Authors

Y Mohan Das

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

S Waheeda Anjum

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

P Karishma

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

G Shalini

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

K Sainath Reddy

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

T M Shahid Raza

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

G M Khalandar

Department of Computer Science and Engineering (Data Science), Gates Institute of Technology, Gooty, Andhra Pradesh, India

Abstract

The integration of artificial intelligence (AI) and advanced human-machine interfaces is transforming emergency medical services. This study proposes an intelligent ambulance system that leverages AI for predictive analytics, decision support, and real-time communication to enhance the efficiency, accuracy, and responsiveness of ambulance operations. Traditional ambulance services often suffer from manual processes, limited patient data, and delayed communication. By incorporating AI algorithms and technologies such as natural language processing, augmented reality, and automated data analysis, the proposed system streamlines emergency response, improves patient assessment, and facilitates seamless coordination between paramedics and hospitals. This innovation aims to optimize critical care delivery, reduce response times, and ultimately improve patient outcomes in emergency situations.

Keywords

Artificial Intelligence (AI) Intelligent Ambulance Emergency Medical Services Human-Machine Interface Predictive Analytics Decision Support System Real-Time Communication Healthcare Technology Patient Care Automated Response

Citation of this Article

Y Mohan Das, S Waheeda Anjum, P Karishma, G Shalini, K Sainath Reddy, T M Shahid Raza, & G M Khalandar. (2025). Predictive Healthcare Ambulance – AI & Human Interface Collaboration. Journal of Artificial Intelligence and Emerging Technologies. 2(4), 10-15. Article DOI: https://doi.org/10.47001/JAIET/2025.204003

Licence Copyright (c) 2026 Journal of Artificial Intelligence and Emerging Technologies. This work is licensed under a Creative Commons Attribution Non Commercial 4.0 International Licence.

References

  1. Advani R, Naess H, Kurz MW. The golden hour of acute ischemic stroke. Scand J Trauma Resusc Emerg Med. 2017;25(1):54.
  2. Heestermans T, van ’t Hof AWJ, ten Berg JM, van Werkum JW, Boersma E, Mosterd A, Stella PR, van Zoelen AB, Marcel Gosselink AT, Kochman W, et al. The golden hour of prehospital reperfusion with triple antiplatelet therapy: A subanalysis from the Ongoing Tirofiban in Myocardial Evaluation 2 (On-TIME 2) trial early initiation of triple antiplatelet therapy. Am Heart J. 2010;160(6):1079–1084.
  3. Al-Dury N, Ravn-Fischer A, Hollenberg J, Israelsson J, Nordberg P, Strömsöe A, Axelsson C, Herlitz J, Rawshani A. Identifying the relative importance of predictors of survival in out of hospital cardiac arrest: A machine learning study. Scand J Trauma Resusc Emerg Med. 2020;28(1):60.
  4. Carter GM, Chaiken JM, Ignall E. Response areas for two emergency units. Oper Res. 1972;20(3):571–594.
  5. Jagtenberg CJ, Bhulai S, van der Mei RD. Dynami3c ambulance dispatching: Is the closest-idle policy always optimal? Health Care Manag Sci. 2017;20(4):517–531.
  6. Haghani A, Tian Q, Hu H. Simulation model for real-time emergency vehicle dispatching and routing. Transp Res Rec. 2004;1882(1):176–183.
  7. Bandara D, Mayorga ME, McLay LA. Priority dispatching strategies for EMS systems. J Oper Res Soc. 2014;65(4):572–587.
  8. Jagtenberg CJ, van den Berg PL, van der Mei RD. Benchmarking online dispatch algorithms for Emergency Medical Services. Eur J Oper Res. 2017;258(2):715–725.
  9. Leeies M, Ffrench C, Strome T, Weldon E, Bullard M, Grierson R. Prehospital application of the Canadian triage and acuity scale by emergency medical services. CJEM. 2017;19(1):26–31.
  10. Medical Priority Dispatch System. IAED. [accessed 8 Feb 2021]. https://www.emergencydispatch.org/what-we-Feb 2021]. https://www.emergencydispatch.org/what-we-do/emergency-priority-dispatch-system/medical- protocol
  11. Sporer KA, Johnson NJ, Yeh CC, Youngblood GM. Can emergency medical dispatch codes predict prehospital interventions for common 9-1-1 call types? Prehosp Emerg Care. 2008;12(4):470–478.
  12. Criteria Based Dispatch—Emergency Medical Dispatch Guidelines. Published online July 2010; http://www.emsonline. net/assets/criteriabaseddispatchguidelines-rev2010.pdf.
  13. Andersen MS, Johnsen SP, Sørensen JN, Jepsen SB, Hansen JB, Christensen EF. Implementing a nationwide criteria-based emergency medical dispatch system: A register- based followup study. Scand J Trauma Resusc Emerg Med. 2013;21:53.
  14. Dami F, Golay C, Pasquier M, Fuchs V, Carron PN, Hugli O. Prehospital triage accuracy in a criteria based dispatch centre. BMC Emerg Med. 2015;15:32.
  15. Bohm K, Kurland L. The accuracy of medical dispatch—A systematic review. Scand J Trauma Resusc Emerg Med. 2018;26:94.
  16. Lidal IB, Holte HH, Vist GE. Triage systems for pre-hospital emergency medical services— A systematic review. Scand J Trauma Resusc Emerg Med. 2013;21:28.
  17. Fong RY, Glen WSS, Mohamed Jamil AK, Tam WWS, Kowitlawakul Y. Comparison of the Emergency Severity Index versus the Patient Acuity Category Scale in an emergency setting. Int Emerg Nurs. 2018;41:13–18.
  18. Blomberg SN, Folke F, Ersbøll AK, Christensen HC, Torp-Pedersen C, Sayre MR, Counts CR, Lippert FK. Machine learning as a supportive tool to recognize cardiac arrest in emergency calls. Resuscitation. 2019;138:322–329.
  19. Blomberg SN, Christensen HC, Lippert F, Ersbøll AK, Torp-Petersen C, Sayre MR, Kudenchuk PJ, Folke F. Effect of machine learning on dispatcher recognition of out-of- hospital cardiac arrest during calls to emergency medical services: A randomized clinical trial. JAMA Netw Open. 2021;4(1):e2032320.
  20. Tollinton L, Metcalf AM, Velupillai S. Enhancing predictions of patient conveyance using emergency call handler free text notes for unconscious and fainting incidents reported to the London Ambulance Service. Int J Med Inform. 2020;141:104179.
  21. Snoek J, Larochelle H, Adams RP. Practical Bayesian optimization of machine learning algorithms. arXiv. 2012. https://doi.org/10.48550/arXiv.1206.2944.
  22. Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81–106.
  23. Chen T, Guestrin C. XGBoost: A scalable tree boosting system. Paper presented at: KDD 2016. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining; 2016 Aug 13–17; San Francisco, CA.
  24. Breiman L. Random forests. Mach Learn. 2001;45(1):5–32.
  25. Prieto JT, Scott K, McEwen D, Podewils LJ, al-Tayyib A, Robinson J, Edwards D, Foldy S, Shlay JC, Davidson AJ. The detection of opioid misuse and heroin use from paramedic response documentation: Machine learning for improved surveillance. J Med Internet Res. 2020;22(1):e15645. https://doi. org/10.2196/15645.