AbstractAs the burgeoning field of Artificial Intelligence (AI) continues to permeate the fabric of healthcare, particularly in the realms of patient surveillance and telemedicine, a transformative era beckons. This manuscript endeavors to unravel the intricacies of recent AI advancements and their profound implications for reconceptualizing the delivery of medical care.

摘要随着新兴的人工智能（AI）领域继续渗透到医疗保健领域，特别是在患者监测和远程医疗领域，一个变革的时代正在召唤。这份手稿试图揭示最近人工智能进步的复杂性及其对重新概念化医疗服务的深远影响。

Through the introduction of innovative instruments such as virtual assistant chatbots, wearable monitoring devices, predictive analytic models, personalized treatment regimens, and automated appointment systems, AI is not only amplifying the quality of care but also empowering patients and fostering a more interactive dynamic between the patient and the healthcare provider.

通过引入虚拟助理聊天机器人、可穿戴监测设备、预测分析模型、个性化治疗方案和自动预约系统等创新工具，人工智能不仅提高了护理质量，而且赋予了患者权力，并促进了患者与医疗保健提供者之间更具互动性的动态。

Yet, this progressive infiltration of AI into the healthcare sphere grapples with a plethora of challenges hitherto unseen. The exigent issues of data security and privacy, the specter of algorithmic bias, the requisite adaptability of regulatory frameworks, and the matter of patient acceptance and trust in AI solutions demand immediate and thoughtful resolution .The importance of establishing stringent and far-reaching policies, ensuring technological impartiality, and cultivating patient confidence is paramount to ensure that AI-driven enhancements in healthcare service provision remain both ethically sound and efficient.

然而，人工智能逐渐渗透到医疗保健领域，面临着迄今为止前所未有的众多挑战。数据安全和隐私的紧迫问题，算法偏见的幽灵，监管框架的必要适应性，以及患者对人工智能解决方案的接受和信任问题，都需要立即和深思熟虑的解决方案。。

In conclusion, we advocate for an expansion of research efforts aimed at navigating the ethical complexities inherent to a technology-evolving landscape, catalyzing policy innovation, and devising AI applications that are not only clinically effective but also earn the trust of the patient populace.

总之，我们主张扩大研究工作，旨在解决技术发展格局固有的道德复杂性，促进政策创新，并设计出不仅具有临床有效性而且赢得患者群众信任的AI应用程序。

By melding expertise across disciplines, we stand at the threshold of an era wherein AI's role in healthcare is both.

通过融合跨学科的专业知识，我们站在了人工智能在医疗保健中的作用兼而有之的时代的门槛上。

IntroductionArtificial Intelligence (AI), a burgeoning domain within computer science, is increasingly being harnessed to execute tasks that demand human-like intelligence, such as solving complex problems, logical reasoning, and conducting learning analysis based on voluminous data sets. In the realm of healthcare, AI's significance cannot be overstated, particularly in areas like patient monitoring and telemedicine where it is driving transformative breakthroughs1.

引言人工智能（AI）是计算机科学中的一个新兴领域，越来越多地被用来执行需要类似人类智能的任务，例如解决复杂问题，逻辑推理以及基于大量数据集进行学习分析。在医疗保健领域，人工智能的重要性无论怎样强调都不为过，特别是在患者监测和远程医疗等领域，人工智能正在推动变革性突破1。

One of the most dynamic frontiers within AI in healthcare is the swift evolution of Natural Language Processing (NLP) algorithms. These sophisticated tools are capable of deciphering and comprehending human language, a skill that has profound implications for patient care. When applied to analyze symptoms narrated by patients, NLP can facilitate more natural and effective communication, thereby enhancing patient engagement and elevating the overall telemedicine experience2.

