bias in AI-based models for medical applications: challenges and mitigation strategies. npj Digit Med. 2023;6:113. doi:10.1038/s41746-023-00858-z
  76. Johnson-Mann CN, Loftus TJ, Bihorac A. Equity and Artificial Intelligence in Surgical Care. JAMA Surg. 2021;156(6):509–510. doi:10.1001/jamasurg.2020.7208
  77. Ferreres AR. Ethical aspects of artificial intelligence in general surgical practice. Rev Col Bras Cir. 2024;51:e20243762EDIT01. doi:10.1590/0100-6991e-20243762EDIT01-en
  78. Pressman SM, Borna S, Gomez-Cabello CA, Haider SA, Haider C, Forte AJ. AI and Ethics: A Systematic Review of the Ethical Considerations of Large Language Model Use in Surgery Research. Healthcare. 2024;12(8):825. doi:10.3390/healthcare12080825
  79. Schmidgall S, Opfermann JD, Kim JW, Krieger A. Will your next surgeon be a robot? Autonomy and AI in robotic surgery. Sci Robot. 2025;10(104):eadt0187. doi:10.1126/scirobotics.adt0187
  80. Gumbs AA, Croner R, Abu-Hilal M, Bannone E, Ishizawa T, Spolverato G, et al. Surgomics and the Artificial intelligence, Radiomics, Genomics, Oncopathomics and Surgomics (AiRGOS) Project. Art Int Surg. 2023;3(3):180–185. doi:10.20517/ais.2023.24
  81. Gumbs AA, Diana M, Rawicz-Pruszyński K, Spolverato G, Abu-Hilal M, Frigerio I, et al. AIONS Consensus Conference on Definitions of Artificial Intelligence Surgery, Surgomics and Robotics. Art Int Surg. 2026;6:98–113. doi:10.20517/ais.2025.113
  82. Yelchuri H, Singh DK, Gnani NK, Prabhakar TV, Singh C. RoboTwin: A Robotic Teleoperation Framework Using Digital Twins. arXiv:2506.01027 [Preprint]. 2025 Jun 1 [cited 2026 Mar 22]. Available from: https://arxiv.org/abs/2506.01027. doi:10.48550/arXiv.2506.01027
  83. Kewalramani D, Loftus TJ, Mayol J, Narayan M. Artificial intelligence in surgery: a global balancing act. Br J Surg. 2024;111(3):znae062. doi:10.1093/bjs/znae062
  84. Kenig N, Monton Echeverria J, Muntaner Vives A. Artificial Intelligence in Surgery: A Systematic Review of Use and Validation. J Clin Med. 2024;13(23):7108. doi:10.3390/jcm13237108
  85. Ruiz NI, Cardona Salazar I, Naranjo Palacio L, et al. Accuracy and reliability of artificial intelligence in surgical decision-making: a literature review. Cureus. 2025;17(10):e95337. doi:10.7759/cureus.95337
  86. Arboit L, Schneider DN, Collins T, Hashimoto DA, Perretta S, Dallemagne B, et al. Surgeons Awareness, Expectations, and Involvement with Artificial Intelligence: a Survey Pre and Post the GPT Era. arXiv:2506.08258 [Preprint]. 2025 Jun 9 [cited 2026 Mar 22]. Available from: https://arxiv.org/abs/2506.08258. doi:10.48550/arXiv.2506.08258
  87. Brandenburg JM, Müller-Stich BP, Wagner M, van der Schaar M. Can surgeons trust AI? Perspectives on machine learning in surgery and the importance of eXplainable Artificial Intelligence (XAI). Langenbecks Arch Surg. 2025;410:53. doi:10.1007/s00423-025-03626-7

LICENSE

Creative Commons License
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.