A SWOT Analysis-Based FullEX Approach for Assessing Artificial Intelligence Adoption Towards Sustainable Development Goal 3
DOI:
https://doi.org/10.66972/iscis21202614Keywords:
Artificial intelligence, SWOT analysis, Good health and well-being, Sustainable development goal, SDG-2, FullEXAbstract
This study adopts a FullEX-based strategic approach to assess the strengths, weaknesses, opportunities, and threats (SWOT) associated with the role of artificial intelligence (AI) in promoting good health and well-being within the context of Sustainable Development Goal 3 (SDG 3). First, 16 SWOT factors are identified through a comprehensive review of the relevant literature. Subsequently, data are collected from three domain experts, and the FullEX approach is applied to determine the relative importance of the identified SWOT factors. The findings reveal that AI-based medical diagnosis and prognosis (S1) represents the most significant strength, while AI-driven precision medicine and vaccine development (O3) emerges as a key opportunity for supporting good health and well-being. In contrast, dependence on continuous data collection (W1) is identified as a major weakness, whereas healthcare data privacy threats (T2) represent a critical external threat. These findings demonstrate that the effective contribution of AI to SDG 3 depends not only on technological capabilities but also on addressing data-related limitations and privacy concerns. The study contributes to the decision sciences and management literature by providing a structured assessment of the strategic role of AI in achieving SDG 3. It also offers practical insights for policymakers and healthcare decision-makers seeking to leverage AI capabilities while managing the associated risks and constraints.
Downloads
References
Secinaro, S., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021). The role of artificial intelligence in healthcare: a structured literature review. BMC Medical Informatics and Decision Making, 21(1), 125. https://doi.org/10.1186/s12911-021-01488-9
Misra, S., Siakas, K., & Lampropoulos, G. (2024). Artificial intelligence of things for achieving sustainable development goals. Springer. https://doi.org/10.1007/978-3-031-53433-1
Aung, Y. Y. M., Wong, D. C. S., & Ting, D. S. W. (2021). The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare. British Medical Bulletin, 139(1), 4-15. https://doi.org/10.1093/bmb/ldab016
Bouraima, M. B. (2026). Unlocking Artificial Intelligence for Sustainable Energy Transition: A Fuzzy MCDM Assessment of Economic and Environmental Barriers. International Journal of Sustainable Development Goals, 2, 448-460. https://doi.org/10.59543/gwh54h42
Bouraima, M. B. (2026). Integrating Artificial Intelligence for Sustainable Development: A Fuzzy Decision-Making Approach. Spectrum of Operational Research, 1-13. https://doi.org/10.31181/sor202776
Bouraima, M. B. (2026). Unveiling the Challenges of Artificial Intelligence Use in Auditing: A Holistic Multi-Criteria Decision-Making Approach. Applied Research Advances, 2(1), 137-145. https://doi.org/10.65069/ara21202613
Mohammad Talebi, H. (2025). Analyzing the Strengths, Weaknesses, Opportunities, and Threats (SWOT) of Chatbots in Emergency Nursing: A Narrative Review of Literature. Creative Nursing, 31(4), 451-460. https://doi.org/10.1177/10784535251341624
Abujaber, A. A., Abd-alrazaq, A., Al-Qudimat, A. R., & Nashwan, A. J. (2023). A Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of ChatGPT Integration in Nursing Education: A Narrative Review. Cureus, 15(11), e48643. https://doi.org/10.7759/cureus.48643
Ciecierski-Holmes, T., Singh, R., Axt, M., Brenner, S., & Barteit, S. (2022). Artificial intelligence for strengthening healthcare systems in low- and middle-income countries: a systematic scoping review. npj Digital Medicine, 5(1), 162. https://doi.org/10.1038/s41746-022-00700-y
