Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care

Abstract

Artificial intelligence (AI) in healthcare aims to learn patterns in large multimodal datasets within and across individuals. These patterns may either improve understanding of current clinical status or predict a future outcome. AI holds the potential to revolutionize geriatric mental health care and research by supporting diagnosis, treatment, and clinical decision-making. However, much of this momentum is driven by data and computer scientists and engineers and runs the risk of being disconnected from pragmatic issues in clinical practice. This interprofessional perspective bridges the experiences of clinical scientists and data science. We provide a brief overview of AI with the main focus on possible applications and challenges of using AI-based approaches for research and clinical care in geriatric mental health. We suggest future AI applications in geriatric mental health consider pragmatic considerations of clinical practice, methodological differences between data and clinical science, and address issues of ethics, privacy, and trust.

A Quasi-experimental study to assess the effectiveness of plan teaching on Knowledge regarding Artificial Intelligence-based learning among nursing students in selected College of Wardha City: A Protocol

“…for the diagnosis and treatment of patients is more significant for better outcomes. 11 There are many articles published on planned teaching related to different medical categories. Nursing education has several challenges, including preparing students for rapidly changing healthcare.…”

Artificial intelligence and aging: potential and precautions

“…According to Renn et al, 39 artificial intelligence in healthcare aims to learn patterns in large multimodal datasets within and across individual that may either improve understanding of their current clinical status or predict a future outcome or both. In this regard, AI has the potential to not only revolutionize chronic health disease prevention and intervention, 40 but geriatric mental health care and research and other facets of health by its advanced ability to carry out diagnoses, advocate treatments, and aid clinical decision-making.…”

Improvement of the psychiatric care through outsourcing artificial intelligence technologies: Where are we now?

“…Currently there are two main types of ML techniquessupervised and unsupervised. The supervised ML firstly uses the labeled datasets to train the algorithms (the model learns on the set of training data), after which the algorithm is tested on unlabeled data to ensure its accuracy in classifying the target variable (22). On the other hand, unsupervised ML is based on analyzing unlabeled data and discovering hidden patterns in them (unlabeled data are sorted into groups or patterns to identify their underlying structure) (22).…”

“…The supervised ML firstly uses the labeled datasets to train the algorithms (the model learns on the set of training data), after which the algorithm is tested on unlabeled data to ensure its accuracy in classifying the target variable (22). On the other hand, unsupervised ML is based on analyzing unlabeled data and discovering hidden patterns in them (unlabeled data are sorted into groups or patterns to identify their underlying structure) (22). Due to the growing availability of data pertaining to an individual's mental health status nowadays, ML technologies are being increasingly applied to improve the understanding of mental health disorders and assist clinicians for improved decision making process (23).…”

“…By extracting the word features (syntax, grammar, semantic meaning) it aims to comprehend human language from transcribed speech. Given that speech and text represent the main real-world data sources in psychiatry, natural language pro-cessing holds great potential in mental health research and care (22).…”