Chatbots and AI Systems for Pre-Hospital Care

Authors

  • Jair Brito do Nascimento Universidad Nacional Ecológica, Santa Cruz de La Sierra, Bolivia
  • Geneci da Silva Barreto Universidad Nacional Ecológica, Santa Cruz de La Sierra, Bolivia https://orcid.org/0009-0004-6166-9194
  • Karin Cristina Santos de Almeida Universidad Nacional Ecológica, Santa Cruz de La Sierra, Bolivia
  • José Carlos Marcolino Neto Universidad Nacional Ecológica, Santa Cruz de La Sierra, Bolivia https://orcid.org/0009-0007-0062-6336
  • Samuel Lucas Ferreira Luz da Silva Universidad Nacional Ecológica, Santa Cruz de La Sierra, Bolivia

DOI:

https://doi.org/10.56226/106

Keywords:

Healthcare Chatbots, Artificial Intelligence, Automated Triage, Pre-Hospital Care, Digital Health

Abstract

Introduction: Using chatbots and artificial intelligence (AI) systems in pre-hospital care has transformed patient triage, initial support and emergency logistics. These technologies offer speed and effectiveness in critical situations and are promising tools for improving health outcomes.

Methods: This study was based on a literature review in PubMed, Scopus and IEEE Xplore databases, between 2015 and 2023. Articles that explored the use of chatbots and AI in emergency triage, first aid and logistics management were selected. Qualitative analysis synthesized the practical and theoretical contributions of these systems.

Results: Chatbots and AI have proven effective in automated triage, reducing response times and improving diagnostic accuracy in medical emergencies. AI systems have optimized ambulance logistics and resources, while chatbots have provided practical guidance on first aid, such as CPR. However, challenges such as unequal access to technology, cultural resistance, and ethical issues related to privacy have been identified.

Discussion: These technologies have a positive impact on pre-hospital care by providing rapid and effective support, especially in remote areas. However, technological and ethical barriers limit their adoption. Cultural acceptance and user trust, combined with appropriate regulations and training, are essential to overcome these obstacles. More accessible and integrated systems represent a promising prospect.

Conclusion: Chatbots and AI are revolutionizing emergency care, offering accuracy, speed, and accessibility. While challenges remain, coordinated efforts in research, infrastructure, and regulation can ensure their ethical and efficient implementation, enhancing their ability to save lives and transform pre-hospital care.

References

Baldwin A. J. (2024). An artificial intelligence language model improves readability of burns first aid information. Burns : journal of the International Society for Burn Injuries, 50(5), 1122–1127. https://doi.org/10.1016/j.burns.2024.03.005

Birkun, A. A., & Gautam, A. (2023). Instructional support on first aid in choking by an artificial intelligence-powered chatbot. The American journal of emergency medicine, 70, 200–202. https://doi.org/10.1016/j.ajem.2023.06.010

Chee, M. L., Chee, M. L., Huang, H., Mazzochi, K., Taylor, K., Wang, H., Feng, M., Ho, A. F. W., Siddiqui, F. J., Ong, M. E. H., & Liu, N. (2023). Artificial intelligence and machine learning in prehospital emergency care: A scoping review. iScience, 26(8), 107407. https://doi.org/10.1016/j.isci.2023.107407

Chen, K. W., Wang, Y. C., Liu, M. H., Tsai, B. Y., Wu, M. Y., Hsieh, P. H., Wei, J. T., Shih, E. S. C., Shiao, Y. T., Hwang, M. J., Wu, Y. L., Hsu, K. C., & Chang, K. C. (2022). Artificial intelligence-assisted remote detection of ST-elevation myocardial infarction using a mini-12-lead electrocardiogram device in prehospital ambulance care. Frontiers in cardiovascular medicine, 9, 1001982. https://doi.org/10.3389/fcvm.2022.1001982

Chenais, G., Lagarde, E., & Gil-Jardiné, C. (2023). Artificial Intelligence in Emergency Medicine: Viewpoint of Current Applications and Foreseeable Opportunities and Challenges. Journal of medical Internet research, 25, e40031. https://doi.org/10.2196/40031

Clark, M., & Severn, M. (2023). Artificial Intelligence in Prehospital Emergency Health Care: CADTH Horizon Scan. Canadian Agency for Drugs and Technologies in Health.

