Artificial Intelligence: An Interprofessional Perspective on Implications for Geriatric Mental Health Research and Care (original) (raw)
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International Journal of Research Publication and Reviews , 2023
Mental health is an important aspect of overall well-being and it has been widely recognized that Artificial intelligence (AI) technologies can play a significant role in improving mental health care. AI has made a significant impact on the healthcare industry, changing the perspectives of identifying, treating and monitoring patients. By enabling more individualized therapies and delivering more precise diagnoses, AI is significantly enhancing healthcare research and outcomes. The ability of AI in healthcare to quickly examine enormous amounts of clinical documentation aids in the identification of illness signs and trends that would otherwise go unnoticed by medical professionals. AI in the mental health field is an emerging field that uses AI techniques like machine learning, natural language processing, and other AI technologies to analyze large amounts of data in order to identify patterns, predict outcomes, and enhance the delivery of mental health care. Healthcare systems can become smarter, quicker, and more effective in providing treatment to millions of people worldwide by utilizing artificial intelligence in hospital and clinical settings.
Expectations for Artificial Intelligence (AI) in Psychiatry
Current Psychiatry Reports
Purpose of Review Artificial intelligence (AI) is often presented as a transformative technology for clinical medicine even though the current technology maturity of AI is low. The purpose of this narrative review is to describe the complex reasons for the low technology maturity and set realistic expectations for the safe, routine use of AI in clinical medicine. Recent Findings For AI to be productive in clinical medicine, many diverse factors that contribute to the low maturity level need to be addressed. These include technical problems such as data quality, dataset shift, black-box opacity, validation and regulatory challenges, and human factors such as a lack of education in AI, workflow changes, automation bias, and deskilling. There will also be new and unanticipated safety risks with the introduction of AI. Summary The solutions to these issues are complex and will take time to discover, develop, validate, and implement. However, addressing the many problems in a methodical manner will expedite the safe and beneficial use of AI to augment medical decision making in psychiatry.
Psychiatry Research, 2019
Preserving cognition and mental capacity is critical to aging with autonomy. Early detection of pathological cognitive decline facilitates the greatest impact of restorative or preventative treatments. Artificial Intelligence (AI) in healthcare is the use of computational algorithms that mimic human cognitive functions to analyze complex medical data. AI technologies like machine learning (ML) support the integration of biological, psychological, and social factors when approaching diagnosis, prognosis, and treatment of disease. This paper serves to acquaint clinicians and other stakeholders with the use, benefits, and limitations of AI for predicting, diagnosing, and classifying mild and major neurocognitive impairments, by providing a conceptual overview of this topic with emphasis on the features explored and AI techniques employed. We present studies that fell into six categories of features used for these purposes: (1) sociodemographics; (2) clinical and psychometric assessments; (3) neuroimaging and neurophysiology; (4) electronic health records and claims; (5) novel assessments (e.g., sensors for digital data); and (6) genomics/other omics. For each category we provide examples of AI approaches, including supervised and unsupervised ML, deep learning, and natural language processing. AI technology, still nascent in healthcare, has great potential to transform the way we diagnose and treat patients with neurocognitive disorders.
Artificial Intelligence Approach to Psychological Wellbeing Among the Ageing Population
IGI Global, 2024
This research examines how biological ageing presents opportunities for Artificial Intelligence (AI). It aims to analyze AI's role in understanding ageing and disease, evaluate recent AI applications in ageing research, assess the feasibility of AI integration in ageing studies, explore the growth of longevity medicine, and investigate the advantages of AI technologies. Using secondary data analysis, the study identifies research gaps and explores AI's potential benefits. Study critical analysis of ageing theory, biological age, and deep ageing clocks. The study also evaluates the feasibility and effectiveness of integrating AI in longevity medicine. The significance of the study is that it emphasizes the care support and rehabilitation services of ageing people. Ultimately, it aims to understand AI's potential in advancing ageing research and its implications for longevity medicine. AI effectively utilizes and protects health data, and improves geriatric care.
JAMIA Open, 2020
Objectives: Geriatric clinical care is a multidisciplinary assessment designed to evaluate older patients' (age 65 years and above) functional ability, physical health, and cognitive well-being. The majority of these patients suffer from multiple chronic conditions and require special attention. Recently, hospitals utilize various artificial intelligence (AI) systems to improve care for elderly patients. The purpose of this systematic literature review is to understand the current use of AI systems, particularly machine learning (ML), in geriatric clinical care for chronic diseases.. We focused on studies that used ML algorithms in the care of geriatrics patients with chronic conditions. Results: We identified 35 eligible studies and classified in three groups: psychological disorder (n ¼ 22), eye diseases (n ¼ 6), and others (n ¼ 7). This review identified the lack of standardized ML evaluation metrics and the need for data governance specific to health care applications. Conclusion: More studies and ML standardization tailored to health care applications are required to confirm whether ML could aid in improving geriatric clinical care.
