1. Mehta V. Artificial intelligence in medicine: revolutionizing healthcare
for improved patient outcomes. J Med Res Innov. 2023; 7(2):e000292. DOI:
10.32892/jmri.292.
2. Manfred D. Artificial intelligence (AI): what are the impacts for
medicine? J Artif Intell Cloud Comput. 2019; 2(2):1–3. DOI:
10.47363/JAICC/2023(2)117.
3. Paudyal R, Shah AD, Akin O, Do RKG, Konar AS, Hatzoglou V, et al. Artificial intelligence in CT and MR imaging for oncological
applications. Cancers. 2023; 15(9):2573. DOI:
10.3390/cancers15092573. PMID:
37174039. PMCID:
PMC10177423.
4. Derevianko A, Pizzoli SFM, Pesapane F, Rotili A, Monzani D, Grasso R, et al. The use of artificial intelligence (AI) in the radiology field:
what is the state of doctor–patient communication in cancer
diagnosis? Cancers. 2023; 15(2):470. DOI:
10.3390/cancers15020470. PMID:
36672417. PMCID:
PMC9856827.
6. Harry A. The future of medicine: harnessing the power of AI for
revolutionizing healthcare. Int J Multidiscip Sci Arts. 2023; 2(1):36–47. DOI:
10.47709/ijmdsa.v2i1.2395.
7. Demetriou DD, Hull R, Kgoebane-Maseko M, Lockhat Z, Dlamini Z. AI-enhanced digital pathology and radiogenomics in precision
oncology. In. Dlamini Z, editor. editor. Artificial intelligence and precision oncology: bridging cancer research
and clinical decision support. Cham:: 1Springer;2023. p. p. 93–113. DOI:
10.1007/978-3-031-21506-3_5.
8. Zeineldin RA, Junger D, Mathis-Ullrich F, Burgert O. Development of an AI-driven system for neurosurgery with a
usability study: a step towards minimal invasive robotics. at - Automatisierungstechnik. 2023; 71(7):537–546. DOI:
10.1515/auto-2023-0061.
10. Voskens FJ, Abbing JR, Ruys AT, Ruurda JP, Broeders IAMJ. A nationwide survey on the perceptions of general surgeons on
artificial intelligence. Artif Intell Surg. 2022; 2(1):8–17. DOI:
10.20517/ais.2021.10.
11. Lång K, Josefsson V, Larsson AM, Larsson S, Högberg C, Sartor H, et al. Artificial intelligence-supported screen reading versus standard
double reading in the Mammography Screening with Artificial Intelligence
trial (MASAI): a clinical safety analysis of a randomised, controlled,
non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 2023; 24(8):936–944. DOI:
10.1016/S1470-2045(23)00298-X. PMID:
37541274.
12. Nam JG, Hwang EJ, Kim J, Park N, Lee EH, Kim HJ, et al. AI improves nodule detection on chest radiographs in a health
screening population: a randomized controlled trial. Radiology. 2023; 307(2):e221894. DOI:
10.1148/radiol.221894. PMID:
36749213.
13. Sachpekidis C, Enqvist O, Ulén J, Kopp-Schneider A, Pan L, Jauch A, et al. Application of an artificial intelligence-based tool in [18F]FDG
PET/CT for the assessment of bone marrow involvement in multiple
myeloma. Eur J Nucl Med Mol Imaging. 2023; 50(12):3697–3708. DOI:
10.1007/s00259-023-06339-5. PMID:
37493665. PMCID:
PMC10547616.
14. Clift AK, Dodwell D, Lord S, Petrou S, Brady M, Collins GS, et al. Development and internal-external validation of statistical and
machine learning models for breast cancer prognostication: cohort
study. BMJ. 2023; 381:e073800. DOI:
10.1136/bmj-2022-073800. PMID:
37164379. PMCID:
PMC10170264.
15. Alaimo L, Lima HA, Moazzam Z, Endo Y, Yang J, Ruzzenente A, et al. Development and validation of a machine-learning model to predict
early recurrence of intrahepatic xholangiocarcinoma. Ann Surg Oncol. 2023; 30(9):5406–5415. DOI:
10.1245/s10434-023-13636-8. PMID:
37210452.
16. Liu M, Wu J, Wang N, Zhang X, Bai Y, Guo J, et al. The value of artificial intelligence in the diagnosis of lung
cancer: a systematic review and meta-analysis. PLOS ONE. 2023; 18(3):e0273445. DOI:
10.1371/journal.pone.0273445. PMID:
36952523. PMCID:
PMC10035910.
17. Subhan S, Malik J, Haq A, Qadeer MS, Zaidi SMJ, Orooj F, et al. Role of artificial intelligence and machine learning in
interventional cardiology. Curr Probl Cardiol. 2023; 48(7):101698. DOI:
10.1016/j.cpcardiol.2023.101698. PMID:
36921654.
19. Ishii M, Kaikita K, Yasuda S, Akao M, Ako J, Matoba T, et al. Risk prediction score for clinical outcome in atrial fibrillation
and stable coronary artery disease. Open Heart. 2023; 10(1):e002292. DOI:
10.1136/openhrt-2023-002292. PMID:
37173099. PMCID:
PMC10186465.
20. Yankam Njiwa J, Gray KR, Costes N, Mauguiere F, Ryvlin P, Hammers A. Advanced [
18F]FDG and [
11C]flumazenil PET
analysis for individual outcome prediction after temporal lobe epilepsy
surgery for hippocampal sclerosis. Neuroimage Clin. 2015; 7:122–131. DOI:
10.1016/j.nicl.2014.11.013. PMID:
25610774. PMCID:
PMC4299974.
