AI and the Treatment of Severe Mental Illnesses
DOI:
https://doi.org/10.63503/acset.978-81-995593-9-4.69Keywords:
Mental Health, depressive disorder, Machine Learning (ML), Natural Language Processing (NLP)Abstract
In addition, Artificial Intelligence (AI) has revolutionised approaches used in the management of mental diseases that put one's life at risk, such as schizophrenia, bipolar disorder, and major depressive disorder. Challenges associated with conventional techniques include a lack of symptom identification, subjectivity in assessing patient status, and limited access to specialised healthcare services. In that regard, AI has developed advanced predictive analytics, machine learning (ML), and natural language processing (NLP) to facilitate the deployment of SMIs by leveraging pattern recognition of behaviour, speech, and physiological parameters. AI Chatbots and virtual psychologists can monitor a person's mental wellness throughout the day without necessarily involving healthcare specialists. In addition, wearable devices worn by patients fitted with AI produce real-time signals, including heart rate variations and sleep patterns, which help pace the respondent's mental state. Recommendation engines based on artificial intelligence provide personalised plans for pharmaceutical and therapeutic interventions. With the above innovations in the hood, however, ethical concerns related to AI, such as data privacy, biases, and AI over-extrapolation, remain a concern. It is crucial to ensure transparency and regulation, as well as a human-AI partnership, for AI to thrive in mental healthcare services. This chapter explores how AI talks, listens, and helps people living with SMIs, examining the nature and limitations of AI in transforming the diagnosis and treatment of SMIs.
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[112] Gupta, S., Garg, P., Agarwal, J., Thakur, H. K., & Yadav, S. P. (Eds.). (2025). Federated learning-based intelligent systems to handle issues and challenges in IoVs (Part 2).
[113] Garg, P., Bhatt, M., Parmar, R., & Arsalan, M. (2025). Generative AI: Evolution, Applications, Challenges, and Future Prospects. Applications, Challenges, and Future Prospects (May 17, 2025).
[114] Kumar, N., Kumar, Y., Khurana, D., Kumar, S., & Garg, P. (2025, November). A Hybrid Ensemble Learning Framework for Interpretable Student Performance Prediction Using Academic and Extracurricular Factors. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 666-672). IEEE.
[115] Khurana, D., Kumar, Y., Kumar, N., Kumar, S., & Garg, P. (2025, November). Transformer-Based Movie Recommendation System with Autoencoder-Enhanced Feature Compression. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 685-690). IEEE.
[116] Garg, P. (2025, November). Comparative Analysis of Various Neural Networks for Galaxy Classification. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 697-701). IEEE.
[117] Saggu, A. K., Babbar, N., & Garg, P. (2025, November). Health-Guard AI: Integrated Health Report Management and Analysis. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 614-623). IEEE.
[118] Kumar, S., Kumar, Y., Kumar, N., Khurana, D., & Garg, P. (2025, November). Hybrid FCM-DNN Model for Uncertainty-Aware Air Quality Classification Using Multi-Pollutant Data. In 2025 International Conference on Innovations and Emerging Technologies in AI & Communication Systems (IETACS) (pp. 679-684). IEEE.
[119] Babbar, N., Singh, H. V., Bendale, S., & Garg, P. (2025, November). Stock Market Price Prediction Using Big Data Analysis: A Performance Evaluation Study. In 2025, the 3rd International Conference on Computational Intelligence and Network Systems (CINS) (pp. 1-6). IEEE.
[120] Singh, A. K., Kori, G., Garg, P., & Srivastava, G. (2025, November). Bank Churn Prediction Using Machine Learning. In 2025, IEEE 7th International Conference on Computing, Communication and Automation (ICCCA) (pp. 1-6). IEEE.
[121] Bhardwaj, A., Das, A., Garg, P., & Yadav, S. (2026). Material-Driven Performance Analysis of a Vertical Nanowire Tunnel FET for Analogue Applications. Journal of Electronic Materials, 55(1), 1099-1110.