人工智能在医疗保健领域最具活力的前沿之一是自然语言处理（NLP）算法的快速发展。这些复杂的工具能够破译和理解人类语言，这项技能对患者护理具有深远的影响。当应用于分析患者讲述的症状时，NLP可以促进更自然和有效的沟通，从而增强患者的参与度并提高整体远程医疗体验2。

Another significant milestone is the application of computer vision algorithms for interpreting medical imaging, such as CT scans and MRIs. By leveraging AI to diagnose and categorize diseases from these images, healthcare providers can make more precise and expedited diagnoses3. The strides made in machine learning are also noteworthy, with AI algorithms being trained on vast repositories of data to identify patterns and make predictions.

另一个重要的里程碑是应用计算机视觉算法来解释医学成像，如CT扫描和MRI。通过利用AI从这些图像中诊断和分类疾病，医疗保健提供者可以做出更精确和快速的诊断3。机器学习取得的进步也值得注意，人工智能算法正在海量数据库上进行训练，以识别模式并做出预测。

This capability can be harnessed to analyze a wealth of patient data, including vital signs and test results, to anticipate health complications and tailor personalized care plans4. Furthermore, the rise of AI-driven virtual assistants in telemedicine is redefining patient-provider interactions, offering patients convenient access to healthcare information and resources, along with the ability to commu.

可以利用这种能力来分析大量患者数据，包括生命体征和测试结果，以预测健康并发症并定制个性化护理计划4。此外，人工智能驱动的远程医疗虚拟助理的兴起正在重新定义患者与提供者的互动，为患者提供方便的医疗信息和资源访问，以及交流能力。

Data availability

数据可用性

Data generated during the current study are available from the corresponding author upon reasonable request.

本研究期间产生的数据可根据合理要求从通讯作者处获得。

ReferencesEsteva, A., Kuprel, B. & Novoa, R. A. Dermatologist-level classification of skin cancer with deep neural networks. Oncologie 19(11–12), 407–408 (2017).

参考文献Esteva，A.，Kuprel，B。＆Novoa，R.A。皮肤科医生用深度神经网络对皮肤癌进行分类。肿瘤学19（11-12），407-408（2017）。

Google Scholar

谷歌学者

Wiriyathammabhum, P. et al. Computer vision and natural language processing: Recent approaches in multimedia and robotics. ACM Comput. Surveys 49(4), 1–44 (2017).Article

Wiriyathammabhum，P。等。计算机视觉和自然语言处理：多媒体和机器人的最新方法。ACM计算机。调查49（4），1-44（2017）。文章

Google Scholar

谷歌学者

Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. Jama-J. Am. Med. Assoc. 316(22), 2402–2410 (2016).Article

Gulshan，V。等人。在视网膜眼底照片中检测糖尿病视网膜病变的深度学习算法的开发和验证。《美国医学会杂志》316（22），2402–2410（2016）。文章

Google Scholar

谷歌学者

Susymary, J. & Deepalakshmi, P. Machine learning for precision medicine forecasts and challenges when incorporating non omics and omics data. Intell. Decis. Technol.-Netherlands 15(1), 69–85 (2021).

Susymary，J。＆Deepalakshmi，P。机器学习在整合非组学和组学数据时对精准医学的预测和挑战。因特尔。十分之一。技术-荷兰15（1），69–85（2021）。

Google Scholar

谷歌学者

Aggarwal, A. et al. Artificial intelligence-based chatbots for promoting health behavioral changes: Systematic review. J. Med. Internet Res. 25, e40789 (2023).Article

Aggarwal，A.等人。基于人工智能的聊天机器人，用于促进健康行为改变：系统综述。J、 医学互联网第25号决议，e40789（2023）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Kurniawan, M. H. et al. A systematic review of artificial intelligence-powered (AI-powered) chatbot intervention for managing chronic illness. Ann. Med. https://doi.org/10.1080/07853890.2024.2302980 (2024).Article

Kurniawan，M.H.等人，《人工智能驱动（AI驱动）聊天机器人干预治疗慢性病的系统评价》。安。医学。https://doi.org/10.1080/07853890.2024.2302980（2024年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Abd Rahman, R. et al. Application of machine learning methods in mental health detection: A systematic review. IEEE Access 8, 183952–183964 (2020).Article

