Page 26 - Read Online

Page 51 Talwar et al. Art Int Surg. 2025;5:46-52 https://dx.doi.org/10.20517/ais.2024.81

Availability of data and materials
Not applicable.

Financial support and sponsorship
None.

Conflicts of interest
All authors declared that there are no conflicts of interest.

Ethical approval and consent to participate
Not applicable.

Consent for publication
Not applicable.

Copyright
© The Author(s) 2025.

REFERENCES
1. Huang K, Altosaar J, Ranganath R. ClinicalBERT: modeling clinical notes and predicting hospital readmission. arXiv.
2019;arXiv:1904.05342. Available from: http://arxiv.org/abs/1904.05342. [accessed 6 Jan 2025]
2. Van Veen D, Van Uden C, Blankemeier L, et al. Clinical text summarization: adapting large language models can outperform human
experts. Res Sq. 2023:rs.3.rs-3483777. DOI PubMed PMC
3. Epic and Microsoft bring GPT-4 to EHRs. Available from: https://www.epic.com/epic/post/epic-and-microsoft-bring-gpt-4-to-ehrs/.
[Last accessed on 6 Jan 2025].
4. Seu M, Cho BH, Pigott R, et al. Trends and perceptions of electronic health record usage among plastic surgeons. Plast Reconstr Surg
Glob Open. 2020;8:e2709. DOI PubMed PMC
5. Sinsky C, Colligan L, Li L, et al. Allocation of physician time in ambulatory practice: a time and motion study in 4 specialties. Ann
Intern Med. 2016;165:753-60. DOI PubMed
6. Arndt BG, Beasley JW, Watkinson MD, et al. Tethered to the EHR: primary care physician workload assessment using EHR event log
data and time-motion observations. Ann Fam Med. 2017;15:419-26. DOI PubMed PMC
7. Ambience. Available from: https://www.ambiencehealthcare.com/. [Last accessed on 6 Jan 2025].
8. Nabla Copilot. Enjoy care again. Available from: https://www.nabla.com/. [Last accessed on 6 Jan 2025].
9. Venkatesh KP, Raza MM, Kvedar JC. Automating the overburdened clinical coding system: challenges and next steps. NPJ Digit Med.
2023;6:16. DOI PubMed PMC
10. Kim BH, Ganapathi V. Read, attend, and code: pushing the limits of medical codes prediction from clinical notes by machines. arXiv.
2021;arXiv:2107.10650. Available from: https://doi.org/10.48550/arXiv.2107.10650. [accessed 6 Jan 2025]
11. Talwar AA, Niu EF, Broach RB, Nelson JA, Fischer JP. Patient-reported outcomes: a primer for plastic surgeons. J Plast Reconstr
Aesthet Surg. 2023;86:35-47. DOI PubMed
12. Abdi S, de Witte L, Hawley M. Exploring the potential of emerging technologies to meet the care and support needs of older people: a
Delphi survey. Geriatrics. 2021;6:19. DOI PubMed PMC
13. Nori H, King N, McKinney SM, Carignan D, Horvitz E. Capabilities of GPT-4 on medical challenge problems. arXiv.
2023;arXiv:2303.13375. Available from: https://doi.org/10.48550/arXiv.2303.13375. [accessed 6 Jan 2025]
14. Singhal K, Azizi S, Tu T, et al. Large language models encode clinical knowledge. arXiv. 2022;arXiv:2212.13138. Available from:
https://doi.org/10.48550/arXiv.2212.13138. [accessed 6 Jan 2025]
15. Gajjar AA, Kumar RP, Paliwoda ED, et al. Usefulness and accuracy of artificial intelligence chatbot responses to patient questions for
neurosurgical procedures. Neurosurgery. 2024. DOI PubMed
16. Karimov Z, Allahverdiyev I, Agayarov OY, Demir D, Almuradova E. ChatGPT vs UpToDate: comparative study of usefulness and
reliability of Chatbot in common clinical presentations of otorhinolaryngology-head and neck surgery. Eur Arch Otorhinolaryngol.
2024;281:2145-51. DOI PubMed PMC
17. Wu J, Liu X, Li M, et al. Clinical text datasets for medical artificial intelligence and large language models - a systematic review.
NEJM AI. 2024;1:AIra2400012. DOI
18. Shah SV. Accuracy, consistency, and hallucination of large language models when analyzing unstructured clinical notes in electronic
medical records. JAMA Network Open. 2024;7:e2425953. DOI PubMed
19. Beutel G, Geerits E, Kielstein JT. Artificial hallucination: GPT on LSD? Crit Care. 2023;27:148. DOI PubMed PMC