Palomares, I., Martínez-Cámara, E., Montes, R., García-Moral, P., Chiachio, M., Chiachio, J., Alonso, S., Melero, F. J., Molina, D., Fernández, B., Moral, C., Marchena, R., de Vargas, J. P., & Herrera, F. (2021). A panoramic view and swot analysis of artificial intelligence for achieving the sustainable development goals by 2030: progress and prospects. Applied Intelligence, 51(9), 6497-6527. https://doi.org/10.1007/s10489-021-02264-y
Bakary, S., Bouraima, M. B., & Badi, I. (2026). A Multi-Criteria-Decision Making Methodology to Prioritizing Telemedicine Expansion Opportunities. Journal of Contemporary Decision Science, 2(1), 55-63. https://doi.org/10.67334/cds2120267
Chumachenko, D., & Yakovlev, S. (2024). Artificial Intelligence Algorithms for Healthcare. Algorithms, 17(3), 105. https://doi.org/10.3390/a17030105
Lin, T.-H., Chung, H.-Y., Jian, M.-J., Chang, C.-K., Perng, C.-L., Liao, G.-S., Yu, J.-C., Dai, M.-S., Yu, C.-P., & Shang, H.-S. (2024). An Advanced Machine Learning Model for a Web-Based Artificial Intelligence–Based Clinical Decision Support System Application: Model Development and Validation Study. Journal of Medical Internet Research, 26, e56022. https://doi.org/10.2196/56022
Liu, S., Castillo-Olea, C., & Berkovsky, S. (2024). Emerging applications and translational challenges for AI in healthcare. Information, 15(2), 90. https://doi.org/10.3390/info15020090
Kim, J. Y., Hasan, A., Kellogg, K. C., Ratliff, W., Murray, S. G., Suresh, H., Valladares, A., Shaw, K., Tobey, D., Vidal, D. E., Lifson, M. A., Patel, M., Raji, I. D., Gao, M., Knechtle, W., Tang, L., Balu, S., & Sendak, M. P. (2024). Development and preliminary testing of Health Equity Across the AI Lifecycle (HEAAL): A framework for healthcare delivery organizations to mitigate the risk of AI solutions worsening health inequities. PLOS Digital Health, 3(5), e0000390. https://doi.org/10.1371/journal.pdig.0000390
Balagopalan, A., Baldini, I., Celi, L. A., Gichoya, J., McCoy, L. G., Naumann, T., Shalit, U., van der Schaar, M., & Wagstaff, K. L. (2024). Machine learning for healthcare that matters: Reorienting from technical novelty to equitable impact. PLOS Digital Health, 3(4), e0000474. https://doi.org/10.1371/journal.pdig.0000474
Reddy, S. (2024). Generative AI in healthcare: an implementation science informed translational path on application, integration and governance. Implementation Science, 19(1), 27. https://doi.org/10.1186/s13012-024-01357-9
Rony, M. K. K., Akter, K., Debnath, M., Rahman, M. M., Johra, F. T., Akter, F., Chandra Das, D., Mondal, S., Mousumi Das, Uddin, M. J., & Rina Parvin, M. (2024). Strengths, weaknesses, opportunities and threats (SWOT) analysis of artificial intelligence adoption in nursing care. Journal of Medicine, Surgery, and Public Health, 3, 100113. https://doi.org/10.1016/j.glmedi.2024.100113
Alves, M., Seringa, J., Silvestre, T., & Magalhães, T. (2024). Use of Artificial Intelligence tools in supporting decision-making in hospital management. BMC Health Services Research, 24(1), 1282. https://doi.org/10.1186/s12913-024-11602-y
Pinsky, M. R., Bedoya, A., Bihorac, A., Celi, L., Churpek, M., Economou-Zavlanos, N. J., Elbers, P., Saria, S., Liu, V., Lyons, P. G., Shickel, B., Toral, P., Tscholl, D., & Clermont, G. (2024). Use of artificial intelligence in critical care: opportunities and obstacles. Critical Care, 28(1), 113. https://doi.org/10.1186/s13054-024-04860-z
Elgin, C. Y., & Elgin, C. (2024). Ethical implications of AI-driven clinical decision support systems on healthcare resource allocation: a qualitative study of healthcare professionals’ perspectives. BMC Medical Ethics, 25(1), 148. https://doi.org/10.1186/s12910-024-01151-8
