| خلاصه مقاله | Poisoning, whether accidental or intentional, remains a significant global health
concern. Traditional methods for diagnosing and managing poisonings often rely
on clinical evaluation, laboratory tests, and access to poison databases, which can
be time-consuming and may not always be readily available. The complexity of
toxicological analysis and the vast array of possible toxins create a scenario where
Artificial Intelligence (AI) can offer considerable assistanceThe application of AI in the
treatment of poisonings is being explored through several avenues. These include:
Machine Learning for Toxicosurveillance, AI-Powered Diagnostic Tools, Data-driven
research.Research into AI’s application in toxicology has yielded significant findings:
Improved Diagnosis: Machine learning models have demonstrated an increase in the
accuracy and speed of identifying the causative agent in poisoning incidents. Some
models have shown >90% accuracy in classifying the ingested substances based
on clinical signs and lab results. Reduced Time to Treatment: Automated systems,
powered by AI, reduce the time required to start the treatment, ultimately
improving patient outcomes.Artificial intelligence is proving to be a valuable tool in the
diagnosis and treatment of poisonings. Machine learning and other AI techniques
offer promising solutions for rapid identification of toxins, predicting severity, and
optimizing patient care. The integration of AI tools into poison control centers and
healthcare systems has the potential to improve patient outcomes by reducing the time
to treatment and increasing the accuracy of diagnoses |