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Park Y, Park S, Lee M. Effectiveness of artificial intelligence in detecting and managing depressive disorders: Systematic review. J Affect Disord 2024; 361:445-456. [PMID: 38889858 DOI: 10.1016/j.jad.2024.06.035] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 11/27/2023] [Accepted: 06/14/2024] [Indexed: 06/20/2024]
Abstract
OBJECTIVES This study underscores the importance of exploring AI's creative applications in treating depressive disorders to revolutionize mental health care. Through innovative integration of AI technologies, the research confirms their positive effects on preventing, diagnosing, and treating depression. The systematic review establishes an evidence base for AI in depression management, offering directions for effective interventions. METHODS This systematic literature review investigates the effectiveness of AI in depression management by analyzing studies from January 1, 2017, to May 31, 2022. Utilizing search engines like IEEE Xplore, PubMed, and Web of Science, the review focused on keywords such as Depression/Mental Health, Machine Learning/Artificial Intelligence, and Prediction/Diagnosis. The analysis of 95 documents involved classification based on use, data type, and algorithm type. RESULTS The study revealed that AI in depression management excelled in accuracy, particularly in monitoring and prediction. Biomarker-derived data demonstrated the highest accuracy, with the CNN algorithm proving most effective. The findings affirm the therapeutic benefits of AI, including treatment, detection, and disease prediction, highlighting its potential in analyzing monitored data for depression management. LIMITATIONS This study exclusively examined the application of AI in individuals with depressive disorders. Interpretation should be cautious due to the limited scope of subjects to this specific population. CONCLUSIONS To introduce digital healthcare and therapies for ongoing depression management, it's crucial to present empirical evidence on the medical fee payment system, safety, and efficacy. These findings support enhanced medical accessibility through digital healthcare, offering personalized disease management for patients seeking non-face-to-face treatment.
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Affiliation(s)
- Yoonseo Park
- Department of Convergence Healthcare Medicine, Ajou University, Suwon, South Korea.
| | - Sewon Park
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea.
| | - Munjae Lee
- Department of Medical Science, Ajou University School of Medicine, Suwon, South Korea.
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2
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Norris ML, Obeid N, El-Emam K. Examining the role of artificial intelligence to advance knowledge and address barriers to research in eating disorders. Int J Eat Disord 2024; 57:1357-1368. [PMID: 38597344 DOI: 10.1002/eat.24215] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/17/2023] [Revised: 03/22/2024] [Accepted: 03/22/2024] [Indexed: 04/11/2024]
Abstract
OBJECTIVE To provide a brief overview of artificial intelligence (AI) application within the field of eating disorders (EDs) and propose focused solutions for research. METHOD An overview and summary of AI application pertinent to EDs with focus on AI's ability to address issues relating to data sharing and pooling (and associated privacy concerns), data augmentation, as well as bias within datasets is provided. RESULTS In addition to clinical applications, AI can utilize useful tools to help combat commonly encountered challenges in ED research, including issues relating to low prevalence of specific subpopulations of patients, small overall sample sizes, and bias within datasets. DISCUSSION There is tremendous potential to embed and utilize various facets of artificial intelligence (AI) to help improve our understanding of EDs and further evaluate and investigate questions that ultimately seek to improve outcomes. Beyond the technology, issues relating to regulation of AI, establishing ethical guidelines for its application, and the trust of providers and patients are all needed for ultimate adoption and acceptance into ED practice. PUBLIC SIGNIFICANCE Artificial intelligence (AI) offers a promise of significant potential within the realm of eating disorders (EDs) and encompasses a broad set of techniques that offer utility in various facets of ED research and by extension delivery of clinical care. Beyond the technology, issues relating to regulation, establishing ethical guidelines for application, and the trust of providers and patients are needed for the ultimate adoption and acceptance of AI into ED practice.
