Smart technology framework for medical waste optimization by integrating wireless tracking with artificial intelligence classification

i

This article has been updated. A Correction to this article was published on .

i

The Original Article was published on

i

The Original Article was published on

i

This article has been updated

i

This article was retracted on

i

This article has been updated

Abstract

In this study, we developed and validated an integrated Radio Frequency Identification- Artificial Intelligence (RFID-AI) framework to optimize medical waste management in resource- constrained healthcare settings. The system combines: (1) UHF RFID-enabled smart bins with real- time mass and environmental monitoring, (2) a fine-tuned ResNet-50 computer vision model achieving 93.1% (±2.1%) waste classification accuracy, and (3) genetic algorithm-based route optimization reducing collection distances by 18%. Implemented across four Jordanian hospitals (92-203 beds) for six months, the system demonstrated significant improvements across key metrics: Operational efficiency (30.1% reduction in collection time, P<0.01; 81% fewer hazardous mixing incidents), staff safety (40.2% reduction in sharps injuries through AI monitoring), environmental impact (15.2% lower particulate emissions via optimized incineration scheduling), and cost-effectiveness (23.2% operational cost reduction with 14-month ROI). The framework's modular design successfully addressed institution-specific challenges, including 78.3% storage overcapacity at Princess Basma Hospital and 39.1% improper sharps disposal at Ibn Al-Nafis Hospital, while maintaining 90.3% compliance with WHO 2022 guidelines. Technical innovations included moisture-resistant RFID tags, maintaining 98.3% read accuracy in high-humidity environments and Arabic-language AI interfaces that reduced training time by 42%. These results provide empirical evidence for the viability of smart waste systems in LMICs, offering a replicable model that balances technological sophistication with practical implementation constraints. We established a new benchmark for intelligent medical waste management systems under resource limitations.

Keywords

RFID technology / artificial intelligence / medical waste management / waste segregation / sustainability / healthcare Efficiency

CHANGE HISTORY

A Correction to this paper has been published:
https://doi.org

The Retraction Note was updated to reflect reception of statements of disagreement from all authors.

This article has been retracted.Please see the Retraction Notice for more detail:
https://doi.org

References

[1] Akhai S, Khang A . Efficient hospital waste treatment and management through IoT and bioelectronics[M]// Revolutionizing Automated Waste Treatment Systems: IoT and Bioelectronics. IGI Global Scientific Publishing, 2024: 126-140.

[2] Alshraideh H, Abu Qdais H (2017) Stochastic modeling and optimization of medical waste collection in Northern Jordan. J Mater Cycles Waste Manag 19: 743-753. https://doi.org/10.1007/s10163-016-0474-3

[3] Bdour A, Altrabsheh B, Hadadin N, Al-Shareif M (2007) Assessment of medical wastes management practice: A case study of the northern part of Jordan. Waste Manage 27. https://doi.org/10.1016/j.wasman.2006.03.004

[4] Božić D, Šego D, Stanković R, Šafran M (2022) Logistics in healthcare: a selected review of literature from 2010 to 2022. Transport Res Procedia 64: 288-298. https://doi.org/10.1016/j.trpro.2022.09.033

[5] Hendi A, Al-Hrinat J, Al-Ansi AM, Hazaimeh M (2024) Strategies for Optimizing Medical Waste Management and Treatment Technologies in Jordanian Hospitals. Opportunities and Chall Sustain 3: 35-49. https://doi.org/10.56578/ocs030103

[6] Janik-Karpinska E, Brancaleoni R, Niemcewicz M, et al (2023) Healthcare Waste-A Serious Problem for Global Health. Healthcare 11: 242. https://doi.org/10.3390/healthcare11020242

[7] Liu G, Wang Q-A, Jiao G, et al (2023) Review of Wireless RFID Strain Sensing Technology in Structural Health Monitoring. Sensors 23: 6925. https://doi.org/10.3390/s23156925

[8] Abuelkhail A, Baroudi U, Raad M, Sheltami T (2021) Internet of things for healthcare monitoring applications based on RFID clustering scheme. Wirel Netw 27: 747-763. https://doi.org/10.1007/s11276-020-02482-1

[9] Jin S, Yang G, Li C (2022) Medical waste traceability management system based on RFID technology[C]// 2022 41st Chinese Control Conference (CCC). IEEE 5106-5110.

[10] Al Osaimi A A, Al Kadi K, Saddik B (2017) Role of radio frequency identification in improving infant safety and the extent of nursing staff acceptance of RFID at King Abdulaziz Medical City in Riyadh[C]// 2017 International Conference on Informatics, Health & Technology (ICIHT). IEEE 1-7.