[122] Srivastava, A. K., Shankdhar, D., Ror, R., & Garg, P. (2026). Harnessing YOLOv5 for real-time object detection: A cloud-based approach. In Recent Advances in Computational Methods in Science and Technology (pp. 441-450). CRC Press.
[123] Srivastava, A. K., Shukla, A., Gupta, H., Saxena, K., & Garg, P. (2026). Towards an intelligent attendance management system with face recognition using the LBPH algorithm. In Recent Advances in Computational Methods in Science and Technology (pp. 8-15). CRC Press.
[124] Srivastava, A. K., Garg, P., & Pandey, H. (2026). Vedcure: Towards intelligent ayurvedic drug recommendation and disease prediction. In Recent Advances in Computational Methods in Science and Technology (pp. 16-23). CRC Press.
[125] Upadhyay, D., Garg, P., & Babbar, N. (2026). Blockchain and IoT-based smart contract framework for efficient and secure product life management. Discover Internet of Things.
[126] Singh, A., Parmar, R., Bhardwaj, P., Sharma, V., & Garg, P. (2026). Fusion of Aerial Networks with Advanced Computing Paradigms. Edge Computing and Aerial Platforms, 355-367.
[127] Kumari, M., Baranwal, A., Sonal, & Garg, P. (2026). Application of Aerial Edge Computing in Disaster Management. Edge Computing and Aerial Platforms, 103-122.
[128] Aditi, Saraswat, P., Sharma, V., & Garg, P. (2026). Advances in Aerial Platforms and Edge Computing. Edge Computing and Aerial Platforms, 123-143.
[129] Garg, P., Arora, K., Bawane, R., Gupta, C., & Ahmed, K. (2025). Detection and Prevention of Cyber Attacks and Threats using AI.
[130] Ahmed, K., Ahmed, A., Khan, J., Garg, P., Seth, S., & Mallik, S. (2025). Principal Component Analysis-Based Clustering of Insecticides and Molecular Docking of Pyrethroid Insecticides.
[131] Kumar, B., Kumar, A., Nanwal, J., Garg, P., & Patnaik, P. (2025, November). Ensemble of YOLOv5 and Segment Anything Model for Brain Tumour Detection. In 2025, the 2nd International Conference on Advanced Computing and Emerging Technologies (ACET) (pp. 1-5). IEEE.
[132] Arsalan, M., Anas, M., & Garg, P. (2025). Transparent AI for Drug Discovery and Development. Available at SSRN 5844242.
[133] Singh, A., Bhardwaj, P., Garg, P., & Singh, N. (2026). Introduction to explainable artificial intelligence in healthcare. In Explainable AI in Clinical Practice (pp. 23-44). Academic Press.
[134] Kapoor, S., Singh, A., Garg, P., & Ramasamy, L. K. (2026). Explainable artificial intelligence in a diagnostic support system. In Explainable AI in Clinical Practice (pp. 131-145). Academic Press.
[135] Ahmed, K., Anas, M., & Garg, P. (2026). Case studies on unlocking the potential of Industry 4.0 for sustainable manufacturing through generative AI-driven innovations. Available at SSRN 6356958.
[136] Garg, P., & Oruganti, S. K. (2026, March). AI Assisted Routing Optimisation in Opportunistic IoT Networks using Machine Learning: A Comprehensive Review on Protocols & Simulators. In Sustainable Global Societies Initiative (Vol. 1, No. 4). Vibrasphere Technologies.
[137] Arsalan, M., Pokhrel, L., & Garg, P. (2026). Architecture, Components, and tools in Integrated AI-Augmented Intelligence: A design perspective. Components and tools in Integrated AI-Augmented Intelligence: A design perspective (March 19, 2026).
[138] Singh, H., Ahmed, K., & Garg, P. (2026). Human Versus Machine Customer Behaviour and Functional Differences. Available at SSRN 6441098.
[139] Saraswat, P., & Garg, P. (2026). Soft Computing In AI Agents.
[140] Saraswat, P., & Garg, P. (2026). Water Quality Prediction Using IOT Sensors and Deep Networks.
