AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNING
An effective emergency and disaster response plan is critical to healthcare management, ensuring timely intervention and mitigating adverse consequences. Integrating Artificial Intelligence (AI) and Machine Learning (ML) into planning, resource allocation, disaster simulation, real-time decision-mak...
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| Format: | Article |
| Language: | English |
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University of Kragujevac
2025-06-01
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| Series: | Proceedings on Engineering Sciences |
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| Online Access: | https://pesjournal.net/journal/v7-n2/60.pdf |
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| author | Manu Sudhi Aishwarya T.R Dasharathraj K Shetty Jayaraj Mymbilly Balakrishnan Sultan Ahmad Priya Pattath Sankaran |
| author_facet | Manu Sudhi Aishwarya T.R Dasharathraj K Shetty Jayaraj Mymbilly Balakrishnan Sultan Ahmad Priya Pattath Sankaran |
| author_sort | Manu Sudhi |
| collection | DOAJ |
| description | An effective emergency and disaster response plan is critical to healthcare management, ensuring timely intervention and mitigating adverse consequences. Integrating Artificial Intelligence (AI) and Machine Learning (ML) into planning, resource allocation, disaster simulation, real-time decision-making, communication, coordination, and ethical considerations presents significant opportunities for enhancing healthcare emergency preparedness. This review examines five key research questions regarding the current state of AI/ML applications in healthcare emergencies and disaster response. A systematic literature review was conducted to identify relevant studies and insights for improving emergency healthcare responses through AI/ML advancements. Findings suggest that AI/ML optimizes resource allocation by leveraging predictive analytics and logistics management, ensuring efficient medical supplies and personnel distribution during crises. AI-driven simulation models enhance preparedness by analyzing various disaster scenarios, aiding in strategic decision-making. Additionally, AI improves real-time information sharing and operational coordination among emergency response teams, facilitating more efficient crisis management. However, ethical concerns surrounding AI implementation, particularly regarding privacy, accountability, and bias, must be carefully addressed. This study underscores the transformative potential of AI/ML in emergency and disaster response, highlighting its benefits and challenges. While AI integration promises improved operational efficiency and enhanced healthcare responses, careful consideration of ethical, regulatory, and practical implications is necessary to ensure responsible deployment. |
| format | Article |
| id | doaj-art-5fd3aaf420a24cecb6b914245b7bd956 |
| institution | Kabale University |
| issn | 2620-2832 2683-4111 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | University of Kragujevac |
| record_format | Article |
| series | Proceedings on Engineering Sciences |
| spelling | doaj-art-5fd3aaf420a24cecb6b914245b7bd9562025-08-20T03:29:19ZengUniversity of KragujevacProceedings on Engineering Sciences2620-28322683-41112025-06-017 21293 130410.24874/PES07.02C.009AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNINGManu Sudhi 0https://orcid.org/0000-0003-4149-5022Aishwarya T.R 1https://orcid.org/0000-0002-0314-0549Dasharathraj K Shetty 2https://orcid.org/0000-0002-5021-4029Jayaraj Mymbilly Balakrishnan 3https://orcid.org/0000-0001-6437-8756Sultan Ahmad 4https://orcid.org/0000-0002-3198-7974Priya Pattath Sankaran 5https://orcid.org/0000-0002-7201-5733Department of Emergency Medicine, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India Department of Hospital Administration, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India Department of Emergency Medicine, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al Kharj 11942, Saudia Arabia Department of Radiodiagnosis, Kasturba Medical College, Manipal, Manipal Academy of Higher Education (MAHE), Manipal, Karnataka, India An effective emergency and disaster response plan is critical to healthcare management, ensuring timely intervention and mitigating adverse consequences. Integrating Artificial Intelligence (AI) and Machine Learning (ML) into planning, resource allocation, disaster simulation, real-time decision-making, communication, coordination, and ethical considerations presents significant opportunities for enhancing healthcare emergency preparedness. This review examines five key research questions regarding the current state of AI/ML applications in healthcare emergencies and disaster response. A systematic literature review was conducted to identify relevant studies and insights for improving emergency healthcare responses through AI/ML advancements. Findings suggest that AI/ML optimizes resource allocation by leveraging predictive analytics and logistics management, ensuring efficient medical supplies and personnel distribution during crises. AI-driven simulation models enhance preparedness by analyzing various disaster scenarios, aiding in strategic decision-making. Additionally, AI improves real-time information sharing and operational coordination among emergency response teams, facilitating more efficient crisis management. However, ethical concerns surrounding AI implementation, particularly regarding privacy, accountability, and bias, must be carefully addressed. This study underscores the transformative potential of AI/ML in emergency and disaster response, highlighting its benefits and challenges. While AI integration promises improved operational efficiency and enhanced healthcare responses, careful consideration of ethical, regulatory, and practical implications is necessary to ensure responsible deployment.https://pesjournal.net/journal/v7-n2/60.pdfartificial intelligencemachine learning; disaster responseemergency medicineresource allocationethical considerationshealthcare managementreal-time decision-making |
| spellingShingle | Manu Sudhi Aishwarya T.R Dasharathraj K Shetty Jayaraj Mymbilly Balakrishnan Sultan Ahmad Priya Pattath Sankaran AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNING Proceedings on Engineering Sciences artificial intelligence machine learning; disaster response emergency medicine resource allocation ethical considerations healthcare management real-time decision-making |
| title | AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNING |
| title_full | AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNING |
| title_fullStr | AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNING |
| title_full_unstemmed | AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNING |
| title_short | AI-DRIVEN INNOVATIONS IN EMERGENCY AND DISASTER RESPONSE: STRATEGIES FOR EFFECTIVE PLANNING |
| title_sort | ai driven innovations in emergency and disaster response strategies for effective planning |
| topic | artificial intelligence machine learning; disaster response emergency medicine resource allocation ethical considerations healthcare management real-time decision-making |
| url | https://pesjournal.net/journal/v7-n2/60.pdf |
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