[11] Ran S-C, Wang Q-A, Wang J-F, et al (2024) A Concise State-of-the-Art Review of Crack Monitoring Enabled by RFID Technology. Appl Sci 14: 3213. https://doi.org/10.3390/app14083213

[12] Wang Q, Zhang C, Ma Z, et al (2022) Towards long‐transmission‐distance and semi‐active wireless strain sensing enabled by dual‐interrogation‐mode RFID technology. Struct Control Health Monit 29. https://doi.org/10.1002/stc.3069

[13] Belhiah M, El Aboudi M, Ziti S (2024) Optimising unplanned waste collection: An IoT‐enabled system for smart cities, a case study in Tangier, Morocco. IET Smart Cities 6: 27-40. https://doi.org/10.1049/smc2.12069

[14] Ishaq A, Mohammad SJ, Bello A-AD, et al (2023) Smart waste bin monitoring using IoT for sustainable biomedical waste management. Environ Sci Pollut Res https://doi.org/10.1007/s11356-023-30240-1

[15] Sharma V, Jamwal A, Agrawal R, Pratap S (2024) A review on digital transformation in healthcare waste management: Applications, research trends and implications. Waste Manag Res. https://doi.org/10.1177/0734242X241285420

[16] Essamlali I, Nhaila H, El Khaili M (2024) Advances in machine learning and IoT for water quality monitoring: A comprehensive review. Heliyon 10: e27920. https://doi.org/10.1016/j.heliyon.2024.e27920

[17] Al-Momani H, Obaidat M, Khazaleh A, et al (2019) Review of medical waste management in Jordanian health care organisations. Brit J Healthc Manag 25: 1-8. https://doi.org/10.12968/bjhc.2019.0041

[18] Dai H-L, Zulqarnain RM, Ali S, et al (2025) Assessment of bio-medical waste disposal techniques using interval-valued pythagorean fuzzy soft Einstein aggregation information. Inf Sci 718: 122393. https://doi.org/10.1016/j.ins.2025.122393

[19] Mohamed NH, Khan S, Jagtap S (2023) Modernizing Medical Waste Management: Unleashing the Power of the Internet of Things (IoT). Sustainability 15: 9909. https://doi.org/10.3390/su15139909

[20] Min Y, Sheng C, Quddusi MR, et al (2025) Assessing the Cost of Hospital Building Materials: Effects of Temperature-Precipitation-Flood Dynamics on Landuse and Landcover. Rangel Ecol Manag 99: 1-17. https://doi.org/10.1016/j.rama.2024.11.007

[21] Zulqarnain RM, Wang H, Siddique I, et al (2025) Thermochemical Techniques for Disposal of Municipal Solid Waste Based on the Intuitionistic Fuzzy Hypersoft Evaluation Based on the Distance from the Average Solution Technique. Sustainability 17: 970. https://doi.org/10.3390/su17030970

[22] Sharma V, Jamwal A, Agrawal R, Pratap S (2024) A review on digital transformation in healthcare waste management: Applications, research trends and implications. Waste Manage Res https://doi.org/10.1177/0734242X241285420

[23] Ranjbari M, Shams Esfandabadi Z, Shevchenko T, et al (2022) Mapping healthcare waste management research: Past evolution, current challenges, and future perspectives towards a circular economy transition. J Hazard Mater 422: 126724. https://doi.org/10.1016/j.jhazmat.2021.126724

[24] Lavin A, Ahmad S (2016) Evaluating real-time anomaly detection algorithms - The numenta anomaly benchmark. In: Proceedings - 2015 IEEE 14th International Conference on Machine Learning and Applications, ICMLA 2015. Institute of Electrical and Electronics Engineers Inc., pp 38-44

[25] Hu J, Zhang Y, Liu Y, et al (2024) Optimization of household medical waste recycling logistics routes: Considering contamination risks. PLoS One 19: e0311582. https://doi.org/10.1371/journal.pone.0311582

[26] Wen L, Li X, Gao L (2020) A transfer convolutional neural network for fault diagnosis based on ResNet-50 . Neural Comput Appl 32: 6111-6124. https://doi.org/10.1007/s00521-019-04097-w

[27] Makkena KR, Natarajan K (2023) Classification Algorithms for Liver Epidemic Identification. EAI Endorsed Trans Pervasive Health Technol 9. https://doi.org/10.4108/eetpht.9.4379

[28] Chang R, Meng D (2023) Construction and effect evaluation of a hierarchical training system for nosocomial infection based on hospitals at all levels. EAI Endorsed Trans Pervasive Health Technol 9. https://doi.org/10.4108/eetpht.9.4293

[29] Abdallat R, Bdour A, Haifa AA, et al (2024) Global Journal of Environmental Science and Management Development of a sustainable, green, and solar-powered filtration system for E. coli removal and greywater treatment. Global J Environ Sci Manage 10: 435-450. https://doi.org/10.22034/gjesm.2024.02.02

[30] WHO-Jordan (2021) Biannual-report-2020-2021

[31] Mamoori HJ, Al-Majali J, Shibli DR, et al (2024) Current status of biomedical waste management practices and barriers among private Jordanian dental clinics: A cross-sectional investigation of the capital Amman. Waste Manage Res 42: 335-343. https://doi.org/10.1177/0734242X231184442

