Interventions of Bioelectronics and Biosensors on Oncology, Infectious and Neurological Diseases

Abstract

With mental health being an exponential issue in the world, mental health care has begun to utilize more technological advancements within their work. With AI techniques becoming more normalized within society, the mental health field has started incorporating Machine Learning (ML) systems and Deep Learning (DL)  models into their relevant professional interventions. Psychiatry specialists have begun to use ML and DL techniques for detecting psychological disorders, providing personalized mental health support, raising the effiency rate for clinical applications, and preventing additional diseases. With all the assistance gained from AI use, many limitations have begun to emerge, specifically related to the data privacy of customers, with the information derived from platforms being misabused by scientists and technologists. All of these following components will be discussed in depth within this publication, by analyzing the efficacy of AI platforms, optimal diagnostic tools, in conjunction with the rise of AI-based platforms that can help identify optimal solutions to patients diagnosed with neurodegenerative diseases. Ultimately, this research publication aims to shed light on various ethical contradictions, legal frameworks and psychiatric interventions that can enable researchers to better expose the traction of Machine Learning tools for future researchers to adopt.