[1] |
|
[2] |
|
[3] |
|
[4] |
|
[5] |
MOOR M, BANERJEE O, ABAD Z S H, et al. Foundation models for generalist medical artificial intelligence [J]. Nature, 2023, 616 (7956) : 259-265. DOI: 10.1038/s41586-023-05881-4.
|
[6] |
PEREZ-LOPEZ R, GHAFFARI LALEH N, MAHMOOD F, et al. A guide to artificial intelligence for cancer researchers [J]. Nat Rev Cancer, 2024, 24 (6) : 427-441. DOI: 10.1038/s41568-024-00694-7.
|
[7] |
|
[8] |
LI Y H, LI Y L, WEI M Y, et al. Innovation and challenges of artificial intelligence technology in personalized healthcare [J]. Sci Rep, 2024, 14 (1) : 18994. DOI: 10.1038/s41598-024-70073-7.
|
[9] |
|
[10] |
LI C, WONG C, ZHANG S, et al. LLaVA-Med: training a large language-and-vision assistant for biomedicine in one day [J]. Advances in Neural Information Processing Systems, 2024, 36.
|
[11] |
ANDREW A. Potential applications and implications of large language models in primary care [J]. Fam Med Community Health, 2024, 12 (Suppl 1) : e002602. DOI: 10.1136/fmch-2023-002602.
|
[12] |
TIU E, TALIUS E, PATEL P, et al. Expert-level detection of pathologies from unannotated chest X-ray images via self-supervised learning [J]. Nat Biomed Eng, 2022, 6 (12) : 1399-1406. DOI: 10.1038/s41551-022-00936-9.
|
[13] |
ACOSTA J N, FALCONE G J, RAJPURKAR P, et al. Multimodal biomedical AI [J]. Nat Med, 2022, 28 (9) : 1773-1784. DOI: 10.1038/s41591-022-01981-2.
|
[14] |
KRISHNAN R, RAJPURKAR P, TOPOL E J. Self-supervised learning in medicine and healthcare [J]. Nat Biomed Eng, 2022, 6 (12) : 1346-1352. DOI: 10.1038/s41551-022-00914-1.
|
[15] |
BROWN T B. Language models are few-shot learners [EB/OL]. arXiv preprint, arXiv: 2005.14165v4 (2020-07-22) [2024-07-22].
|
[16] |
BOMMASANI R, HUDSON D A, ADELI E, et al. On the opportunities and risks of foundation models [EB/OL]. arXiv preprint, arXiv: 2108.07258v3 (2022-07-12) [2024-07-22].
|
[17] |
ALAYRAC J-B, DONAHUE J, LUC P, et al. Flamingo: a visual language model for few-shot learning [EB/OL]. arXiv preprint, arXiv: 2204.14198v2 (2022-11-15) [2024-07-22].
|
[18] |
张宗久, 焦雅辉, 高光明. 医疗服务的最后一公里 [M]. 北京: 清华大学出版社, 2022.
|
[19] |
TOPOL E. Deep medicine: how artificial intelligence can make healthcare human again[M]. New York: Basic Books, 2019.
|
[20] |
|
[21] |
MESKÓ B, GÖRÖG M. A short guide for medical professionals in the era of artificial intelligence [J]. NPJ Digit Med, 2020, 3: 126. DOI: 10.1038/s41746-020-00333-z.
|
[22] |
BONGURALA AR, SAVE D, VIRMANI A, et al. Transforming health care with artificial intelligence: redefining medical documentation [J]. Mayo Clinic Proceedings: Digital Health, 2024, 2 (3) : 342-347. DOI: 10.1016/j.mcpdig.2024.05.006.
|
[23] |
REDDY S, FOX J, PUROHIT M P. Artificial intelligence-enabled healthcare delivery [J]. J R Soc Med, 2019, 112 (1) : 22-28. DOI: 10.1177/0141076818815510.