Coherent lab. Generative AI VS Predictive AI | Importance of two popular AI systems. 2023: https://www.coherentlab.com/blog/ generative-vs-predictive-ai. Accessed 17 March, 2025.

Cui, H., Wang, C., Maan, H., Pang, K., Luo, F., Duan, N., & Wang, B. (2024). scGPT: toward building a foundation model for single-cell multi-omics using generative AI. Nature methods, 21(8), 1470–1480. https://doi.org/10.1038/s41592-024-02201-0

Cui, X., Su, T., & Wang, M. Y. (2022). A Study on the Roles of Online Social Tools for the Prevention and Control of Covid-19 Pandemic: The case of WeChat. International Healthcare Review (online). https://doi.org/10.56226/41

Daungsupawong, H., & Wiwanitkit, V. (2024). Artificial intelligence language model and burns first aid information: Comment. Burns : journal of the International Society for Burn Injuries, 50(6), 1710–1711. https://doi.org/10.1016/j.burns.2024.03.026

de Koning, E., van der Haas, Y., Saguna, S., Stoop, E., Bosch, J., Beeres, S., Schalij, M., & Boogers, M. (2023). AI Algorithm to Predict Acute Coronary Syndrome in Prehospital Cardiac Care: Retrospective Cohort Study. JMIR cardio, 7, e51375. https://doi.org/10.2196/51375

Deshpande, S., & Warren, J. (2021). Self-Harm Detection for Mental Health Chatbots. Studies in health technology and informatics, 281, 48–52. https://doi.org/10.3233/SHTI210118

Eysenbach G. (2023). The Role of ChatGPT, Generative Language Models, and Artificial Intelligence in Medical Education: A Conversation With ChatGPT and a Call for Papers. JMIR medical education, 9, e46885. https://doi.org/10.2196/46885

Jouni, H. (2024). On The Digital Transformation of Health Systems: what is the role of Human Resources Management and Health Informatics?. International Healthcare Review (online). https://doi.org/10.56226/81

Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLOS digital health, 2(2), e0000198. https://doi.org/10.1371/journal.pdig.0000198

Lee, S. H., Hong, W. P., Kim, J., Cho, Y., & Lee, E. (2024). Smartphone AI vs. Medical Experts: A Comparative Study in Prehospital STEMI Diagnosis. Yonsei medical journal, 65(3), 174–180. https://doi.org/10.3349/ymj.2023.0341

Mammas, C. S., & Mamma, A. S. (2024). Remote Monitoring, AI, Machine Learning and Mobile Ultrasound Integration upon 5G Internet in the Prehospital Care to Support the Golden Hour Principle and Optimize Outcomes in Severe Trauma and Emergency Surgery. Studies in health technology and informatics, 316, 1807–1811. https://doi.org/10.3233/SHTI240782

Masoumian Hosseini, M., Masoumian Hosseini, S. T., Qayumi, K., Ahmady, S., & Koohestani, H. R. (2023). The Aspects of Running Artificial Intelligence in Emergency Care; a Scoping Review. Archives of academic emergency medicine, 11(1), e38. https://doi.org/10.22037/aaem.v11i1.1974

Morley, J., Machado, C. C. V., Burr, C., Cowls, J., Joshi, I., Taddeo, M., & Floridi, L. (2020). The ethics of AI in health care: A mapping review. Social science & medicine (1982), 260, 113172. https://doi.org/10.1016/j.socscimed.2020.113172

