Digital Innovations in Patient-Centered Care: The Emerging Role of Natural Language Processing
Downloads
Patient-Centered Care (PCC) faces critical challenges such as fragmented communication, limited interpretation of patient narratives, and underutilization of real-time feedback. Natural Language Processing (NLP) offers promising solutions by enabling the structured analysis of unstructured data like Electronic Health Records (EHRs), social media content, and patient feedback. This study aims to systematically map the scholarly landscape of NLP applications in PCC between 2015 and 2025, identifying key trends, dominant research themes, and knowledge gaps. A bibliometric analysis was conducted using the Scopus database, with inclusion criteria focused on peer-reviewed, English-language articles in relevant health and technology fields. From an initial set of 645 records, 254 publications met the eligibility requirements. Data cleaning and network analysis were performed using OpenRefine, MS Excel, and VOSviewer, focusing on co-authorship, keyword co-occurrence, and citation density. Results indicate an exponential increase in research output, rising from five publications in 2015 to eighty-one in 2024, largely driven by high-income countries with advanced digital infrastructure. Five thematic clusters emerged: (1) Social Media–Based Patient Communication, (2) Sentiment Analysis for Care Feedback, (3) Clinical Decision Support via NLP, (4) AI-Powered Patient Empowerment, and (5) Modeling Perceived Quality of Care. Implications include the development of real-time, AI-driven feedback loops, multimodal data integration, and culturally responsive chatbot systems. This study also highlights urgent directions for future research, such as building explainable and ethical AI models, integrating diverse data sources, and designing adaptive NLP applications that support longitudinal patient engagement. It offers foundational insights into the evolving role of NLP in enhancing personalized, responsive, and ethically sound PCC.
[1] Langford AT, Kang SK, Braithwaite RS. When does nonadherence indicate a deviation from patient-centered care? American Journal of Managed Care 2021;27:E141–4. https://doi.org/10.37765/ajmc.2021.88635.
[2] Goldfarb MJ, Saylor MA, Bozkurt B, Code J, Di Palo KE, Durante A, et al. Patient-Centered Adult Cardiovascular Care: A Scientific Statement From the American Heart Association. Circulation 2024;149:E1176–88. https://doi.org/10.1161/CIR.0000000000001233.
[3] Rouhi AD, Millstein JH. “Let Me Show You How I Think About This Problem…” : Impactful Nuances of Shared Medical Decision-Making. J Patient Exp 2022;9:454–61. https://doi.org/10.1177/23743735221089699.
[4] Ong H, Fernandez P, Lim H. Family engagement as part of managing patients with mental illness in primary care. Singapore Med J 2021;62:213–9. https://doi.org/10.11622/smedj.2021057.
[5] Brunelli A, Blackmon SH, Sentürk M, Cavalheri V, Pompili C. Patient-centred care in thoracic surgery: a holistic approach—A review of the subjects of enhanced recovery after surgery, rehabilitation, pain management and patient-reported outcome measures in thoracic surgery. J Thorac Dis 2022;14:546–52. https://doi.org/10.21037/jtd-21-1763.
[6] Newman B, Joseph K, Chauhan A, Seale H, Li J, Manias E, et al. Do patient engagement interventions work for all patients? A systematic review and realist synthesis of interventions to enhance patient safety. Health Expectations 2021;24:1905–23. https://doi.org/10.1111/hex.13343.
[7] Li F, Li J, Hu L, Hou Y. Deploying Natural Language Processing for Improved Patient-Doctor Communication in Gynecological Consultations. 2024 IEEE 6th Eurasia Conference on Biomedical Engineering, Healthcare and Sustainability (ECBIOS), IEEE; 2024, p. 318–22. https://doi.org/10.1109/ECBIOS61468.2024.10885466.
[8] van Buchem MM, Neve OM, Kant IMJ, Steyerberg EW, Boosman H, Hensen EF. Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM). BMC Med Inform Decis Mak 2022;22:183. https://doi.org/10.1186/s12911-022-01923-5.
[9] Veeramani M, Karthick P, Venkateswaran S, Sriman B, Bhanu ST, Devi VS. Transparency in Text. Explainable Artificial Intelligence in the Healthcare Industry, Wiley; 2025, p. 131–60. https://doi.org/10.1002/9781394249312.ch7.
