REFERENCES

1. Topol EJ. High-performance medicine: the convergence of human and artificial intelligence. Nat Med 2019;25:44-56.

2. Hernandez-Boussard T, Bozkurt S, Ioannidis JPA, Shah NH. MINIMAR (MINimum Information for Medical AI Reporting): developing reporting standards for artificial intelligence in health care. J Am Med Inform Assoc 2020;27:2011-5.

3. Schiff GD, Bates DW. Can electronic clinical documentation help prevent diagnostic errors? N Engl J Med 2010;362:1066-9.

4. Mak ML, Al-Shaqsi SZ, Phillips J. Prevalence of machine learning in craniofacial surgery. J Craniofac Surg 2020;31:898-903.

5. Jarvis T, Thornburg D, Rebecca AM, Teven CM. Artificial intelligence in plastic surgery: current applications, future directions, and ethical implications. Plast Reconstr Surg Glob Open 2020;8:e3200.

6. Kanevsky J, Corban J, Gaster R, Kanevsky A, Lin S, Gilardino M. Big data and machine learning in plastic surgery: a new frontier in surgical innovation. Plast Reconstr Surg 2016;137:890e-7e.

7. Zhu VZ, Tuggle CT, Au AF. Promise and limitations of big data research in plastic surgery. Ann Plast Surg 2016;76:453-8.

8. Kim YJ, Kelley BP, Nasser JS, Chung KC. Implementing precision medicine and artificial intelligence in plastic surgery: concepts and future prospects. Plast Reconstr Surg Glob Open 2019;7:e2113.

9. Murphy DC, Saleh DB. Artificial intelligence in plastic surgery: what is it? Where are we now? What is on the horizon? Ann R Coll Surg Engl 2020;102:577-80.

10. Dhillon H, Chaudhari PK, Dhingra K, et al. Current applications of artificial intelligence in cleft care: a scoping review. Front Med 2021;8:676490.

11. Wu J, Tse R, Shapiro LG. Learning to rank the severity of unrepaired cleft lip nasal deformity on 3D mesh data. Proc IAPR Int Conf Pattern Recogn 2014;2014:460-4.

12. Maier A, Hönig F, Bocklet T, et al. Automatic detection of articulation disorders in children with cleft lip and palate. J Acoust Soc Am 2009;126:2589-602.

13. Bouletreau P, Makaremi M, Ibrahim B, Louvrier A, Sigaux N. Artificial intelligence: applications in orthognathic surgery. J Stomatol Oral Maxillofac Surg 2019;120:347-54.

14. Patcas R, Bernini DAJ, Volokitin A, Agustsson E, Rothe R, Timofte R. Applying artificial intelligence to assess the impact of orthognathic treatment on facial attractiveness and estimated age. Int J Oral Maxillofac Surg 2019;48:77-83.

15. Du W, Bi W, Liu Y, Zhu Z, Tai Y, Luo E. Machine learning-based decision support system for orthognathic diagnosis and treatment planning. BMC Oral Health 2024;24:286.

16. Qamar A, Bangi SF, Barve R. Artificial intelligence applications in diagnosing and managing non-syndromic craniosynostosis: a comprehensive review. Cureus 2023;15:e45318.

17. Mashouri P, Skreta M, Phillips J, et al.

18. Pham TD, Holmes SB, Coulthard P. A review on artificial intelligence for the diagnosis of fractures in facial trauma imaging. Front Artif Intell 2023;6:1278529.

19. Wang HC, Wang SC, Yan JL, Ko LW. Artificial intelligence model trained with sparse data to detect facial and cranial bone fractures from head CT. J Digit Imaging 2023;36:1408-18.

20. Moon G, Lee D, Kim WJ, Kim Y, Sung KY, Choi HS. Very fast, high-resolution aggregation 3D detection CAM to quickly and accurately find facial fracture areas. Comput Methods Programs Biomed 2024;256:108379.

21. Moon G, Kim S, Kim W, Kim Y, Jeong Y, Choi H. Computer aided facial bone fracture diagnosis (CA-FBFD) system based on object detection model. IEEE Access 2022;10:79061-70.

22. Wang X, Xu Z, Tong Y, et al. Detection and classification of mandibular fracture on CT scan using deep convolutional neural network. Clin Oral Investig 2022;26:4593-601.

23. Roy M, Corkum JP, Shah PS, et al. Effectiveness and safety of the use of gracilis muscle for dynamic smile restoration in facial paralysis: a systematic review and meta-analysis. J Plast Reconstr Aesthet Surg 2019;72:1254-64.