Mueller, B., Kinoshita, T., Peebles, A., Graber, M. A., & Lee, S. (2022). Artificial intelligence and machine learning in emergency medicine: a narrative review. Acute medicine & surgery, 9(1), e740. https://doi.org/10.1002/ams2.740

Price, W. N., 2nd, Gerke, S., & Cohen, I. G. (2019). Potential Liability for Physicians Using Artificial Intelligence. JAMA, 322(18), 1765–1766. https://doi.org/10.1001/jama.2019.15064

Niu, M. (2023). On Planning and Designing General Hospitals in Smart Technology Contexts. International Healthcare Review (online). https://doi.org/10.56226/59

Sarraju, A., Bruemmer, D., Van Iterson, E., Cho, L., Rodriguez, F., & Laffin, L. (2023). Appropriateness of Cardiovascular Disease Prevention Recommendations Obtained From a Popular Online Chat-Based Artificial Intelligence Model. JAMA, 329(10), 842–844. https://doi.org/10.1001/jama.2023.1044

Semeraro F. (2024). AI-Powered clinical assessments: GPT-4o's role in standardizing CPR skill evaluations. Resuscitation, 204, 110411. https://doi.org/10.1016/j.resuscitation.2024.110411

Semeraro, F., Cascella, M., Montomoli, J., Bellini, V., & Bignami, E. G. (2025). Comparative analysis of AI tools for disseminating CPR guidelines: Implications for cardiac arrest education. Resuscitation, 208, 110528. https://doi.org/10.1016/j.resuscitation.2025.110528

Shang, X. (2024). Understanding the role of policy to bridge the digital divide of the elderly: A qualitative content analysis . International Healthcare Review (online). https://doi.org/10.56226/80

Tseng A. Health Canada paving the way for more AI/ML medical devices. 2021: https://www.smartbiggar.ca/insights/ publication/health-canada-paving-the-way-for-more-ai-ml-medical-devices. Accessed 18 March, 2025.

Vearrier, L., Derse, A. R., Basford, J. B., Larkin, G. L., & Moskop, J. C. (2022). Artificial Intelligence in Emergency Medicine: Benefits, Risks, and Recommendations. The Journal of emergency medicine, 62(4), 492–499. https://doi.org/10.1016/j.jemermed.2022.01.001

Voytovich, L., & Greenberg, C. (2022). Natural Language Processing: Practical Applications in Medicine and Investigation of Contextual Autocomplete. Acta neurochirurgica. Supplement, 134, 207–214. https://doi.org/10.1007/978-3-030-85292-4_24

Zhu, T., Huang, Y. H., Li, W., Zhang, Y. M., Lin, Y. Y., Cheng, M. Y., Wu, Z. Y., Ye, G. L., Lin, Y., & Wang, K. (2023). Multifactor artificial intelligence model assists axillary lymph node surgery in breast cancer after neoadjuvant chemotherapy: multicenter retrospective cohort study. International journal of surgery (London, England), 109(11), 3383–3394. https://doi.org/10.1097/JS9.0000000000000621

Zúñiga Salazar, G., Zúñiga, D., Vindel, C. L., Yoong, A. M., Hincapie, S., Zúñiga, A. B., Zúñiga, P., Salazar, E., & Zúñiga, B. (2023). Efficacy of AI Chats to Determine an Emergency: A Comparison Between OpenAI's ChatGPT, Google Bard, and Microsoft Bing AI Chat. Cureus, 15(9), e45473. https://doi.org/10.7759/cureus.45473

concept image chatbox healthcare

Downloads

Published

06-11-2025

How to Cite

Brito do Nascimento, J., da Silva Barreto, G., Cristina Santos de Almeida, K., Carlos Marcolino Neto, J., & Lucas Ferreira Luz da Silva, S. (2025). Chatbots and AI Systems for Pre-Hospital Care. International Healthcare Review (online). https://doi.org/10.56226/106

Issue

Online First

Section

Recent Articles

License

Copyright (c) 2025 The Publisher

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.

Most read articles by the same author(s)