[10] Rojas-Carabali W, Agrawal R, Gutierrez-Sinisterra L, Baxter SL, Cifuentes-González C, Wei YC, et al. Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician. Asia-Pacific Journal of Ophthalmology 2024;13:100084. https://doi.org/10.1016/j.apjo.2024.100084.
[11] Clay B, Bergman HI, Salim S, Pergola G, Shalhoub J, Davies AH. Natural language processing techniques applied to the electronic health record in clinical research and practice - an introduction to methodologies. Comput Biol Med 2025;188:109808. https://doi.org/10.1016/j.compbiomed.2025.109808.
[12] Thatoi P, Choudhary R, Shiwlani A, Qureshi HA, Kumar S. Natural Language Processing (NLP) in the Extraction of Clinical Information from Electronic Health Records (EHRs) for Cancer Prognosis . International Journal of Membrane Science and Technology, 2023;10:2676–94.
[13] Gala D, Behl H, Shah M, Makaryus AN. The Role of Artificial Intelligence in Improving Patient Outcomes and Future of Healthcare Delivery in Cardiology: A Narrative Review of the Literature. Healthcare 2024;12:481. https://doi.org/10.3390/healthcare12040481.
[14] Khanbhai M, Anyadi P, Symons J, Flott K, Darzi A, Mayer E. Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review. BMJ Health Care Inform 2021;28:e100262. https://doi.org/10.1136/bmjhci-2020-100262.
[15] Terheyden JH, Pielka M, Schneider T, Holz FG, Sifa R. A new generation of patient-reported outcome measures with large language models. J Patient Rep Outcomes 2025;9:34. https://doi.org/10.1186/s41687-025-00867-4.
[16] Chen Y, Zhan X, Yang W, Yan X, Du Y, Zhao T. A NLP analysis of digital demand for healthcare jobs in China. Sci Rep 2025;15:14518. https://doi.org/10.1038/s41598-025-98552-5.
[17] Tabaie A, Sengupta S, Pruitt ZM, Fong A. A natural language processing approach to categorise contributing factors from patient safety event reports. BMJ Health Care Inform 2023;30:e100731. https://doi.org/10.1136/bmjhci-2022-100731.
[18] Khanbhai M, Anyadi P, Symons J, Flott K, Darzi A, Mayer E. Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review. BMJ Health Care Inform 2021;28:e100262. https://doi.org/10.1136/bmjhci-2020-100262.
[19] Clay B, Bergman HI, Salim S, Pergola G, Shalhoub J, Davies AH. Natural language processing techniques applied to the electronic health record in clinical research and practice - an introduction to methodologies. Comput Biol Med 2025;188:109808. https://doi.org/10.1016/j.compbiomed.2025.109808.
[20] Cabello-Collado C, Rodriguez-Juan J, Ortiz-Perez D, Garcia-Rodriguez J, Tomás D, Vizcaya-Moreno MF. Automated Generation of Clinical Reports Using Sensing Technologies with Deep Learning Techniques. Sensors 2024;24:2751. https://doi.org/10.3390/s24092751.
[21] Kumar M, Khan L, Chang H-T. Evolving techniques in sentiment analysis: a comprehensive review. PeerJ Comput Sci 2025;11:e2592. https://doi.org/10.7717/peerj-cs.2592.
[22] Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering 2024;11:1–38. https://doi.org/10.3390/bioengineering11040337.
[23] Singh GB, Kumar R, Ghosh RC, Punia A, Sharma N, Bhakuni P. Transforming Healthcare Systems With AI. https://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/979-8-3693-9521-9.ch004, IGI Global Scientific Publishing; 2025, p. 111–30. https://doi.org/10.4018/979-8-3693-9521-9.ch004.
[24] Talwar A, Shen C, Shin JH. Natural language processing in plastic surgery patient consultations. Artificial Intelligence Surgery 2025;5:46–52. https://doi.org/10.20517/ais.2024.81.