21. Ma C, Wang L, Song D, Gao C, Jing L, Lu Y, et al. Multimodal-based machine learning strategy for accurate and
non-invasive prediction of intramedullary glioma grade and mutation status
of molecular markers: a retrospective study. BMC Med. 2023; 21(1):198. DOI:
10.1186/s12916-023-02898-4. PMID:
37248527. PMCID:
PMC10228074.
22. Liu Z, Zhang C, Ge S. Efficacy and safety of robotic-assisted versus median sternotomy
for cardiac surgery: results from a university affiliated
hospital. J Thorac Dis. 2023; 15(4):1861–1871. DOI:
10.21037/jtd-23-197. PMID:
37197544. PMCID:
PMC10183528.
23. Fujita T, Kakuta T, Kawamoto N, Shimahara Y, Yajima S, Tadokoro N, et al. Benefits of robotically-assisted surgery for complex mitral valve
repair. Interact Cardiovasc Thorac Surg. 2021; 32(3):417–425. DOI:
10.1093/icvts/ivaa271. PMID:
33221856. PMCID:
PMC8906674.
24. Palmieri V, Montisci A, Vietri MT, Colombo PC, Sala S, Maiello C, et al. Artificial intelligence, big data and heart transplantation:
actualities. Int J Med Inform. 2023; 176:105110. DOI:
10.1016/j.ijmedinf.2023.105110. PMID:
37285695.
25. Houserman DJ, Berend KR, Lombardi AV Jr, Fischetti CE, Duhaime EP, Jain A, et al. The viability of an artificial intelligence/machine learning
prediction model to determine candidates for knee
arthroplasty. J Arthroplasty. 2023; 38(10):2075–2080. DOI:
10.1016/j.arth.2022.04.003. PMID:
35398523.
26. Jang SJ, Kunze KN, Bornes TD, Anderson CG, Mayman DJ, Jerabek SA, et al. Leg-length discrepancy variability on standard anteroposterior
pelvis radiographs: an analysis using deep learning
measurements. J Arthroplasty. 2023; 38(10):2017–2023. E3. DOI:
10.1016/j.arth.2023.03.006. PMID:
36898486.
27. Endo Y, Tokuyasu T, Mori Y, Asai K, Umezawa A, Kawamura M, et al. Impact of AI system on recognition for anatomical landmarks
related to reducing bile duct injury during laparoscopic
cholecystectomy. Surg Endosc. 2023; 37(7):5752–5759. DOI:
10.1007/s00464-023-10224-5. PMID:
37365396. PMCID:
PMC10322759.
28. Zhang R, Chen J, Wang Z, Yang Z, Ren Y, Shi P, et al. A step towards conditional autonomy - robotic
appendectomy. IEEE Robot Autom Lett. 2023; 8(5):2429–2436. DOI:
10.1109/LRA.2023.3254859.
29. Moheb M, Gebran A, Maurer LR, Naar L, El Hechi M, Breen K, et al. Artificial intelligence versus surgeon gestalt in predicting risk
of emergency general surgery. J Trauma Acute Care Surg. 2023; 95(4):565–572. DOI:
10.1097/TA.0000000000004030. PMID:
37314698.
30. Tomé F, Michelin L, Lins RS, Bringmann DR, Corso LL. Using artificial neural networks for pattern recognition of
post-surgical infections. Braz J Health Rev. 2023; 6(1):3329–3339. DOI:
10.34119/bjhrv6n1-260.
31. Kokkinakis S, Kritsotakis EI, Lasithiotakis K. Artificial intelligence in surgical risk
prediction. J Clin Med. 2023; 12(12):4016. DOI:
10.3390/jcm12124016. PMID:
37373709. PMCID:
PMC10299093.
32. Watanabe A, Wiseman SM. A new era in surgical research: the evolving role of artificial
intelligence. Am J Surg. 2023; 226(6):923–925. DOI:
10.1016/j.amjsurg.2023.06.040. PMID:
37419728.
33. Rimmer L, Howard C, Picca L, Bashir M. The automaton as a surgeon: the future of artificial intelligence
in emergency and general surgery. Eur J Trauma Emerg Surg. 2021; 47(3):757–762. DOI:
10.1007/s00068-020-01444-8. PMID:
32715331.
34. Zhou XY, Guo Y, Shen M, Yang GZ. Application of artificial intelligence in surgery. Front Med. 2020; 14(4):417–430. DOI:
10.1007/s11684-020-0770-0. PMID:
32705406.
35. Mangano A, Valle V, Dreifuss NH, Aguiluz G, Masrur MA. Role of artificial intelligence (AI) in surgery: introduction,
general principles, and potential applications. Surg Technol Int. 2020; 38:17–21. DOI:
10.52198/21.STI.38.SO1369. PMID:
33370842.
36. McCartney J. AI is poised to “revolutionize” surgery
[Internet]. Chicago (IL): American College of Surgeons;c2023. [cited 2024 Jan 10]. Available from. https://www.facs.org/for-medical-professionals/news-publications/news-and-articles/bulletin/2023/june-2023-volume-108-issue-6/ai-is-poised-to-revolutionize-surgery/.
38. Bar O, Neimark D, Zohar M, Hager GD, Girshick R, Fried GM, et al. Impact of data on generalization of AI for surgical intelligence
applications. Sci Rep. 2020; 10(1):22208. DOI:
10.1038/s41598-020-79173-6. PMID:
33335191. PMCID:
PMC7747564.