|
[24] |
BOIKANYO K, ZUNGERU A M, SIGWENI B, et al. Remote patient monitoring systems: applications, architecture, and challenges [J]. Sci Afr, 2023, 20: e01638. DOI: 10.1016/j.sciaf.2023.e01638.
|
[25] |
SIDDIQ M. Use of machine learning to predict patient developing a disease or condition for early diagnose [J]. International Journal of Multidisciplinary Sciences and Arts, 2022, 1 (1) : 13-23. DOI: 10.47709/ijmdsa.v1i1.2271.
|
[26] |
ZAFEIROPOULOS N, MAVROGIORGOU A, KLEFTAKIS S, et al. Interpretable stroke risk prediction using machine learning algorithms [C] //Lecture Notes in Networks and Systems. Singapore: Springer Nature Singapore, 2023: 647-656.
|
[27] |
ZARETSKY J, KIM J M, BASKHAROUN S, et al. Generative artificial intelligence to transform inpatient discharge summaries to patient-friendly language and format [J]. JAMA Netw Open, 2024, 7 (3) : e240357. DOI: 10.1001/jamanetworkopen.2024.0357.
|
[28] |
VAN DE SANDE D, VAN GENDEREN M E, VERHOEF C, et al. Optimizing discharge after major surgery using an artificial intelligence-based decision support tool (DESIRE) : an external validation study [J]. Surgery, 2022, 172 (2) : 663-669. DOI: 10.1016/j.surg.2022.03.031.
|
[29] |
HARADA T, MIYAGAMI T, KUNITOMO K, et al. Clinical decision support systems for diagnosis in primary care: a scoping review [J]. Int J Environ Res Public Health, 2021, 18 (16) : 8435. DOI: 10.3390/ijerph18168435.
|
[30] |
ZHANG Y Y, DENG Z Q, XU X Y, et al. Application of artificial intelligence in drug-drug interactions prediction: a review [J]. J Chem Inf Model, 2024, 64 (7) : 2158-2173. DOI: 10.1021/acs.jcim.3c00582.
|
[31] |
FARID F, BELLO A, AHAMED F, et al. The roles of ai technologies in reducing hospital readmission for chronic diseases: a comprehensive analysis [EB/OL]. Preprints.org, Preprints: 2023071000 (2023-07-14) [2024-07-28].
|
[32] |
VAN BUCHEM M M, NEVE O M, KANT I M J, et al. Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM) [J]. BMC Med Inform Decis Mak, 2022, 22 (1) : 183. DOI: 10.1186/s12911-022-01923-5.
|
[33] |
SABARMATHI G, CHINNAIYAN R, MUTHULAKSHMI R. NLP-based health care-hospital recommendation systems with online text reviews by patients satisfaction [C] //2023 International Conference on New Frontiers in Communication, Automation, Management and Security (ICCAMS). October 27-28, 2023, Bangalore, India. IEEE, 2023: 1-4.
|
[34] |
JO E, EPSTEIN D A, JUNG H, et al. Understanding the benefits and challenges of deploying conversational ai leveraging large language models for public health intervention[Z]. Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems. ACM, 2023.
|
[35] |
HEBBAR S, VANDANA B. Artificial intelligence in future telepsychiatry and psychotherapy for E-mental health revolution [M] //Computational Intelligence in Medical Decision Making and Diagnosis. Boca Raton: CRC Press, 2023: 39-60.
|
[36] |
GUAL-MONTOLIO P, JAÉN I, MARTÍNEZ-BORBA V, et al. Using artificial intelligence to enhance ongoing psychological interventions for emotional problems in real-or close to real-time: a systematic review [J]. Int J Environ Res Public Health, 2022, 19 (13) : 7737. DOI: 10.3390/ijerph19137737.
|
[37] |
YASEEN I, RATHER R A. A theoretical exploration of artificial intelligence's impact on feto-maternal health from conception to delivery [J]. Int J Womens Health, 2024, 16: 903-915. DOI: 10.2147/IJWH.S454127.