[32] Mohamed NH, Khan S, Jagtap S (2023) Modernizing Medical Waste Management: Unleashing the Power of the Internet of Things (IoT). Sustainability 15: 9909. https://doi.org/10.3390/su15139909

[33] Al Smadi B, Al Mhairat T, Moqbel S (2023) Assessment of the Pharmaceutical Waste Management System in Jordan: Regulations and System Characteristics. J Hazard Toxic Radioact Waste 27. https://doi.org/10.1061/JHTRBP.HZENG-1214

[34] (2022) Barriers to the Implementation of a Unique Identifier and a Track and Trace System in the Lebanese Pharmaceutical Sector. (c2022). LAU

[35] Kharabsheh RM, Bdour AN (2025) Impact of COVID-19 on the Yearly Concentration Reduction of Three Criteria Air Pollutants and Meteorological Parameters’ Effects on Aerosol Dispersion. Nature Environ Pollut Technol 24: 63-76. https://doi.org/10.46488/NEPT.2024.v24iS1.004

[36] Bdour A, Hejab A, Almakhadmah L, Hawwa M Global Journal of Environmental Science and Management Management strategies for the efficient energy production of brackish water desalination to ensure reliability, cost reduction, and sustainability. Global J Environ Sci Manage 2023: 1-20. https://doi.org/10.22035/gjesm.2023.01

[37] Taha T, Abdal S, Ali L, et al (2025) Artificial intelligence approach to magnetohydrodynamic flow of non-Newtonian fluids over a wedge: Thermophoresis and Brownian motion effects. Eng Sci Technol 66: 102071. https://doi.org/10.1016/j.jestch.2025.102071

[38] Dalhatu I, Aniekwe C, Bashorun A, et al (2023) From Paper Files to Web-Based Application for Data-Driven Monitoring of HIV Programs: Nigeria’s Journey to a National Data Repository for Decision-Making and Patient Care. Methods Inf Med 62: 130-139. https://doi.org/10.1055/s-0043-1768711

[39] Gulyamov, S., Khazratkulov, O., Yuldashev, J., Kan, E. (2024). Application of Computational Law and Artificial Intelligence in Electronic Automated Waste Management Systems Based on Blockchain. In: Jha, P.K., Jamwal, P., Tripathi, B., Garg, D., Sharma, H. (eds) Proceedings of the Second Congress on Control, Robotics, and Mechatronics. CRM 2024 2024. Smart Innovation, Systems and Technologies, vol 409. Springer, Singapore. https://doi.org/10.1007/978-981-97-7094-6_28

[40] Shultz T, Gabriel S, Hussein M, et al (2024) Transforming Childhood Vaccination Rates in Rural Egypt: A Case Study on Results-Based Management in Healthcare Programs, medRxiv https://doi.org/10.1101/2024.08.04.24311459

[41] Zong Z, Guan Y (2024) AI-Driven Intelligent Data Analytics and Predictive Analysis in Industry 4.0: Transforming Knowledge, Innovation, and Efficiency . J Knowl Econ https://doi.org/10.1007/s13132-024-02001-z

[42] Ramani-Chander A, Thrift A, Van Olmen J, et al (2023) Prioritising and planning scale-up research projects targeting non-communicable diseases: A mixed-method study by the Global Alliance for Chronic Diseases upscaling working group. BMJ Glob Health 8. https://doi.org/10.1136/bmjgh-2023-012804

[43] Cannone C, Hoseinpoori P, Martindale L, et al (2023) Addressing Challenges in Long-Term Strategic Energy Planning in LMICs: Learning Pathways in an Energy Planning Ecosystem. Energies (Basel) 16. https://doi.org/10.3390/en16217267

[44] Maamari O, Brandam C, Lteif R, Salameh D (2015) Health Care Waste generation rates and patterns: The case of Lebanon. Waste Manage 43: 550-554. https://doi.org/10.1016/j.wasman.2015.05.005

[45] Elawa O, abdellatif N, Galal T, Farahat E (2022) Assessment of Ambient Air Quality Level at 21 sites in cement sector, Egypt. Egypt J Chem https://doi.org/10.21608/ejchem.2022.106133.4880

[46] Abdullah N, Al-wesabi OA, Mohammed BA, et al (2022) Integrated Approach to Achieve a Sustainable Organic Waste Management System in Saudi Arabia. Foods 11: 1214. https://doi.org/10.3390/foods11091214

[47] Dalhatu I, Aniekwe C, Bashorun A, et al (2023) From Paper Files to Web-Based Application for Data-Driven Monitoring of HIV Programs: Nigeria’s Journey to a National Data Repository for Decision-Making and Patient Care. Methods Inf Med 62: 130-139. https://doi.org/10.1055/s-0043-1768711