Asia Pacific Journal of Medical Toxicology

Current Issue

By Issue

By Author

By Subject

Author Index

Keyword Index

About Journal

Aims and Scope

Editorial Board

Publication Ethics

Indexing and Abstracting

Related Links

FAQ

Peer Review Process

Journal Metrics

News

A New Window of Hope for Management and Prevention of Poisoning: The Use of Artificial Intelligence

Document Type : Review Article

Authors

1 Department of Toxicology and Pharmacology, Faculty of Pharmacy, Mazandaran University of Medical Sciences, Sari, Iran.

2 Department of Forensic Medicine, School of Medicine, Tehran University of Medical Sciences, Tehran, Iran.

Abstract

Background: Poisoning is a significant public health concern, encompassing a broad range of sudden and severe health issues resulting from the ingestion, inhalation, or contact with toxic substances. The potential for injury or fatality necessitates urgent medical attention and care. With the ongoing advancements in artificial intelligence (AI) and its increasing integration into medical and pharmaceutical domains, there is a growing interest in exploring the role of AI in the context of poisonings. This study aims to investigate the potential applications of AI in the management and treatment of poisoning.
Materials and methods: This research is a review by searching the keywords ("Artificial intelligence") [TIAB] AND (poisoning [TIAB] OR toxicity[TIAB] OR intoxication[TIAB] OR Toxin[TIAB] OR poison[TIAB]) was searched in the internet databases PubMed, Scopus, and Google Scholar search engine in 2016-2024.
Conclusion: Enhancing system management and treatment approaches is crucial in preventing accidental and intentional poisoning. This can be achieved by incorporating cutting-edge medical equipment, such as those equipped with AI. AI technology can recognize complex patterns beyond predefined rules and process large amounts of data, exceeding human capabilities. In the future, more progress in AI will likely affect various areas of healthcare, including poison prevention and treatment, to improve patient outcomes and reduce the burden of poisoning on healthcare systems.
 