Abd Rahman，R.等人。机器学习方法在心理健康检测中的应用：系统综述。IEEE Access 8183952–183964（2020）。文章

Google Scholar

谷歌学者

Calvaresi, D. et al. Exploring agent-based chatbots: A systematic literature review. J. Ambient Intell. Hum. Comput. 14(8), 11207–11226 (2023).Article

Calvaresi，D.等人，《探索基于代理的聊天机器人：系统文献综述》。J、 环境内部。哼。电脑。14（8），11207–11226（2023）。文章

Google Scholar

谷歌学者

Bertagnolli, M. M. et al. The electronic health record as a clinical trials tool: Opportunities and challenges. Clin. Trials 17(3), 237–242 (2020).Article

Bertagnolli，M.M.等人，《作为临床试验工具的电子健康记录：机遇与挑战》。临床。试验17（3），237-242（2020）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Kiourtis, A., et al. Improving Health Information Exchange through Wireless Communication Protocols. In: 16th International Conference on Wireless and Mobile Computing, Networking and Communications (IEEE WiMob). Electr Network. (2020).Nickel, B. et al. Words do matter: A systematic review on how different terminology for the same condition influences management preferences.

Kiourtis，A。等人。通过无线通信协议改进健康信息交换。发表于：第16届无线和移动计算、网络和通信国际会议（IEEE WiMob）。Electr Network（2020年）。Nickel，B.等人，《词语确实很重要：关于相同条件下不同术语如何影响管理偏好的系统综述》。

BMJ Open 7(7), e014129 (2017).Article .

BMJ公开赛7（7），e014129（2017）。第条。

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

van Panhuis, W. G. et al. A systematic review of barriers to data sharing in public health. BMC Public Health https://doi.org/10.1186/1471-2458-14-1144 (2014).Article

van Panhuis，W.G.等人，《公共卫生数据共享障碍的系统综述》。BMC公共卫生https://doi.org/10.1186/1471-2458-14-1144（2014年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

He, T. & Lee, C. Evolving flexible sensors, wearable and implantable technologies towards BodyNET for advanced healthcare and reinforced life quality. IEEE Open J. Circuits Syst. 2, 702–720 (2021).Article

He，T.＆Lee，C.将柔性传感器，可穿戴和植入式技术发展为BodyNET，以实现先进的医疗保健和提高生活质量。IEEE Open J.Circuits系统。2702-720（2021）。文章

Google Scholar

谷歌学者

Nguyen, T. N. et al. Guest editorial innovations in wearable, implantable, mobile, & remote healthcare with IoT & sensor informatics and patient monitoring. IEEE J. Biomed. Health Inform. 27(5), 2152–2154 (2023).Article

Nguyen，T.N.等人，《利用物联网和传感器信息学以及患者监测在可穿戴、植入式、移动和远程医疗保健方面的客座编辑创新》。IEEE J.Biomed。健康信息。。文章

Google Scholar

谷歌学者

Hrabovska, N., Kajati, E. & Zolotova, I. A validation study to confirm the accuracy of wearable devices based on health data analysis. Electronics 12(11), 2536 (2023).Article

Hrabovska，N.，Kajati，E。＆Zolotova，I。一项基于健康数据分析确认可穿戴设备准确性的验证研究。电子12（11），2536（2023）。文章

Google Scholar

谷歌学者

Wang, T.-L. et al. Assessment of heart rate monitoring during exercise with smart wristbands and a heart rhythm patch: Validation and comparison study. Jmir Form. Res. 7, e52519 (2023).Article

Wang，T.-L.等人。使用智能腕带和心律贴片评估运动期间的心率监测：验证和比较研究。Jmir表格第7号决议，e52519（2023）。文章

ADS

广告

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Mukkamala, R. et al. Evaluation of the accuracy of cuffless blood pressure measurement devices: Challenges and proposals. Hypertension 78(5), 1161–1167 (2021).Article

Mukkamala，R.等人。无袖带血压测量装置准确性的评估：挑战和建议。高血压78（5），1161-1167（2021）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Zamani, E. D. et al. Artificial intelligence and big data analytics for supply chain resilience: A systematic literature review. Ann. Oper. Res. 327(2), 605–632 (2023).Article