[25] Pan M, Huang R, Liu C, Xiong Y, Li N, Peng H, et al. Application of artificial intelligence in palliative care: a bibliometric analysis of research hotspots and trends. Front Med (Lausanne) 2025;12:1–14. https://doi.org/10.3389/fmed.2025.1597195.
[26] Pranckutė R. Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World. Publications 2021;9:1–59.
[27] Wilder EI, Walters WH. Using Conventional Bibliographic Databases for Social Science Research: Web of Science and Scopus are not the Only Options. Scholarly Assessment Reports 2021;3:4. https://doi.org/10.29024/sar.36.
[28] AlRyalat SAS, Malkawi LW, Momani SM. Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases. Journal of Visualized Experiments 2019;2019:e58494. https://doi.org/10.3791/58494.
[29] Kumpulainen M, Seppänen M. Combining Web of Science and Scopus datasets in citation-based literature study. Scientometrics 2022;127:5613–31. https://doi.org/10.1007/s11192-022-04475-7.
[30] Alsharif AH, Salleh NZM, Baharun R. Research trends of neuromarketing: A bibliometric analysis. J Theor Appl Inf Technol 2020;98:2948–62.
[31] Manoj Kumar L., George RJ, P.S. A. Bibliometric Analysis for Medical Research. Indian J Psychol Med 2023;45:277–82. https://doi.org/10.1177/02537176221103617.
[32] Kraus S, Schiavone F, Pluzhnikova A, Invernizzi AC. Digital transformation in healthcare: Analyzing the current state-of-research. J Bus Res 2021;123:557–67. https://doi.org/10.1016/j.jbusres.2020.10.030.
[33] Paul M, Maglaras L, Ferrag MA, Almomani I. Digitization of healthcare sector: A study on privacy and security concerns. ICT Express 2023;9:571–88. https://doi.org/10.1016/j.icte.2023.02.007.
[34] Tortorella GL, Fogliatto FS, Saurin TA, Tonetto LM, McFarlane D. Contributions of Healthcare 4.0 digital applications to the resilience of healthcare organizations during the COVID-19 outbreak. Technovation 2022;111:102379. https://doi.org/10.1016/j.technovation.2021.102379.
[35] Işık C, Aydın E, Dogru T, Rehman A, Sirakaya-Turk E, Karagöz D. Innovation Research in Tourism and Hospitality Field: A Bibliometric and Visualization Analysis. Sustainability 2022;14:7889. https://doi.org/10.3390/su14137889.
[36] Hoang A-D. Evaluating Bibliometrics Reviews: A Practical Guide for Peer Review and Critical Reading. Eval Rev 2025. https://doi.org/10.1177/0193841X251336839.
[37] Suzina AC. English as lingua franca . Or the sterilisation of scientific work. Media Cult Soc 2021;43:171–9. https://doi.org/10.1177/0163443720957906.
[38] Tucudean G, Bucos M, Dragulescu B, Caleanu CD. Natural language processing with transformers: a review. PeerJ Comput Sci 2024;10:e2222. https://doi.org/10.7717/peerj-cs.2222.
[39] Khurana D, Koli A, Khatter K, Singh S. Natural language processing: state of the art, current trends and challenges. Multimed Tools Appl 2023;82:3713–44. https://doi.org/10.1007/s11042-022-13428-4.
[40] Shickel B, Tighe PJ, Bihorac A, Rashidi P. Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis. IEEE J Biomed Health Inform 2018;22:1589–604. https://doi.org/10.1109/JBHI.2017.2767063.
[41] Wu H, Wang M, Wu J, Francis F, Chang Y-H, Shavick A, et al. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. NPJ Digit Med 2022;5:186. https://doi.org/10.1038/s41746-022-00730-6.
[42] Kotei E, Thirunavukarasu R. A Systematic Review of Transformer-Based Pre-Trained Language Models through Self-Supervised Learning. Information 2023;14:187. https://doi.org/10.3390/info14030187.
[43] Sarker A, Al-Garadi MA, Yang Y-C, Choi J, Quyyumi AA, Martin GS. Defining Patient-Oriented Natural Language Processing: A New Paradigm for Research and Development to Facilitate Adoption and Use by Medical Experts. JMIR Med Inform 2021;9:e18471. https://doi.org/10.2196/18471.