Keywords

Main Subjects

References
  1. Jesslin J, Adepu R, Churi S. Assessment of prevalence and mortality incidences due to poisoning in a South Indian tertiary care teaching hospital. Indian journal of pharmaceutical sciences. 2010;72(5):587.
  2. Parekh U, Gupta S. Epidemio-toxicological profile of poisoning cases - A five years retrospective study. J Forensic Leg Med. 2019;65:124-32.
  3. Salem W, Abdulrouf P, Thomas B, Elkassem W, Abushanab D, Rahman Khan H, et al. Epidemiology, clinical characteristics, and associated cost of acute poisoning: a retrospective study. J Pharm Policy Pract. 2024;17(1):2325513.
  4. Gummin DD, Mowry JB, Beuhler MC, Spyker DA, Rivers LJ, Feldman R, et al. 2022 Annual Report of the National Poison Data System®(NPDS) from America’s Poison Centers®: 40th Annual Report. Clinical Toxicology. 2023;61(10):717-939.
  5. Alinejad S, Zamani N, Abdollahi M, Mehrpour O. A Narrative Review of Acute Adult Poisoning in Iran. Iran J Med Sci. 2017;42(4):327-46.
  6. Ghane T, Behmanesh Y, Alizadeh Ghamsari A, Amini M, Siavashian F, Yazdani-Rostam A, et al. Toxic agents responsible for acute poisonings treated at four medical settings in Iran during 2012-2013: A report from Iran's national drug and poison information center. Asia Pacific Journal of Medical Toxicology. 2016;5(1):11-4.
  7. Naghavi M. Global, regional, and national burden of suicide mortality 1990 to 2016: systematic analysis for the Global Burden of Disease Study 2016. bmj. 2019;364.
  8. Burwinkel H, Keicher M, Bani-Harouni D, Zellner T, Eyer F, Navab N, et al., editors. Decision support for intoxication prediction using graph convolutional networks. Medical Image Computing and Computer Assisted Intervention–MICCAI 2020: 23rd International Conference, Lima, Peru, October 4–8, 2020, Proceedings, Part II 23; 2020: Springer.
  9. Fortuna LR. Disrupting Pathways to Self-Harm in Adolescence: Machine Learning as an Opportunity. Journal of the American Academy of Child and Adolescent Psychiatry. 2021;60(12):1459-60.
  10. Tan YL, Ho HK. Hypothermia advocates functional mitochondria and alleviates oxidative stress to combat acetaminophen-induced hepatotoxicity. Cells. 2020;9(11):2354.
  11. Khoshmohabat H, Bizari D, Mehrvarz S, Soleymanitabar A. Application and Capabilities of Artificial Intelligence in the Management of Traumatic Patients. Journal of Military Medicine. 2023;25(1):1675-80.
  12. Hidaka N, Kaji Y, Takatori S, Tanaka A, Matsuoka I, Tanaka M. Risk factors for acetaminophen-induced liver injury: a single-center study from Japan. Clinical Therapeutics. 2020;42(4):704-10.
  13. Nikvarz M, Faramarzpour M, Vazirinasab H, Mozaffari N. The frequency of causes of poisoning in children referred to Imam Khomeini hospital of Jiroft in 2015. Journal of Jiroft University of Medical Sciences. 2017;3(2):55-64.
  14. Chary MA, Manini AF, Boyer EW, Burns M. The role and promise of artificial intelligence in medical toxicology. Journal of Medical Toxicology. 2020;16(4):458-64.
  15. Jiao Z, Ji H, Yan J, Qi X. Application of big data and artificial intelligence in epidemic surveillance and containment. Intelligent medicine. 2023;3(1):36-43.
  16. Kamel Boulos MN, Peng G, VoPham T. An overview of GeoAI applications in health and healthcare. International journal of health geographics. 2019;18(1):7.
  17. Kung TH, Cheatham M, Medenilla A, Sillos C, De Leon L, Elepaño C, et al. Performance of ChatGPT on USMLE: potential for AI-assisted medical education using large language models. PLoS digital health. 2023;2(2):e0000198.
  18. Liu Q, Yang L, Peng Q. Artificial intelligence technology-based medical information processing and emergency first aid nursing management. Computational and mathematical methods in medicine. 2022;2022.
  19. Chandna A, Fisher AC, Cunningham I, Stone D, Mitchell M. Pattern recognition of vertical strabismus using an artificial neural network (StrabNet©). Strabismus. 2009;17(4):131-8.
  20. Shen Y, Tang X, Lin S, Jin X, Ding J, Shao M. Automatic dose prediction using deep learning and plan optimization with finite-element control for intensity modulated radiation therapy. Med Phys. 2024;51(1):545-55.
  21. Chan MJ, Hu CC, Huang WH, Hsu CW, Yen TH, Weng CH. An artificial intelligence algorithm for analyzing globus pallidus necrosis after carbon monoxide intoxication. Hum Exp Toxicol. 2023;42:9603271231190906.
  22. Sabry Abdel-Messih M, Kamel Boulos MN. ChatGPT in Clinical Toxicology. JMIR Med Educ. 2023;9:e46876.
  23. Zellner T, Romanek K, Rabe C, Schmoll S, Geith S, Heier EC, et al. ToxNet: an artificial intelligence designed for decision support for toxin prediction. Clin Toxicol (Phila). 2023;61(1):56-63.