Zamani，E.D.等人，《供应链弹性的人工智能和大数据分析：系统文献综述》。。第327（2）号决议，第605-632（2023）号决议。文章

Google Scholar

谷歌学者

Cai, Y. et al. Artificial intelligence in the risk prediction models of cardiovascular disease and development of an independent validation screening tool: A systematic review. BMC Med. https://doi.org/10.1186/s12916-024-03273-7 (2024).Article

Cai，Y.等人，《心血管疾病风险预测模型中的人工智能和独立验证筛选工具的开发：系统综述》。BMC医学。https://doi.org/10.1186/s12916-024-03273-7（2024年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Lopez-Cortes, X. A. et al. Machine-learning applications in oral cancer: A systematic review. Appl. Sci.-Basel 12(11), 5715 (2022).Article

Lopez-Cortes，X.A.等人，《机器学习在口腔癌中的应用：系统综述》。应用。科学-巴塞尔协议12（11），5715（2022）。文章

CAS

中科院

Google Scholar

谷歌学者

Mohsin, S. N. et al. The role of artificial intelligence in prediction, risk stratification, and personalized treatment planning for congenital heart diseases. Cureus J. Med. Sci. https://doi.org/10.7759/cureus.44374 (2023).Article

Mohsin，S.N.等人，《人工智能在先天性心脏病预测、危险分层和个性化治疗计划中的作用》。Cureus J.医学科学。https://doi.org/10.7759/cureus.44374（2023年）。文章

Google Scholar

谷歌学者

D’Agostino, R. B. et al. General cardiovascular risk profile for use in primary care: The Framingham heart study. Circulation 118(4), E86–E86 (2008).

D'Agostino，R.B.等人。用于初级保健的一般心血管风险概况：弗雷明汉心脏研究。发行量118（4），E86–E86（2008）。

Google Scholar

谷歌学者

Zheng, Y. et al. Risk prediction models for breast cancer: a systematic review. BMJ Open 12(7), e055398 (2022).Article

Zheng，Y.等。乳腺癌风险预测模型：系统综述。BMJ公开赛12（7），e055398（2022）。文章

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Zeng, S. et al. Machine learning approaches for the prediction of postoperative complication risk in liver resection patients. BMC Med. Inform. Decis. Mak. https://doi.org/10.1186/s12911-021-01731-3 (2021).Article

Zeng，S.等人。用于预测肝切除患者术后并发症风险的机器学习方法。。十分之一。马克。https://doi.org/10.1186/s12911-021-01731-3（2021年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Monteiro Costa, M. L. et al. Development and validation of predictive model for long-term hospitalization, readmission, and in-hospital death of patients over 60 years old. Einstein-Sao Paulo https://doi.org/10.31744/einstein_journal/2022AO8012 (2022).Article

Monteiro Costa，M.L.等人。60岁以上患者长期住院，再入院和院内死亡预测模型的开发和验证。https://doi.org/10.31744/einstein_journal/2022AO8012（2022年）。文章

Google Scholar

谷歌学者

Yarborough, B. J. H. et al. Clinical implementation of suicide risk prediction models in healthcare: A qualitative study. BMC Psychiatry https://doi.org/10.1186/s12888-022-04400-5 (2022).Article

Yarborough，B.J.H.等人。医疗保健中自杀风险预测模型的临床实施：定性研究。BMC精神病学https://doi.org/10.1186/s12888-022-04400-5（2022年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Wiberg, H. M., Data-Driven Healthcare via Constraint Learning and Analytics. (2022).Obermeyer, Z. & Emanuel, E. J. Predicting the future—Big data, machine learning, and clinical medicine. N. Engl. J. Med. 375(13), 1216–1219 (2016).Article

