Artificial Intelligence: The Future of Individualized and Effective Healthcare Approach for Psychosis

Every year, the incidence and prevalence of mental diseases rise. There has been a spike in drug usage, loneliness, and suicidal ideation because of the COVID-19 pandemic. Psychosis is a common, debilitating symptom of many psychiatric, neurodevelopmental, neurological, and medical diseases. It is a significant area for research in neurologic and psychiatric practice. Implementing artificial intelligence (AI) into the medical field has become more prevalent as healthcare providers look to maximize their efficiency in their practices and discover and develop new drugs. Not only does this save time for the physicians, but it also helps organize patients’ electronic data, such as their genomics and allergic conditions, leading to more individualization that can help reduce morbidity and mortality and improve therapeutic efficacy. Moreover, it can ease the process and aid researchers in discovering or developing novel pharmacological agents and therapeutic, diagnostic, or preventive approaches. AI can help traverse the gap between the two and interpret a multitude of simultaneous, real-time data. From subjective interpretations to reading MRI scans, AI can convert raw data into significant readings instantaneously. AI has been coupled with machine learning to develop discriminatory factors to create a learning algorithm that continuously becomes more efficient, which helps in the early detection and prevention of patients with psychosis. AI also supports the accurate differentiation of other illnesses that have symptoms similar to psychosis, such as dementia and Alzheimer’s disease. Currently, through predictive analysis of neural signatures, AI and machine learning are advancing therapy for psychosis, especially with suicidal ideation. Ultimately, the physician-patient interaction, drug discovery, and precise drug dosing will become progressively cost-effective and methodical as AI and machine learning evolve.