|
[38] |
KHAN M, KHURSHID M, VATSA M, et al. On AI approaches for promoting maternal and neonatal health in low resource settings: a review [J]. Front Public Health, 2022, 10: 880034. DOI: 10.3389/fpubh.2022.880034.
|
[39] |
熊尚华, 陈颖, 黄玉清, 等. 儿童生长发育智慧管理平台设计与应用 [J]. 中国数字医学, 2024, 19 (5) : 101-105.
|
[40] |
|
[41] |
|
[42] |
PALANISWAMY T. Hyperparameter optimization based deep convolution neural network model for automated bone age assessment and classification [J]. Displays, 2022, 73: 102206. DOI: 10.1016/j.displa.2022.102206.
|
[43] |
ZHOU X L, WANG E G, LIN Q, et al. Diagnostic performance of convolutional neural network-based Tanner-Whitehouse 3 bone age assessment system [J]. Quant Imaging Med Surg, 2020, 10 (3) : 657-667. DOI: 10.21037/qims.2020.02.20.
|
[44] |
HAN A, ISAACSON A, MUENNIG P. The promise of big data for precision population health management in the US [J]. Public Health, 2020, 185: 110-116. DOI: 10.1016/j.puhe.2020.04.040.
|
[45] |
SPINEWINE A, EVRARD P, HUGHES C. Interventions to optimize medication use in nursing homes: a narrative review [J]. Eur Geriatr Med, 2021, 12 (3) : 551-567. DOI: 10.1007/s41999-021-00477-5.
|
[46] |
MENG X B, YAN X Y, ZHANG K, et al. The application of large language models in medicine: a scoping review [J]. iScience, 2024, 27 (5) : 109713. DOI: 10.1016/j.isci.2024.109713.
|
[47] |
|
[48] |
COSENTINO J, BELYAEVA A, LIU X, et al. Towards a Personal Health Large Language Model [EB/OL]. arXiv preprint, arXiv: 240606474 (2024-06-10) [2024-07-28].
|
[49] |
GINSBURG G S, PICARD R W, FRIEND S H. Key issues as wearable digital health technologies enter clinical care [J]. N Engl J Med, 2024, 390 (12) : 1118-1127. DOI: 10.1056/NEJMra2307160.
|
[50] |
|
[51] |
|
[52] |
王茹俊, 王丹. ChatGPT介入医学教育的伦理风险及应对策略 [J]. 医学与哲学, 2024, 45 (2) : 76-81.
|
[53] |
PRICE W N 2nd, GERKE S, COHEN I G. Potential liability for physicians using artificial intelligence [J]. JAMA, 2019, 322 (18) : 1765-1766. DOI: 10.1001/jama.2019.15064.
|
[54] |
WANG C Y, LIU S R, YANG H, et al. Ethical considerations of using ChatGPT in health care [J]. J Med Internet Res, 2023, 25: e48009. DOI: 10.2196/48009.
|
[55] |
GOODMAN R S, PATRINELY J R Jr, OSTERMAN T, et al. On the cusp: Considering the impact of artificial intelligence language models in healthcare [J]. Med, 2023, 4 (3) : 139-140. DOI: 10.1016/j.medj.2023.02.008.
|
[56] |
CHOWDHERY A, NARANG S, DEVLIN J, et al. Palm: Scaling language modeling with pathways [J]. Journal of Machine Learning Research, 2023, 24 (240) : 1-113.
|
[57] |
|
[58] |
|
[59] |
SUMMERTON N, CANSDALE M. Artificial intelligence and diagnosis in general practice [J]. Br J Gen Pract, 2019, 69 (684) : 324-325. DOI: 10.3399/bjgp19X704165.
|
[60] |
|
[61] |
YU K H, HEALEY E, LEONG T Y, et al. Medical artificial intelligence and human values [J]. N Engl J Med, 2024, 390 (20) : 1895-1904. DOI: 10.1056/NEJMra2214183.
|