Wiberg，H.M.，通过约束学习和分析进行数据驱动的医疗保健。（2022年）。Obermeyer，Z.＆Emanuel，E.J.预测未来的大数据，机器学习和临床医学。N、 英语。J、 医学375（13），1216-1219（2016）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Liu, Z. et al. Effect of SNPs in protein kinase <i>Cz</i> gene on gene expression in the reporter gene detection system. World J. Gastroenterol. 10(16), 2357–2360 (2004).Article

Liu，Z。等人。在报告基因检测系统中，蛋白激酶i Cz/i基因中的SNP对基因表达的影响。世界J.胃肠病学。10（16），2357–2360（2004）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Beam, A. L. & Kohane, I. S. Big data and machine learning in health care. Jama-J. Am. Med. Assoc. 319(13), 1317–1318 (2018).Article

Beam，A.L。和Kohane，I.S。医疗保健中的大数据和机器学习。《美国医学会杂志》319（13），1317-1318（2018）。文章

Google Scholar

谷歌学者

Wang, C.-W., Khalil, M.-A. & Firdi, N. P. A survey on deep learning for precision oncology. Diagnostics 12(6), 1489 (2022).Article

Wang，C.-W.，Khalil，M.-A。和Firdi，N.P。关于精确肿瘤学深度学习的调查。诊断12（6），1489（2022）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Cuocolo, R. et al. Machine learning in oncology: A clinical appraisal. Cancer Lett. 481, 55–62 (2020).Article

Cuocolo，R。等人，《肿瘤学中的机器学习：临床评估》。癌症Lett。481,55-62（2020）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

McKillip, R. P. et al. Patient perceptions of care as influenced by a large institutional pharmacogenomic implementation program. Clin. Pharmacol. Ther. 102(1), 106–114 (2017).Article

McKillip，R.P.等人。受大型机构药物基因组学实施计划影响的患者对护理的看法。临床。药理学。他们。102（1），106-114（2017）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Ryan, D. K. et al. Artificial intelligence and machine learning for clinical pharmacology. Br. J. Clin. Pharmacol. 90(3), 629–639 (2024).Article

Ryan，D.K.等人，《临床药理学的人工智能和机器学习》。Br.J.临床。药理学。90（3），629-639（2024）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Wang, Y. et al. DeepDRK: A deep learning framework for drug repurposing through kernel-based multi-omics integration. Brief. Bioinform. https://doi.org/10.1093/bib/bbab048 (2021).Article

Wang，Y.等人。DeepDRK：通过基于内核的多组学集成进行药物再利用的深度学习框架。简介。生物信息。https://doi.org/10.1093/bib/bbab048（2021年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Varnai, R. et al. Pharmacogenomic biomarkers in docetaxel treatment of prostate cancer: From discovery to implementation. Genes 10(8), 599 (2019).Article

Varnai，R.等。多西紫杉醇治疗前列腺癌的药物基因组生物标志物：从发现到实施。基因10（8），599（2019）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Werner, K. et al. Behavioural economic interventions to reduce health care appointment non-attendance: a systematic review and meta-analysis. BMC Health Serv. Res. https://doi.org/10.1186/s12913-023-10059-9 (2023).Article

Werner，K.等人，《减少医疗保健预约不出席的行为经济干预：系统评价和荟萃分析》。BMC健康服务。研究。https://doi.org/10.1186/s12913-023-10059-9（2023年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Posadzki, P. et al. Automated telephone communication systems for preventive healthcare and management of long-term conditions. Cochrane Database Syst. Rev. https://doi.org/10.1002/14651858.CD009921.pub2 (2016).Article

Posadzki，P。等人。用于预防保健和长期疾病管理的自动电话通信系统。Cochrane数据库系统。版次：。https://doi.org/10.1002/14651858.CD009921.pub2（2016年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Bjornholt, K. et al. The effect of daily small text message reminders for medicine compliance amongst young people connected with the outpatient department for child and adolescent psychiatry. A controlled and randomized investigation. Nordic J. Psychiatry 70(4), 285–289 (2016).Article .