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Affiliation(s)
- Mark L Norris
- Department of Pediatrics, Children's Hospital of Eastern Ontario (CHEO), University of Ottawa, Ottawa, Ontario, Canada
- CHEO Research Institute, Ottawa, Ontario, Canada
| | - Nicole Obeid
- CHEO Research Institute, Ottawa, Ontario, Canada
- Department of Psychiatry, University of Ottawa, Ottawa, Ontario, Canada
| | - Khaled El-Emam
- CHEO Research Institute, Ottawa, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
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3
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Cleary M, Kornhaber R, Le Lagadec D, Stanton R, Hungerford C. Artificial Intelligence in Mental Health Research: Prospects and Pitfalls. Issues Ment Health Nurs 2024:1-5. [PMID: 38683972 DOI: 10.1080/01612840.2024.2341038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 05/02/2024]
Affiliation(s)
- Michelle Cleary
- School of Nursing, Midwifery & Social Sciences, CQUniversity, Sydney, New South Wales, Australia
| | - Rachel Kornhaber
- School of Nursing, Paramedicine and Healthcare Sciences, Charles Sturt University, Bathurst, New South Wales, Australia
| | - Danielle Le Lagadec
- School of Nursing, Midwifery and Social Sciences, CQUniversity, Bundaberg, Queensland, Australia
| | - Robert Stanton
- School of Health, Medical and Applied Sciences, CQUniversity, Rockhampton, Queensland, Australia
| | - Catherine Hungerford
- School of Nursing, Midwifery & Social Sciences, CQUniversity, Sydney, New South Wales, Australia
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4
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Lee DY, Kim N, Park C, Gan S, Son SJ, Park RW, Park B. Explainable multimodal prediction of treatment-resistance in patients with depression leveraging brain morphometry and natural language processing. Psychiatry Res 2024; 334:115817. [PMID: 38430816 DOI: 10.1016/j.psychres.2024.115817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 02/19/2024] [Accepted: 02/23/2024] [Indexed: 03/05/2024]
Abstract
Although 20 % of patients with depression receiving treatment do not achieve remission, predicting treatment-resistant depression (TRD) remains challenging. In this study, we aimed to develop an explainable multimodal prediction model for TRD using structured electronic medical record data, brain morphometry, and natural language processing. In total, 247 patients with a new depressive episode were included. TRD-predictive models were developed based on the combination of following parameters: selected tabular dataset features, independent components-map weightings from brain T1-weighted magnetic resonance imaging (MRI), and topic probabilities from clinical notes. All models applied the extreme gradient boosting (XGBoost) algorithm via five-fold cross-validation. The model using all data sources showed the highest area under the receiver operating characteristic of 0.794, followed by models that used combined brain MRI and structured data, brain MRI and clinical notes, clinical notes and structured data, brain MRI only, structured data only, and clinical notes only (0.770, 0.762, 0.728, 0.703, 0.684, and 0.569, respectively). Classifications of TRD were driven by several predictors, such as previous exposure to antidepressants and antihypertensive medications, sensorimotor network, default mode network, and somatic symptoms. Our findings suggest that a combination of clinical data with neuroimaging and natural language processing variables improves the prediction of TRD.
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Affiliation(s)
- Dong Yun Lee
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; Department of Medical Sciences, Graduate School of Ajou University, Suwon, South Korea
| | - Narae Kim
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
| | - ChulHyoung Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; Department of Medical Sciences, Graduate School of Ajou University, Suwon, South Korea
| | - Sujin Gan
- Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea
| | - Sang Joon Son
- Department of Psychiatry, Ajou University School of Medicine, Suwon, South Korea
| | - Rae Woong Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; Department of Biomedical Sciences, Graduate School of Ajou University, Suwon, South Korea.
| | - Bumhee Park
- Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, South Korea; Office of Biostatistics, Medical Research Collaborating Center, Ajou Research Institute for Innovative Medicine, Ajou University Medical Center, Suwon, South Korea.
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5
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DelPozo-Banos M, Stewart R, John A. Machine learning in mental health and its relationship with epidemiological practice. Front Psychiatry 2024; 15:1347100. [PMID: 38528983 PMCID: PMC10961376 DOI: 10.3389/fpsyt.2024.1347100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2023] [Accepted: 02/22/2024] [Indexed: 03/27/2024] Open
Affiliation(s)
| | - Robert Stewart
- King’s College London, Institute of Psychiatry, Psychology and Neuroscience, London, United Kingdom
- South London and Maudsley National Health Service (NHS) Foundation Trust, London, United Kingdom
| | - Ann John
- Swansea University Medical School, Swansea, United Kingdom
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6
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Singh RK, Nayak NP, Behl T, Arora R, Anwer MK, Gulati M, Bungau SG, Brisc MC. Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences. Diagnostics (Basel) 2024; 14:139. [PMID: 38248016 PMCID: PMC11154438 DOI: 10.3390/diagnostics14020139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2023] [Revised: 01/03/2024] [Accepted: 01/05/2024] [Indexed: 01/23/2024] Open
Abstract
To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.