Hennrich, J., Ritz, E., Hofmann, P., & Urbach, N. (2024). Capturing artificial intelligence applications’ value proposition in healthcare – a qualitative research study. BMC Health Services Research, 24(1), 420. https://doi.org/10.1186/s12913-024-10894-4
Pagallo, U., O’Sullivan, S., Nevejans, N., Holzinger, A., Friebe, M., Jeanquartier, F., Jean-Quartier, C., & Miernik, A. (2024). The underuse of AI in the health sector: Opportunity costs, success stories, risks and recommendations. Health and Technology, 14(1), 1-14. https://doi.org/10.1007/s12553-023-00806-7
Bošković, S., Švadlenka, L., Jovčić, S., Dobrodolac, M., Simic, V., & Bacanin, N. (2023). A New FullEX Decision-Making Technique for Criteria Importance Assessment: An Application to the Sustainable Last-Mile Delivery Courier Selection. IEEE Access, 11, 137426-137436. https://doi.org/10.1109/access.2023.3339580
Jarika, S. K., Choudhury, S., Biswas, S., Biswas, B., & Chatterjee, P. (2025). A Multi-Criteria Decision Analysis Framework to Explore Determinants of Catastrophic Healthcare Expenses. Societies, 15(12), 353. https://doi.org/10.3390/soc15120353
Ninović, M., Dobrodolac, M., Bošković, S., Dupljanin, O., Lazarević, D., & Dumnić, S. (2025). An Extended FullEX Method: An Application to the Selection of Online Orders Distribution Modes Based on the Shared Economy. Journal of Theoretical and Applied Electronic Commerce Research, 20(3), 207. https://doi.org/10.3390/jtaer20030207
Bošković, S., Švadlenka, L., Jovčić, S., Simic, V., Dobrodolac, M., & Elomiya, A. (2024). Sustainable propulsion technology selection in penultimate mile delivery using the FullEX-AROMAN method. Socio-Economic Planning Sciences, 95, 102013. https://doi.org/10.1016/j.seps.2024.102013
Bouraima, M. B. (2026). Fermatean Fuzzy Decision-Analytics-Based Assessment of Policy Mechanisms for Promoting Electric Mobility. Spectrum of Mechanical Engineering and Operational Research, 1-15. https://doi.org/10.31181/smeor41202756
Seyfi-Shishavan, S. A., Gündoğdu, F. K., & Farrokhizadeh, E. (2021). An assessment of the banking industry performance based on Intuitionistic fuzzy Best-Worst Method and fuzzy inference system. Applied Soft Computing, 113, 107990. https://doi.org/10.1016/j.asoc.2021.107990
Donyatalab, Y., Seyfi-Shishavan, S. A., Farrokhizadeh, E., Kutlu Gündoğdu, F., & Kahraman, C. (2020). Spherical Fuzzy Linear Assignment Method for Multiple Criteria Group Decision-Making Problems. Informatica, 707-722. https://doi.org/10.15388/20-infor433
Aydın, S., & Kutlu Gündoğdu, F. (2020). Interval-valued spherical fuzzy MULTIMOORA method and its application to industry 4.0. In Decision Making with Spherical Fuzzy Sets: Theory and Applications (pp. 295-322). Springer. https://doi.org/10.1007/978-3-030-45461-6_13
Badi, I., & Bouraima, M. B. (2026). Fuzzy SWARA and Its Application to Prioritize the Artificial Intelligence-Based SWOT Factors for Achieving SDG 2 (Zero Hunger). Journal of Intelligent Decision Making and Granular Computing, 2(1), 186-196. https://doi.org/10.31181/jidmgc21202641
Algazel, E., & Badi, I. (2026). A Systematic Review of integrated GIS and MCDM methodology for Wind Farm Site Selection: Trends, Methods, and Criteria. Spectrum of Operational Research, 1-19. https://doi.org/10.31181/sor41202761
Kousar, S., Ansar, A., Kausar, N., & Freen, G. (2025). Multi-Criteria Decision-Making for Smog Mitigation: A Comprehensive Analysis of Health, Economic, and Ecological Impacts. Spectrum of Decision Making and Applications, 2(1), 53-67. https://doi.org/10.31181/sdmap2120258
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Mouhamed Bayane Bouraima (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
All site content, except where otherwise noted, is licensed under the 