Bjornholt，K.等人。与儿童和青少年精神病学门诊有关的年轻人每天发送短信提醒对药物依从性的影响。对照和随机调查。北欧J.精神病学70（4），285-289（2016）。文章。

Google Scholar

谷歌学者

Dombkowski, K. J. et al. The impacts of email reminder/recall on adolescent influenza vaccination. Vaccine 35(23), 3089–3095 (2017).Article

Dombkowski，K.J.等人。电子邮件提醒/召回对青少年流感疫苗接种的影响。疫苗35（23），3089-3095（2017）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Price, W. N. & Cohen, I. G. Privacy in the age of medical big data. Nat. Med. 25(1), 37–43 (2019).Article

Price，W.N。＆Cohen，I.G。医疗大数据时代的隐私。《自然医学》25（1），37-43（2019）。文章

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Figueroa-Lorenzo, S., Anorga, J. & Arrizabalaga, S. A Role-based access control model in modbus SCADA systems. A centralized model approach. Sensors 19(20), 4455 (2019).Article

Figueroa Lorenzo，S.，Anorga，J。＆Arrizabalaga，S。modbus SCADA系统中基于角色的访问控制模型。。传感器19（20），4459（2019）。文章

ADS

广告

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Boyce, B. HIPAA compliance from a private practice purview. J. Acad. Nutr. Dietetics 114(9), 1341 (2014).Article

。J、 阿卡德。营养。《营养学》114（9），1341（2014）。文章

Google Scholar

谷歌学者

Selbst, A.D., et al. Fairness and Abstraction in Sociotechnical Systems. In: ACM Conference on Fairness, Accountability, and Transparency (FAT). Atlanta, GA. (2019).Daneshjou, R. et al. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci. Adv. https://doi.org/10.1126/sciadv.abq6147 (2022).Article .

Selbst，A.D。等人，《社会技术系统中的公平与抽象》。发表于：ACM公平、问责制和透明度会议（FAT）。佐治亚州亚特兰大（2019）。Daneshjou，R.等人。在不同的策划临床图像集上，皮肤病AI表现的差异。科学。高级。https://doi.org/10.1126/sciadv.abq6147（2022年）。文章。

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Larrazabal, A. J. et al. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc. Natl. Acad. Sci. U. S. A. 117(23), 12592–12594 (2020).Article

Larrazabal，A.J.等人。医学成像数据集中的性别不平衡产生了用于计算机辅助诊断的有偏见的分类器。。纳特尔。阿卡德。科学。U、 《美国法典》117（23），12592–12594（2020）。文章

ADS

广告

CAS

中科院

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Weenink, J.-W. et al. Publication of inspection frameworks: A qualitative study exploring the impact on quality improvement and regulation in three healthcare settings. BMJ Qual. Saf. 30(10), 804–811 (2021).Article

Weenink，J.-W.等人，《检查框架的出版：一项定性研究，探讨在三种医疗保健环境中对质量改进和监管的影响》。。南非。30（10），804-811（2021）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Melville, S. et al. A comparison and calibration of a wrist-worn blood pressure monitor for patient management: Assessing the reliability of innovative blood pressure devices. J. Med. Internet Res. 20(4), e111 (2018).Article

Melville，S.等人。用于患者管理的腕式血压监测仪的比较和校准：评估创新血压装置的可靠性。J、 医学互联网第20（4）号决议，e111（2018）。文章

MathSciNet

MathSciNet

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Miller, D. D. Machine intelligence in cardiovascular medicine. Cardiol. Rev. 28(2), 53–64 (2020).Article

Miller，D.D。心血管医学中的机器智能。心脏病。修订版28（2），53-64（2020）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Lievevrouw, E., Marelli, L. & Van Hoyweghen, I. The FDA’s standard-making process for medical digital health technologies: Co-producing technological and organizational innovation. Biosocieties 17(3), 549–576 (2022).Article

Lievevrouw，E.，Marelli，L。和Van Hoyweghen，I。FDA的医疗数字健康技术标准制定过程：共同产生技术和组织创新。生物社会17（3），549-576（2022）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Brindza, L. J. FDA regulation of computerized cytology devices. Anal. Quant. Cytol. Histol. 13(1), 3–6 (1991).MathSciNet

