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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
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.
19. Beutel G, Geerits E, Kielstein JT. Artificial hallucination: GPT on LSD? Crit Care. 2023;27:148.
20. Zaretsky J, Kim JM, Baskharoun S, et al. Generative artificial intelligence to transform inpatient discharge summaries to patient-friendly language and format. JAMA Netw Open. 2024;7:e240357.
21. Kwong JCC, Wang SCY, Nickel GC, Cacciamani GE, Kvedar JC. The long but necessary road to responsible use of large language models in healthcare research. NPJ Digit Med. 2024;7:177.
22. Haider SA, Pressman SM, Borna S, et al. Evaluating large language model (LLM) performance on established breast classification systems. Diagnostics. 2024;14:1491.
23. Schmidl B, Hütten T, Pigorsch S, et al. Assessing the use of the novel tool Claude 3 in comparison to ChatGPT 4.0 as an artificial intelligence tool in the diagnosis and therapy of primary head and neck cancer cases. Eur Arch Otorhinolaryngol. 2024;281:6099-109.
24. Kunze KN, Varady NH, Mazzucco M, et al. The large language model ChatGPT-4 exhibits excellent triage capabilities and diagnostic performance for patients presenting with various causes of knee pain. Arthroscopy. 2024;In Press.