  24. Setiya A, Jani V, Sonavane U, Joshi R. MolToxPred: small molecule toxicity prediction using machine learning approach. RSC Adv. 2024;14(6):4201-20.
  25. Mohtarami SA, Mostafazadeh B, Shadnia S, Rahimi M, Evini PET, Ramezani M, et al. Prediction of naloxone dose in opioids toxicity based on machine learning techniques (artificial intelligence). Daru. 2024.
  26. Gilson A, Safranek CW, Huang T, Socrates V, Chi L, Taylor RA, et al. How Does ChatGPT Perform on the United States Medical Licensing Examination (USMLE)? The Implications of Large Language Models for Medical Education and Knowledge Assessment. JMIR Medical Education. 2023;9(1):e45312.
  27. Tabakova-Komsalova V, Stoyanov S, Stoyanova-Doycheva A, Stoyanov I, Doukovska L, Dukovski A, editors. An Expert System for the Diagnosis of Livestock Poisoning. 2023 International Conference Automatics and Informatics (ICAI); 2023: IEEE.
  28. Mehrpour O, Saeedi F, Abdollahi J, Amirabadizadeh A, Goss F. The value of machine learning for prognosis prediction of diphenhydramine exposure: National analysis of 50,000 patients in the United States. J Res Med Sci. 2023;28:49.
  29. Rahimi M, Hosseini SM, Mohtarami SA, Mostafazadeh B, Evini PET, Fathy M, et al. Prediction of acute methanol poisoning prognosis using machine learning techniques. Toxicology. 2024;504:153770.
  30. Mehrpour O, Hoyte C, Nakhaee S, Megarbane B, Goss F. Using a decision tree algorithm to distinguish between repeated supra-therapeutic and acute acetaminophen exposures. BMC Med Inform Decis Mak. 2023;23(1):102.
  31. Badger J, LaRose E, Mayer J, Bashiri F, Page D, Peissig P. Machine learning for phenotyping opioid overdose events. J Biomed Inform. 2019;94:103185.
  32. Mehrpour O, Hoyte C, Al Masud A, Biswas A, Schimmel J, Nakhaee S, et al. Deep learning neural network derivation and testing to distinguish acute poisonings. Expert Opin Drug Metab Toxicol. 2023;19(6):367-80.
  33. Mehrpour O, Nakhaee S, Saeedi F, Valizade B, Lotfi E, Nawaz MH. Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System (NPDS). Environ Sci Pollut Res Int. 2023;30(20):57801-10.
  34. Niżnik Ł, Toporowska-Kaźmierak J, Jabłońska K, Głąb N, Stach S, Florek J, et al. Toxicovigilance 2.0 - modern approaches for the hazard identification and risk assessment of toxicants in human beings: A review. Toxicology. 2024;503:153755.
  35. Chen X, Yang L, Xue H, Li L, Yu Y. A Machine Learning Model Based on GRU and LSTM to Predict the Environmental Parameters in a Layer House, Taking CO(2) Concentration as an Example. Sensors (Basel). 2023;24(1).
  36. Frndak S, Yan F, Edelson M, Immergluck LC, Kordas K, Idris MY, et al. Predicting Low-Level Childhood Lead Exposure in Metro Atlanta Using Ensemble Machine Learning of High-Resolution Raster Cells. Int J Environ Res Public Health. 2023;20(5).
  37. Potash E, Ghani R, Walsh J, Jorgensen E, Lohff C, Prachand N, et al. Validation of a Machine Learning Model to Predict Childhood Lead Poisoning. JAMA Netw Open. 2020;3(9):e2012734.
  38. Sambanis A, Osiecki K, Cailas M, Quinsey L, Jacobs DE. Using Artificial Intelligence to Identify Sources and Pathways of Lead Exposure in Children. J Public Health Manag Pract. 2023;29(5):E208-e13.
  39. Chen K-H, Chen S-H. Applying an artificial intelligence model using multidimensional spatial-temporal data to predict arsenic contamination of groundwater. Process Safety and Environmental Protection. 2022;163:362-7.
  40. Kumar S, Pati J. Machine learning approach for assessment of arsenic levels using physicochemical properties of water, soil, elevation, and land cover. Environ Monit Assess. 2023;195(6):641.
  41. De Abreu Ferreira R, Zhong S, Moureaud C, Le MT, Rothstein A, Li X, et al. A Pilot, Predictive Surveillance Model in Pharmacovigilance Using Machine Learning Approaches. Adv Ther. 2024;41(6):2435-45.
  42. Miller TH, Gallidabino MD, MacRae JI, Hogstrand C, Bury NR, Barron LP, et al. Machine Learning for Environmental Toxicology: A Call for Integration and Innovation. Environ Sci Technol. 2018;52(22):12953-5.
  43. Nogué-Xarau S, Amigó-Tadin M, Ríos-Guillermo J. Can artificial intelligence help the emergency physician diagnose poisoning? Emergencias. 2024;36(2):153-6.
  44. Schür C, Gasser L, Perez-Cruz F, Schirmer K, Baity-Jesi M. A benchmark dataset for machine learning in ecotoxicology. Sci Data. 2023;10(1):718.

 

Asia Pacific Journal of Medical Toxicology
Volume 13, Issue 1 - Serial Number 1
March 2024
Pages 16-20
Files
History
  • Receive Date: 04 March 2024
  • Revise Date: 10 June 2024
  • Accept Date: 06 July 2024
  • Publish Date: 01 March 2024
Share
How to cite
Statistics