Artificial intelligence in psychiatry: achievements, expectations, prospects, problems
2024
Mental disorders are a complex health problem that requires significant resources and highly qualified specialists. Artificial Intelligence (AI) offers innovative solutions to transform psychiatric care, encompassing prevention, diagnosis, therapy and research. Currently, AI algorithms demonstrate high accuracy in the diagnosis of various disorders, including schizophrenia, depression and autism, using data from electronic medical records, neuroimaging and "digital phenotypes". AI helps predict the course of diseases, the response to treatment, and risks such as suicide or aggressive behavior. Virtual assistants, chatbots and virtual reality technologies support patients by providing psychoeducation, cognitive behavioral therapy and condition monitoring. AI automates systematic literature reviews, analyzes large amounts of data, and builds clinical and psychological models, for example, for the treatment of addictive disorders. AI helps doctors in obtaining information, preparing for exams and making recommendations for patients. Promising technologies: psychovisualization (combining neuroimaging, biometric data and AI to visualize thoughts, perceptions and emotions). There are a number of problems with the introduction of AI into psychiatry: lack of high-quality data, opacity of AI models, difficulties of validation and regulation, lack of knowledge about AI among clinicians, the need to change workflows, risks of error automation , questions about data confidentiality, responsibility for decisions, algorithm bias, balance between efficiency and safety. Interdisciplinary cooperation, increasing confidence in AI systems through understanding the logic of decision-making, and training specialists to work with new technologies will become ways to overcome problems. AI has great potential to transform psychiatric care, but it requires a responsible approach and solutions to existing problems. Keywords:"artificial intelligence in psychiatry", "neural networks in psychiatry", "computer vision in psychiatry", "psychovisualization in psychiatry using machine learning", "emotion recognition using artificial intelligence", "diagnosis of mental disorders using machine learning", "personalized psychiatry using artificial intelligence".
The Egyptian Journal of Neurology, Psychiatry and Neurosurgery
Background Artificial intelligence (AI) has made significant advances in recent years, and its applications in psychiatry have gained increasing attention. The use of AI in psychiatry offers the potential to improve patient outcomes and provide valuable insights for healthcare workers. However, the potential benefits of AI in psychiatry are accompanied by several challenges and ethical implications that require consideration. In this review, we explore the use of AI in psychiatry and its applications in monitoring mental illness, treatment, prediction, diagnosis, and deep learning. We discuss the potential benefits of AI in terms of improved patient outcomes, efficiency, and cost-effectiveness. However, we also address the challenges and ethical implications associated with the use of AI in psychiatry, including issues of accuracy, privacy, and the risk of perpetuating existing biases in the field. Results This is a review article, thus not applicable. Conclusion Despite the challen...
Research Square (Research Square), 2023
Objective:To carry out systematic analysis of existing literature on role of Arti cial Intelligence in geriatric patient healthcare. Methods: A detailed online search was carried out using search phrases in reliable sources of information like Pubmed database,Embase database, Ovid database, Global Health database, PsycINFO, and Web of Science. Study speci c information was gathered, including the organisation, year of publication, nation, setting, design of the research, information about population, size of study sample, group dynamics, eligibility and exclusion requirements, information about intervention, duration of exposure to the intervention , comparators, details of outcome measures, scheduling of evaluations, and consequences. After information gathering, the reviewers gathered to discuss any differences. Results: 31 studies were nally selected for systemic review. Although there was some disagreement on the acceptance of AI-enhanced treatments in LTC settings, this review indicated that there was little consensus about the e cacy of those initiatives for older individuals. Social robots have been shown to increase social interaction and mood, but the data was more con icting and less de nitive for the other innovations and consequences. The majority of research evaluated a variety of results, which made it impossible to synthesise them in a meaningful way and prevented a meta-analysis. In addition, many studies have moderate to severe bias risks due to underpowered design Conclusion: It is challenging to determine whether AI supplemented technologies for geriatric patients are signi cantly bene cial. Although some encouraging ndings were made, more study is required. Background The ageing population around the world is dramatically rising, posing a threat to the viability of conventional approaches of healthcare that have centered on in-person surveillance. An extended lifespan frequently entails dealing with disabilities and chronic illnesses that can make it di cult for people to carry out daily tasks or operate independently. [1-3] Many nations are struggling with a distinct lack of direct care professionals like home healthcare workers, which adds to the pressure from an ageing population that needs increased levels of individualised attention, support, and care. The di culty of replacing the ageing health workforce persists. Women make up the majority of informal carers