DefinePK

Abstract

In post pandemic healthcare management, Artificial intelligence is integrated in post pandemic commentary, mental health assessment, reduction of stigma and machine learning catalysts to diagnosis. The aim of this research is to see how AI can predict mental health conditions, to aid early diagnosis, as well as minimize the workload of the doctor by optimizing the treatment. Dataset of post pandemic mental health cases is evaluated using the four machine learning algorithms—Random forest, Support vector machine (SVM), Convolutional neural networks (CNN), and Long short term memory (LSTM). The accuracies achieved by each architecture show that CNN achieved the highest accuracy of 92.5%, while the LSTM achieved the accuracy of 89.8%, Random Forest 85.4, and SVM 81.7. The results reveal that deep learning models are superior to traditional machine learning methods in the process of mental health diagnosis. Also, the use of AI based sentience analysis and Metaverse interventions to reduce stigma decreased by 32 percent, improving patient engagement in digital therapy programs. The study also addresses to the application of AI for electronic medical records (EMRs), predictive analytics and healthcare logistics in making a cost effective, scalable and a stigma free mental healthcare ecosystem. In the future, the research should be extended to improve AI interpretability and ethical considerations as well as making the model performance and applicability wider.