Brindza，L.J。FDA对计算机化细胞学设备的监管。。数量。。历史。13（1），3-6（1991）。数学网

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Wu, Y. et al. BERT-based natural language processing of drug labeling documents: A case study for classifying drug-induced liver injury risk. Front. Artif. Intell. https://doi.org/10.3389/frai.2021.729834 (2021).Article

Wu，Y.等人。基于BERT的药物标签文件自然语言处理：药物性肝损伤风险分类的案例研究。正面。人工制品。因特尔。https://doi.org/10.3389/frai.2021.729834（2021年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Okamoto, R., Kojima, R. & Nakatsui, M. Toward AI-supported evaluation for safety control measures against near-miss events in pharmaceutical products. Saf. Sci. 168, 106314 (2023).Article

Okamoto，R.，Kojima，R。＆Nakatsui，M。Toward AI支持评估针对药品未遂事件的安全控制措施。南非。科学。168106314（2023）。文章

Google Scholar

谷歌学者

Meszaros, J., Minari, J. & Huys, I. The future regulation of artificial intelligence systems in healthcare services and medical research in the European Union. Front. Genet. https://doi.org/10.3389/fgene.2022.927721 (2022).Article

Meszaros，J.，Minari，J。＆Huys，I。欧盟医疗服务和医学研究中人工智能系统的未来监管。正面。基因。https://doi.org/10.3389/fgene.2022.927721（2022年）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Moshi, M. R. et al. Evaluation of mobile health applications: Is regulatory policy up to the challenge?. Int. J. Technol. Assess. Health Care 35(4), 351–360 (2019).Article

Moshi，M.R.等人，《移动健康应用的评估：监管政策能否应对挑战？》？。Int.J.Technol公司。评估。医疗保健35（4），351-360（2019）。文章

PubMed

PubMed

Google Scholar

谷歌学者

Bruining, N. & de Jaegere, P. The Mayo Clinic: Digital health centre of excellence. Eur. Heart J. Digit. Health 3(1), 5–7 (2022).Article

Bruining，N。和de Jaegere，P。梅奥诊所：数字健康卓越中心。欧洲心脏病J.数字。健康3（1），5-7（2022）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Haidet, P. et al. Shared decision making reimagined. Patient Educ. Counsel. 123, 108249 (2024).Article

Haidet，P.等人，重新设想了共同决策。患者教育。律师。123108249（2024）。文章

Google Scholar

谷歌学者

Char, D. S., Shah, N. H. & Magnus, D. Implementing machine learning in health care—Addressing ethical challenges. N. Engl. J. Med. 378(11), 981–983 (2018).Article

Char，D.S.，Shah，N.H。＆Magnus，D。在医疗保健中实施机器学习以应对道德挑战。N、 英语。J、 医学杂志378（11），981-983（2018）。文章

PubMed

PubMed

PubMed Central

公共医学中心

Google Scholar

谷歌学者

Topol, E. J. High-performance medicine: The convergence of human and artificial intelligence. Nat. Med. 25(1), 44–56 (2019).Article

Topol，E.J。高性能医学：人类和人工智能的融合。《自然医学》25（1），44-56（2019）。文章

CAS

中科院

PubMed

PubMed

Google Scholar

谷歌学者

Download referencesAcknowledgementsThis work was supported by the National Natural Science Foundation of China (No. 82171053, 81570864)Author informationAuthors and AffiliationsDepartment of Ophthalmology, The Second Norman Bethune Hospital of Jilin University, Changchun, 130000, ChinaYu-Lin Li, Mu-Yang Wei & Guang-Yu LiInternational School, Beijing University of Posts and Telecommunications, Bei Jing, 100876, ChinaYu-Hao LiAuthorsYu-Hao LiView author publicationsYou can also search for this author in.