worldwide, and they frequently juggle caring for their elderly relatives while also ful lling other domestic, familial, and work obligations [4, 5]. The possible number of family carers per elderly person is also anticipated to decline signi cantly as a consequence of shifting family dynamics, shrinking family sizes, women's increasing engagement in the employment, and migration trends [6, 7]. Many senior citizens place a high importance on living independently or "ageing in place," which implies they want to remain in their current residence with the necessary support instead of enter institutional care, which is similarly in limited supply and may be out of reach for many senior citizens [8, 9]. The recent COVID-19 global epidemic, which has profoundly in uenced older individuals, particularly those already in long-term care centres (LTCs), strengthens calls for alternative approache to support people in staying as independently as possible in their homes and/or receiving continuous monitoring of health that needs the smallest amount of face-to-face contact. Systems that are using video cameras for recording people's behaviors at home as a part of remote surveillance systems, could help older adults maintain their independence. These technologies however still depend on human workers or families and carers to be observing video streams in real-time and acting in accordance with their judgements. As a result, they require a lot of labor and are vulnerable to human mistakes and diversions [11]. Will emerging computerized and ongoing innovations, like Arti cial Intelligence (AI) healthcare monitoring, improve older folks' capacity to function safely in their preferred circumstances as we face mounting human resource hurdles? There has not yet been a thorough synthesis and evaluation of the research on AI-enhanced approaches in LTC services for geriatric patients. The generalizability of outcomes and clinical consequences may be impacted by existing assessments that have concentrated on other technological solutions like environmental sensors and social robots for supporting older people, but not focused over LTC, [7, 8]. The comparisons with certain other AI-enhanced approaches that might be more approachable and less expensive is limited because prior assessments have concentrated on individual robots (like PARO) in senior care facilities. [9, 10] We conducted a thorough literature assessment on the tolerability and e cacy of AI-enhanced treatments for senior citizens receiving LTC in search of a solution to this lack of evidence.. The purpose of this systematic review is to respond to the following research queries: (1) What LTC services-related AI-enhanced treatments have been tested? (2) Which AI-enhanced activities for senior citizens undergoing LTC have been proven to be successful? (3) Which AI-enhanced initiatives have been demonstrated to be well-received by senior citizens undergoing LTC? Methods Eligibility criteria The eligibility criteria are presented as. Inclusion criteria 1. Journal papers with peer review and recognized comprehensive conference proceedings 2.Pilot investigations, viability and acceptance research, controlled (non-randomized) clinical research, pre-evaluation-and post-evaluation clinical research 3.Senior citizens with an average age of 65 years or higher
Artificial Intelligence: A Manifested Leap in Psychiatric Rehabilitation
IJPBA, 2023
The goal of psychiatric rehabilitation is to help disabled individuals develop the emotional, social, and intellectual skills needed to live, learn, and work in the community with the least amount of professional support. This study aims to identify opportunities and utilization of AI in mental healthcare and to describe key insights from implementation science of potential relevance to understanding and facilitating AI implementation in psychiatric care. Mental health professionals are using artificial intelligence (AI) to improve the accuracy of diagnosis and treatment. Our mental health system faces significant challenges such as a shortage of psychiatrists, long wait times, and stigma. The integration of AI-grounded procedures and the Internet of things is very important in the advancement of smart and intelligent paradigms. The compilation of articles in this particular edition exemplifies the promise inherent in digital therapeutics for mental health. AI can bring about a revolutionary paradigm shift in the rehabilitation of psychiatric disorders.
IEEE Reviews in Biomedical Engineering, 2019
Dementia is a chronic and degenerative condition affecting millions globally. The care of patients with dementia presents an ever continuing challenge to healthcare systems in the 21st century. Medical and health sciences have generated unprecedented volumes of data related to health and wellbeing for patients with dementia due to advances in information technology, such as genetics, neuroimaging, cognitive assessment, free texts, routine electronic health records etc. Making the best use of these diverse and strategic resources will lead to high quality care of patients with dementia. As such, machine learning becomes a crucial factor in achieving this objective. The aim of this paper is to provide a state-of-the-art review of machine learning methods applied to health informatics for dementia care. We collate and review the existing scientific methodologies and identify the relevant issues and challenges when faced with big health data. Machine learning has demonstrated promising applications to neuroimaging data analysis for dementia care, while relatively less efforts have been made to make use of integrated heterogeneous data via advanced machine learning approaches. We further indicate the future potentials and research directions of applying advanced machine learning, such as deep learning, to dementia informatics.