[44] Maleki Varnosfaderani S, Forouzanfar M. The Role of AI in Hospitals and Clinics: Transforming Healthcare in the 21st Century. Bioengineering 2024;11:1–38. https://doi.org/10.3390/bioengineering11040337.
[45] Morley J, Floridi L. NHS AI Lab: Why We Need to Be Ethically Mindful About AI for Healthcare. SSRN Electronic Journal 2019. https://doi.org/10.2139/ssrn.3445421.
[46] Wang J, Deng H, Liu B, Hu A, Liang J, Fan L, et al. Systematic evaluation of research progress on natural language processing in medicine over the past 20 years: Bibliometric study on pubmed. J Med Internet Res 2020;22:1–19. https://doi.org/10.2196/16816.
[47] Jerfy A, Selden O, Balkrishnan R. The Growing Impact of Natural Language Processing in Healthcare and Public Health. Inquiry 2024;61. https://doi.org/10.1177/00469580241290095.
[48] Butler JM, Gibson B, Patterson O V., Damschroder LJ, Halls CH, Denhalter DW, et al. Clinician documentation of patient centered care in the electronic health record. BMC Med Inform Decis Mak 2022;22:65. https://doi.org/10.1186/s12911-022-01794-w.
[49] Abbas K, Hasan MK, Abbasi A, Mokhtar UA, Khan A, Abdullah SNHS, et al. Predicting the Future Popularity of Academic Publications Using Deep Learning by Considering It as Temporal Citation Networks. IEEE Access 2023;11:83052–68. https://doi.org/10.1109/ACCESS.2023.3290906.
[50] Shahzad M, Alhoori H, Freedman R, Rahman SA. Quantifying the online long-term interest in research. J Informetr 2022;16:1–39. https://doi.org/10.1016/j.joi.2022.101288.
[51] Nashwan AJ. Diversifying the Impact Factor: Shifting from Mere Citations to True Representation. Editing Practice 2023;1:1–2. https://doi.org/10.54844/ep.2023.0483.
[52] Schlierkamp J, Berchtold C, Linde-Frech I. Uncovering the Science-Policy Interface: Applying Bibliometric Approaches to the Wildfire Risk Management Domain. Environ Manage 2025;75:1368–87. https://doi.org/10.1007/s00267-025-02161-x.
[53] Zheng Q, Xu J, Gao Y, Liu M, Cheng L, Xiong L, et al. Past, present and future of living systematic review: a bibliometrics analysis. BMJ Glob Health 2022;7:e009378. https://doi.org/10.1136/bmjgh-2022-009378.
[54] Nielsen MW, Andersen JP. Global citation inequality is on the rise. Proceedings of the National Academy of Sciences 2021;118:1–10. https://doi.org/10.1073/pnas.2012208118.
[55] Al-Garadi MA, Yang Y-C, Sarker A. The Role of Natural Language Processing during the COVID-19 Pandemic: Health Applications, Opportunities, and Challenges. Healthcare 2022;10:2270. https://doi.org/10.3390/healthcare10112270.
[56] Wu H, Wang M, Wu J, Francis F, Chang Y-H, Shavick A, et al. A survey on clinical natural language processing in the United Kingdom from 2007 to 2022. NPJ Digit Med 2022;5:186. https://doi.org/10.1038/s41746-022-00730-6.
[57] Hu S, Oppong A, Mogo E, Collins C, Occhini G, Barford A, et al. Natural Language Processing Technologies for Public Health in Africa: Scoping Review. J Med Internet Res 2025;27:e68720. https://doi.org/10.2196/68720.
[58] Chen Y, Zhan X, Yang W, Yan X, Du Y, Zhao T. A NLP analysis of digital demand for healthcare jobs in China. Sci Rep 2025;15:14518. https://doi.org/10.1038/s41598-025-98552-5.
[59] He Z. When data protection norms meet digital health technology: China’s regulatory approaches to health data protection. Computer Law & Security Review 2022;47:105758. https://doi.org/10.1016/j.clsr.2022.105758.
[60] Shen Y, Yu J, Zhou J, Hu G. Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review. J Med Internet Res 2025;27:e59024. https://doi.org/10.2196/59024.