[141] Arsalan, M., Ahmed, K., & Garg, P. (2026). Machine learning for Anomaly detection in sensor networks. Available at SSRN 6441518.
[142] Kumari, M., & Garg, P. (2026). Hybrid Cloud Infrastructure: Models, Benefits, Security, and Challenges. Benefits, Security, and Challenges (March 19, 2026).
[143] Singh, H., & Garg, P. (2026). Demystifying Artificial Distributed Intelligence (ADI). Available at SSRN 6442698.
[144] Saraswat, P., & Garg, P. (2026). Human AI Collaboration: The Future of Clinical Decision Making.
[145] Saraswat, P., & Garg, P. (2026). Breaking Data Boundaries: Federated Learning in Digital Healthcare.
[146] Singh, A., Parmar, R., Bhardwaj, P., Sharma, V., & Garg, P. (2026). Fusion of Aerial Networks with Advanced Computing Paradigms. Edge Computing and Aerial Platforms, 355-367.
[147] Kumari, M., Baranwal, A., Sonal, & Garg, P. (2026). Application of Aerial Edge Computing in Disaster Management. Edge Computing and Aerial Platforms, 103-122.
[148] Aditi, Saraswat, P., Sharma, V., & Garg, P. (2026). Advances in Aerial Platforms and Edge Computing. Edge Computing and Aerial Platforms, 123-143.
[149] Garg, P. (2026). Zero-Trust Security Enforcement through AI-Powered Anomaly Detection in Cloud Systems. Journal of Artificial Intelligence in Governance and Public Policy (JAIGPP), 1(1), 1-8.
[150] Singh, A. P., Sharma, A., & Garg, P. (2026, January). AI-Powered Adaptive Mock Interview Generation System. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 1421-1426). IEEE.
[151] Raghav, A., Mishra, A., & Garg, P. (2026, January). Enhancing Healthcare Access: An AI-driven Chatbot for Doctor Appointment Management. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 910-914). IEEE.
[152] Kumar, B., Chauhan, D., Singh, H., Verma, H., Sahu, K., & Garg, P. (2026, January). Decoding Linear A with Artificial Intelligence: A Comprehensive Machine Learning and NLP Framework. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 1-7). IEEE.
[153] Gupta, V., Chakravarti, L., Akhtar, M. M., Maheshwari, P., Garg, P., & Tiwari, D. (2026, January). A Sentence-Level Risk Estimator for Identifying Hallucinations in Generative AI. In 2026 International Conference on AI-Driven Smart Systems and Ubiquitous Computing (ICAUC) (pp. 1619-1626). IEEE.
[154] Patnaik, P., & Garg, P. (2026). Principles of Artificial Intelligence: Cognitive Architectures, Large Language Models, and Computational Limits. Deep Science Publishing.
[155] Singh, K., & Garg, P. (2026). Trustworthy Deep Learning: Robustness, Uncertainty Quantification, and Adversarial Resilience. Deep Science Publishing.
[156] Garg, P. (2026). Survey of Load Balancing Strategies in Fog-Cloud Architectures for IoT Integration. International Journal of Research Publications in Engineering, Technology and Management (IJRPETM), 9(2), 595-604.
[157] Tiwari, D., Bhati, B. S., Garg, P., & Babbar, N. (2026). A novel lexicon dictionary and CNN-LSTM employed hybrid approach for sentiment detection of COVID-19 vaccines. Scientific Reports.
[158] Sethi, P., Kumar, B., Garg, P., & Yadav, P. (2026, February). Graph Neural Networks for Causal Inference in Climate Science: A Novel Approach to Modelling Complex Interactions. In 2026 International Conference on Intelligent Computing and Automation for Sustainable Solutions (ICASS) (pp. 1-7). IEEE.
[159] Kumar, B., Garg, P., Garg, A., Nanwal, J., & Yadav, P. (2026, February). An AI-Centric Multimodal Framework for Cognitive Productivity and Digital Wellbeing. In 2026 International Conference on Intelligent Computing and Automation for Sustainable Solutions (ICASS) (pp. 1-7). IEEE.