24. Boonipat T, Asaad M, Lin J, Glass GE, Mardini S, Stotland M. Using artificial intelligence to measure facial expression following facial reanimation surgery. Plast Reconstr Surg 2020;146:1147-50.

25. Dusseldorp JR, Guarin DL, van Veen MM, Miller M, Jowett N, Hadlock TA. Automated spontaneity assessment after smile reanimation: a machine learning approach. Plast Reconstr Surg 2022;149:1393-402.

26. Sendak MP, Gao M, Brajer N, Balu S. Presenting machine learning model information to clinical end users with model facts labels. NPJ Digit Med 2020;3:41.

27. Castelvecchi D. Can we open the black box of AI? Nature 2016;538:20-3.

28. Knight W. The dark secret at the heart of AI. Available from: https://www.technologyreview.com/2017/04/11/5113/the-dark-secret-at-the-heart-of-ai/. [Last accessed on 5 Dec 2024].

29. Buolamwini J, Gebru T. Gender shades: intersectional accuracy disparities in commercial gender classification. In: Proceedings of the 1st Conference on Fairness, Accountability and Transparency. PMLR; 2018. pp. 77-91. Available from: https://proceedings.mlr.press/v81/buolamwini18a.html?mod=article_inline&ref=akusion-ci-shi-dai-bizinesumedeia. [Last accessed on 5 Dec 2024]

30. Kish LJ, Topol EJ. Unpatients-why patients should own their medical data. Nat Biotechnol 2015;33:921-4.

31. Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics 2021;22:122.

32. Brundage M, Avin S, Clark J, et al. The malicious use of artificial intelligence: forecasting, prevention, and mitigation. ArXiv. [Preprint.] Dec 1, 2024 [accessed 2024 Dec 5]. Available from: https://doi.org/10.48550/arXiv.1802.07228.

33. Watson J, Hutyra CA, Clancy SM, et al. Overcoming barriers to the adoption and implementation of predictive modeling and machine learning in clinical care: what can we learn from US academic medical centers? JAMIA Open 2020;3:167-72.

34. Salehinejad H, Kitamura J, Ditkofsky N, et al. A real-world demonstration of machine learning generalizability in the detection of intracranial hemorrhage on head computerized tomography. Sci Rep 2021;11:17051.

35. Mechelli A, Vieira S. From models to tools: clinical translation of machine learning studies in psychosis. NPJ Schizophr 2020;6:4.

36. Patel R, Oduola S, Callard F, et al. What proportion of patients with psychosis is willing to take part in research? A mental health electronic case register analysis. BMJ Open 2017;7:e013113.

37. Collaborative learning without sharing data. Nat Mach Intell 2021;3:459.

38. Antoniadi AM, Du Y, Guendouz Y, et al. Current challenges and future opportunities for XAI in machine learning-based clinical decision support systems: a systematic review. Appl Sci 2021;11:5088.

39. Bussone A, Stumpf S, O’Sullivan D. The role of explanations on trust and reliance in clinical decision support systems. In: 2015 International Conference on Healthcare Informatics; 2015 Oct 21-23; Dallas, USA. IEEE; 2015. pp. 160-9.

40. Amann J, Blasimme A, Vayena E, Frey D, Madai VI; Precise4Q consortium. Explainability for artificial intelligence in healthcare: a multidisciplinary perspective. BMC Med Inform Decis Mak 2020;20:310.

41. Barredo Arrieta A, Díaz-Rodríguez N, Del Ser J, et al. Explainable artificial intelligence (XAI): concepts, taxonomies, opportunities and challenges toward responsible AI. Inform Fusion 2020;58:82-115.

42. Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: a review of machine learning interpretability methods. Entropy 2020;23:18.

43. Accuracy. Artificial Intelligence Episode 1 - overview of leading artificial intelligence clusters around the globe. Available from: https://www.accuracy.com/perspectives/overview-leading-artificial-intelligence-clusters-around-globe. [Last accessed on 5 Dec 2024].

44. McKinsey and Company. Transforming healthcare with AI: the impact on the workforce and organizations. Available from: https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/transforming-healthcare-with-ai. [Last accessed on 5 Dec 2024].

Artificial Intelligence Surgery
ISSN 2771-0408 (Online)
Follow Us

Portico

All published articles will be preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles will be preserved here permanently:

https://www.portico.org/publishers/oae/