[61] Bharti U, Bajaj D, Batra H, Lalit S, Lalit S, Gangwani A. Medbot: Conversational Artificial Intelligence Powered Chatbot for Delivering Tele-Health after COVID-19. 2020 5th International Conference on Communication and Electronics Systems (ICCES), IEEE; 2020, p. 870–5. https://doi.org/10.1109/ICCES48766.2020.9137944.
[62] Klug K, Beckh K, Antweiler D, Chakraborty N, Baldini G, Laue K, et al. From admission to discharge: a systematic review of clinical natural language processing along the patient journey. BMC Med Inform Decis Mak 2024;24:238. https://doi.org/10.1186/s12911-024-02641-w.
[63] Ranard BL, Werner RM, Antanavicius T, Schwartz HA, Smith RJ, Meisel ZF, et al. Yelp Reviews Of Hospital Care Can Supplement And Inform Traditional Surveys Of The Patient Experience Of Care. Health Aff 2016;35:697–705. https://doi.org/10.1377/hlthaff.2015.1030.
[64] Hawkins JB, Brownstein JS, Tuli G, Runels T, Broecker K, Nsoesie EO, et al. Measuring patient-perceived quality of care in US hospitals using Twitter. BMJ Qual Saf 2016;25:404–13. https://doi.org/10.1136/bmjqs-2015-004309.
[65] Martinez B, Dailey F, Almario C V., Keller MS, Desai M, Dupuy T, et al. Patient Understanding of the Risks and Benefits of Biologic Therapies in Inflammatory Bowel Disease: Insights from a Large-scale Analysis of Social Media Platforms. Inflamm Bowel Dis 2017;23:1057–64. https://doi.org/10.1097/MIB.0000000000001110.
[66] Foufi V, Timakum T, Gaudet-Blavignac C, Lovis C, Song M. Mining of Textual Health Information from Reddit: Analysis of Chronic Diseases With Extracted Entities and Their Relations. J Med Internet Res 2019;21:e12876. https://doi.org/10.2196/12876.
[67] Zhang L, Hall M, Bastola D. Utilizing Twitter data for analysis of chemotherapy. Int J Med Inform 2018;120:92–100. https://doi.org/10.1016/J.IJMEDINF.2018.10.002.
[68] Rastegar-Mojarad M, Ye Z, Wall D, Murali N, Lin S. Collecting and Analyzing Patient Experiences of Health Care From Social Media. JMIR Res Protoc 2015;4:e78. https://doi.org/10.2196/resprot.3433.
[69] Cook N, Mullins A, Gautam R, Medi S, Prince C, Tyagi N, et al. Evaluating Patient Experiences in Dry Eye Disease Through Social Media Listening Research. Ophthalmol Ther 2019;8:407–20. https://doi.org/10.1007/s40123-019-0188-4.
[70] Jeblick K, Schachtner B, Dexl J, Mittermeier A, Stüber AT, Topalis J, et al. ChatGPT makes medicine easy to swallow: an exploratory case study on simplified radiology reports. Eur Radiol 2023;34:2817–25. https://doi.org/10.1007/s00330-023-10213-1.
[71] Garcelon N, Neuraz A, Salomon R, Faour H, Benoit V, Delapalme A, et al. A clinician friendly data warehouse oriented toward narrative reports: Dr. Warehouse. J Biomed Inform 2018;80:52–63. https://doi.org/10.1016/j.jbi.2018.02.019.
[72] Hao H, Zhang K, Wang W, Gao G. A tale of two countries: International comparison of online doctor reviews between China and the United States. Int J Med Inform 2017;99:37–44. https://doi.org/10.1016/J.IJMEDINF.2016.12.007.
[73] De Jong R, Bus D. VOSviewer: putting research into context. Research Software Community Leiden 2023. https://doi.org/10.21428/a1847950.acdc99d6.
[74] Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS Digital Health 2023;2:e0000198. https://doi.org/10.1371/journal.pdig.0000198.
[75] Greaves F, Ramirez-Cano D, Millett C, Darzi A, Donaldson L. Use of sentiment analysis for capturing patient experience from free-text comments posted online. J Med Internet Res 2013;15:1–9. https://doi.org/10.2196/jmir.2721.