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Affiliation(s)
- Rahul Kumar Singh
- Energy Cluster, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India; (R.K.S.); (N.P.N.)
| | - Nirlipta Priyadarshini Nayak
- Energy Cluster, University of Petroleum and Energy Studies, Dehradun 248007, Uttarakhand, India; (R.K.S.); (N.P.N.)
| | - Tapan Behl
- Amity School of Pharmaceutical Sciences, Amity University, Mohali 140306, Punjab, India
| | - Rashmi Arora
- Chitkara College of Pharmacy, Chitkara University, Rajpura 140401, Punjab, India;
| | - Md. Khalid Anwer
- Department of Pharmaceutics, College of Pharmacy, Prince Sattam Bin Abdulaziz University, Alkharj 11942, Saudi Arabia;
| | - Monica Gulati
- School of Pharmaceutical Sciences, Lovely Professional University, Phagwara 1444411, Punjab, India;
- Australian Research Centre in Complementary and Integrative Medicine, Faculty of Health, University of Technology Sydney, Ultimo, NSW 20227, Australia
| | - Simona Gabriela Bungau
- Department of Pharmacy, Faculty of Medicine and Pharmacy, University of Oradea, 410028 Oradea, Romania
- Doctoral School of Biological and Biomedical Sciences, University of Oradea, 410087 Oradea, Romania
| | - Mihaela Cristina Brisc
- Department of Medical Disciplines, Faculty of Medicine and Pharmacy, University of Oradea, 410073 Oradea, Romania;
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7
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Grzenda A, Widge AS. Electronic health records and stratified psychiatry: bridge to precision treatment? Neuropsychopharmacology 2024; 49:285-290. [PMID: 37667021 PMCID: PMC10700348 DOI: 10.1038/s41386-023-01724-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/11/2023] [Revised: 08/24/2023] [Accepted: 08/27/2023] [Indexed: 09/06/2023]
Abstract
The use of a stratified psychiatry approach that combines electronic health records (EHR) data with machine learning (ML) is one potentially fruitful path toward rapidly improving precision treatment in clinical practice. This strategy, however, requires confronting pervasive methodological flaws as well as deficiencies in transparency and reporting in the current conduct of ML-based studies for treatment prediction. EHR data shares many of the same data quality issues as other types of data used in ML prediction, plus some unique challenges. To fully leverage EHR data's power for patient stratification, increased attention to data quality and collection of patient-reported outcome data is needed.
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Affiliation(s)
- Adrienne Grzenda
- Department of Psychiatry & Biobehavioral Sciences, David Geffen School of Medicine, University of California-Los Angeles, Los Angeles, CA, USA.
- Olive View-UCLA Medical Center, Sylmar, CA, USA.
| | - Alik S Widge
- Department of Psychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, MN, USA
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8
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Ettman CK, Galea S. The Potential Influence of AI on Population Mental Health. JMIR Ment Health 2023; 10:e49936. [PMID: 37971803 PMCID: PMC10690520 DOI: 10.2196/49936] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/14/2023] [Revised: 10/09/2023] [Accepted: 10/29/2023] [Indexed: 11/19/2023] Open
Abstract
The integration of artificial intelligence (AI) into everyday life has galvanized a global conversation on the possibilities and perils of AI on human health. In particular, there is a growing need to anticipate and address the potential impact of widely accessible, enhanced, and conversational AI on mental health. We propose 3 considerations to frame how AI may influence population mental health: through the advancement of mental health care; by altering social and economic contexts; and through the policies that shape the adoption, use, and potential abuse of AI-enhanced tools.