下载参考文献致谢这项工作得到了国家自然科学基金（No.8217105381570864）作者信息作者和附属机构吉林大学第二诺曼白求恩医院眼科的支持，长春，130000，中国李玉林，牟阳伟和李光裕北京邮电大学国际学院，北京，100876，中国李玉浩作者或李玉浩作者出版物您也可以在中搜索这位作者。

PubMed Google ScholarYu-Lin LiView author publicationsYou can also search for this author in

PubMed Google ScholarYu Lin LiView作者出版物您也可以在

PubMed Google ScholarMu-Yang WeiView author publicationsYou can also search for this author in

PubMed Google Scholarmamu Yang WeiView作者出版物您也可以在

PubMed Google ScholarGuang-Yu LiView author publicationsYou can also search for this author in

PubMed Google ScholarGuang Yu LiView作者出版物您也可以在

PubMed Google ScholarContributionsY-H L: Conceptualization, Methodology, Writing. Y-L L, M-Y W: Writing—review & editing. G-Y L: Conceptualization, Writing—original draft, Writing—review & editing, Funding acquisition, Resources, Supervision.Corresponding authorCorrespondence to.

PubMed谷歌学术贡献SY-H L：概念化，方法论，写作。Y-L L，M-Y W：写作评论和编辑。G-Y L：概念化，撰写原稿，撰写评论和编辑，资金获取，资源，监督。对应作者对应。

Guang-Yu Li.Ethics declarations

李光宇。道德宣言

Competing interests

相互竞争的利益

The authors declare no competing interests.

作者声明没有利益冲突。

Additional informationPublisher's noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Rights and permissions

Additional informationPublisher的noteSpringer Nature在已发布地图和机构隶属关系中的管辖权主张方面保持中立。权限和权限

Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material.

开放获取本文是根据知识共享署名非商业性NoDerivatives 4.0国际许可证授权的，该许可证允许以任何媒介或格式进行任何非商业性使用，共享，分发和复制，只要您对原始作者和来源给予适当的信任，提供知识共享许可证的链接，并指出您是否修改了许可材料。

You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

根据本许可证，您无权共享源自本文或其部分的改编材料。本文中的图像或其他第三方材料包含在文章的知识共享许可证中，除非该材料的信用额度中另有说明。如果材料未包含在文章的知识共享许可中，并且您的预期用途不受法律法规的许可或超出许可用途，则您需要直接获得版权所有者的许可。

To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/..

要查看此许可证的副本，请访问http://creativecommons.org/licenses/by-nc-nd/4.0/..

Reprints and permissionsAbout this articleCite this articleLi, YH., Li, YL., Wei, MY. et al. Innovation and challenges of artificial intelligence technology in personalized healthcare.

转载和许可本文引用本文Li，YH。，李，YL。，Wei，MY。等人。人工智能技术在个性化医疗保健中的创新和挑战。

Sci Rep 14, 18994 (2024). https://doi.org/10.1038/s41598-024-70073-7Download citationReceived: 26 April 2024Accepted: 12 August 2024Published: 16 August 2024DOI: https://doi.org/10.1038/s41598-024-70073-7Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy to clipboard.

Sci Rep 1418994（2024）。https://doi.org/10.1038/s41598-024-70073-7Download引文接收日期：2024年4月26日接受日期：2024年8月12日发布日期：2024年8月16日OI：https://doi.org/10.1038/s41598-024-70073-7Share本文与您共享以下链接的任何人都可以阅读此内容：获取可共享链接对不起，本文目前没有可共享的链接。复制到剪贴板。

Provided by the Springer Nature SharedIt content-sharing initiative

由Springer Nature SharedIt内容共享计划提供

KeywordsArtificial intelligenceHealthcareVirtual assistant chatbotsRemote patient careData security

关键词人工智能健康护理虚拟助理聊天机器人远程患者护理数据安全

Subjects

主题

Computer scienceHealth care economicsHealth policyHealth servicesPatient educationPublic healthQuality of life

计算机科学卫生保健经济卫生政策卫生服务空间教育公共卫生生活质量

CommentsBy submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

评论通过提交评论，您同意遵守我们的条款和社区指南。如果您发现有虐待行为或不符合我们的条款或准则，请将其标记为不合适。