[76] Yu H, Fan L, Li L, Zhou J, Ma Z, Xian L, et al. Large Language Models in Biomedical and Health Informatics: A Review with Bibliometric Analysis. J Healthc Inform Res 2024;8:658–711. https://doi.org/10.1007/s41666-024-00171-8.
[77] Alhashmi SM, Hashem IAT, Al-Qudah I. Artificial Intelligence applications in healthcare: A bibliometric and topic model-based analysis. Intelligent Systems with Applications 2024;21:200299. https://doi.org/10.1016/j.iswa.2023.200299.
[78] Schillinger Dean. The Intersections Between Social Determinants of Health, Health Literacy, and Health Disparities. HHS Public Access, vol. 269, 2020, p. 22–41. https://doi.org/10.3233/SHTI200020.
[79] Sallam M. ChatGPT Utility in Healthcare Education, Research, and Practice: Systematic Review on the Promising Perspectives and Valid Concerns. Healthcare 2023;11:887. https://doi.org/10.3390/healthcare11060887.
[80] Van Mierlo T. The 1% rule in four digital health social networks: An observational study. J Med Internet Res 2014;16:1–9. https://doi.org/10.2196/jmir.2966.
[81] Rubinstein B, Matos S. Value Creation for Healthcare Ecosystems through Artificial Intelligence Applied to Physician-to-Physician Communication: A Systematic Review. Neural Process Lett 2025;57:6. https://doi.org/10.1007/s11063-025-11725-1.
[82] Gong J, Sihag V, Kong Q, Zhao L. Visualizing Knowledge Evolution Trends and Research Hotspots of Personal Health Data Research: Bibliometric Analysis. JMIR Med Inform 2021;9:e31142. https://doi.org/10.2196/31142.
[83] De Jong R, Bus D. VOSviewer: putting research into context. Research Software Community Leiden 2023. https://doi.org/10.21428/a1847950.acdc99d.
[84] Vieira ES. The influence of research collaboration on citation impact: the countries in the European Innovation Scoreboard. Scientometrics 2023;128:3555–79. https://doi.org/10.1007/s11192-023-04715-4.
[85] Chae S, Davoudi A, Song J, Evans L, Bowles KH, Mcdonald M V., et al. Developing a clinical decision support framework for integrating predictive models into routine nursing practices in home health care for patients with heart failure. Journal of Nursing Scholarship 2025;57:165–77. https://doi.org/10.1111/jnu.13030.
[86] Carchiolo V, Malgeri M. Trends, Challenges, and Applications of Large Language Models in Healthcare: A Bibliometric and Scoping Review. Future Internet 2025;17:76. https://doi.org/10.3390/fi17020076.
[87] Yin H, Zhang F, Yang X, Meng X, Miao Y, Noor Hussain MS, et al. Research trends of artificial intelligence in pancreatic cancer: a bibliometric analysis. Front Oncol 2022;12:1–13. https://doi.org/10.3389/fonc.2022.973999.
[88] Lee JJ, Haupt JP. Scientific Collaboration on COVID-19 Amidst Geopolitical Tensions between the US and China. Journal of Higher Education 2021;92:303–29. https://doi.org/10.1080/00221546.2020.1827924.
[89] Gao S, Zhai Z, Shan M. Mapping Construction Contractors’ Green Behavior: Developments, Gaps, and Implications. Buildings 2025;15:2902. https://doi.org/10.3390/buildings15162902.
[90] Rojas-Carabali W, Agrawal R, Gutierrez-Sinisterra L, Baxter SL, Cifuentes-González C, Wei YC, et al. Natural Language Processing in medicine and ophthalmology: A review for the 21st-century clinician. Asia-Pacific Journal of Ophthalmology 2024;13:100084. https://doi.org/10.1016/j.apjo.2024.100084.
[91] Nag PK, Bhagat A, Vishnu Priya R, Khare DK. Emotional Intelligence Through Artificial Intelligence: NLP and Deep Learning in the Analysis of Healthcare Texts. 2023 International Conference on Artificial Intelligence for Innovations in Healthcare Industries (ICAIIHI), IEEE; 2023, p. 1–7. https://doi.org/10.1109/ICAIIHI57871.2023.10489117.
[92] Chen S-Y, Kuo HY, Chang S-H. Perceptions of ChatGPT in healthcare: usefulness, trust, and risk. Front Public Health 2024;12:1457131. https://doi.org/10.3389/fpubh.2024.1457131.
[93] Chen Y, Lehmann CU, Malin B. Digital Information Ecosystems in Modern Care Coordination and Patient Care Pathways and the Challenges and Opportunities for AI Solutions. J Med Internet Res 2024;26:e60258. https://doi.org/10.2196/60258.
[94] Shi YV, Komiak S. Unveiling patient-centric interactions in virtual consultation: A comprehensive text mining approach. Health Informatics J 2025;31. https://doi.org/10.1177/14604582251327093.
[95] Joehl HE. Using Natural Language Processing to Evaluate Electronic Health Records of Patients with Ovarian Cancer for Documentation of Goals of Care. Loyola University Chicago, 2023.
[96] Chopra S, Carroll J, Pater J. Providing Context to the “Unknown”: Patient and Provider Reflections on Connecting Personal Tracking, Patient-Reported Insights, and EHR Data within a Post-COVID Clinic. Proc ACM Hum Comput Interact 2024;8:1–34. https://doi.org/10.1145/3686988.
[97] Li Y-H, Li Y-L, Wei M-Y, Li G-Y. Innovation and challenges of artificial intelligence technology in personalized healthcare. Sci Rep 2024;14:18994. https://doi.org/10.1038/s41598-024-70073-7.
[98] Sai S, Yashvardhan U, Chamola V, Sikdar B. Generative AI for Cyber Security: Analyzing the Potential of ChatGPT, DALL-E, and Other Models for Enhancing the Security Space. IEEE Access 2024;12:53497–516. https://doi.org/10.1109/ACCESS.2024.3385107.
[99] Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing dermatology: The role of artificial intelligence in clinical practice. IP Indian Journal of Clinical and Experimental Dermatology 2024;10:107–12. https://doi.org/10.18231/j.ijced.2024.021.
[100] Ahmed M, Lam J, Chow A, Chow C-M. A Primer on Large Language Models (LLMs) and ChatGPT for Cardiovascular Healthcare Professionals. CJC Open 2025;7:660–6. https://doi.org/10.1016/j.cjco.2025.02.012.
[101] Bollos LACL, Zhao Y, Soriano GP, Tanioka T, Otsuka H, Locsin R. Technologies, Physician’s Caring Competency, and Patient Centered Care: A Systematic Review. Journal of Medical Investigation 2023;70:307–16. https://doi.org/10.2152/jmi.70.307.
[102] Alowais SA, Alghamdi SS, Alsuhebany N, Alqahtani T, Alshaya AI, Almohareb SN, et al. Revolutionizing dermatology: The role of artificial intelligence in clinical practice. IP Indian Journal of Clinical and Experimental Dermatology 2024;10:107–12. https://doi.org/10.18231/j.ijced.2024.021.
[103] Diaz-Asper C, Hauglid MK, Chandler C, Cohen AS, Foltz PW, Elvevåg B. A framework for language technologies in behavioral research and clinical applications: Ethical challenges, implications, and solutions. American Psychologist 2024;79:79–91. https://doi.org/10.1037/amp0001195.
[104] Song J, Topaz M, Landau AY, Klitzman R, Shang J, Stone P, et al. Using natural language processing to identify acute care patients who lack advance directives, decisional capacity, and surrogate decision makers. PLoS One 2022;17:e0270220. https://doi.org/10.1371/journal.pone.0270220.
[105] Abbasgholizadeh Rahimi S, Cwintal M, Huang Y, Ghadiri P, Grad R, Poenaru D, et al. Application of Artificial Intelligence in Shared Decision Making: Scoping Review. JMIR Med Inform 2022;10:e36199. https://doi.org/10.2196/36199.
Copyright (c) 2025 Bima Ananta Putra, Merita Arini, Yoviena Kusuma Terichtia Tamaranny (Author)
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution-ShareAlikel 4.0 International (CC BY-SA 4.0) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