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Affiliation(s)
- Catherine K Ettman
- Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, United States
| | - Sandro Galea
- Office of the Dean, Boston University School of Public Health, Boston, MA, United States
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9
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Wilhelmy S, Giupponi G, Groß D, Eisendle K, Conca A. A shift in psychiatry through AI? Ethical challenges. Ann Gen Psychiatry 2023; 22:43. [PMID: 37919759 PMCID: PMC10623776 DOI: 10.1186/s12991-023-00476-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Accepted: 10/24/2023] [Indexed: 11/04/2023] Open
Abstract
The digital transformation has made its way into many areas of society, including medicine. While AI-based systems are widespread in medical disciplines, their use in psychiatry is progressing more slowly. However, they promise to revolutionize psychiatric practice in terms of prevention options, diagnostics, or even therapy. Psychiatry is in the midst of this digital transformation, so the question is no longer "whether" to use technology, but "how" we can use it to achieve goals of progress or improvement. The aim of this article is to argue that this revolution brings not only new opportunities but also new ethical challenges for psychiatry, especially with regard to safety, responsibility, autonomy, or transparency. As an example, the relationship between doctor and patient in psychiatry will be addressed, in which digitization is also leading to ethically relevant changes. Ethical reflection on the use of AI systems offers the opportunity to accompany these changes carefully in order to take advantage of the benefits that this change brings. The focus should therefore always be on balancing what is technically possible with what is ethically necessary.
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Affiliation(s)
- Saskia Wilhelmy
- Institute for History, Theory and Ethics in Medicine, University Hospital, RWTH Aachen University, Wendlingweg 2, 5074, Aachen, Germany.
| | - Giancarlo Giupponi
- Academic Teaching Department of Psychiatry, Central Hospital, Sanitary Agency of South Tyrol, Via Lorenz Böhler 5, 39100, Bolzano, Italy
| | - Dominik Groß
- Institute for History, Theory and Ethics in Medicine, University Hospital, RWTH Aachen University, Wendlingweg 2, 5074, Aachen, Germany
| | - Klaus Eisendle
- Institute of General Practice and Public Health, Provincial College for Health Professions Claudiana, Lorenz-Böhler-Straße 13, 39100, Bolzano, Italy
| | - Andreas Conca
- Academic Teaching Department of Psychiatry, Central Hospital, Sanitary Agency of South Tyrol, Via Lorenz Böhler 5, 39100, Bolzano, Italy
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10
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Bhugra D, Smith A, Ventriglio A, Hermans MHM, Ng R, Javed A, Chumakov E, Kar A, Ruiz R, Oquendo M, Chisolm MS, Werneke U, Suryadevara U, Jibson M, Hobbs J, Castaldelli-Maia J, Nair M, Seshadri S, Subramanyam A, Patil N, Chandra P, Liebrenz M. World Psychiatric Association-Asian Journal of Psychiatry Commission on Psychiatric Education in the 21st century. Asian J Psychiatr 2023; 88:103739. [PMID: 37619422 DOI: 10.1016/j.ajp.2023.103739] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Accepted: 08/10/2023] [Indexed: 08/26/2023]
Abstract
Psychiatric practice faces many challenges in the first quarter of 21st century. Society has transformed, as have training requirements and patient expectations, underlining an urgent need to look at educational programmes. Meanwhile, awareness has grown around psychiatric disorders and there are evolving workforce trends, with more women going to medical school and specialising in psychiatry. Trainee psychiatrists carry different expectations for work-life balance and are increasingly becoming conscious of their own mental health. A tendency to see health as a commodity and the litigious nature of society has elicited additional pressures for healthcare professionals. Cartesian mind-body dualism has created further complexity and this can often be frustrating for patients and care-partners alike. In many cultures across Asia and beyond, patients can present with physical symptoms to express underlying psychological distress with increasing physical investigations. Simultaneously, in various countries, a shift from asylums to community-based interventions and then home treatments have changed psychiatric care in remarkable ways. These changes have added to pressures faced by mental healthcare professionals. However, trainees and other mental healthcare professionals continue to receive similar training as they did a generation ago. The tensions and differences in ideology/orientation between different branches of psychiatry have made responses to patient needs challenging. Recognising that it is difficult to predict the future, this World Psychiatric Association-Asian Journal of Psychiatry Commission makes recommendations that could help institutions and individuals enhance psychiatric education. This Commission draws from existing resources and recent developments to propose a training framework for future psychiatrists.
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Affiliation(s)
- Dinesh Bhugra
- Institute of Psychiatry, Psychology and Neurosciences, Kings College, London SE5 8AF, UK.
| | - Alexander Smith
- Department of Forensic Psychiatry, University of Bern, Bern, Switzerland
| | | | | | - Roger Ng
- Secretary for Education, WPA, Geneva, Switzerland
| | - Afzal Javed
- World Psychiatric Association, Geneva. Switzerland. Fountain House, Lahore. Pakistan
| | - Egor Chumakov
- Department of Psychiatry & Addiction, St Petersburg State University, St Petersburg, Russia
| | - Anindya Kar
- Advanced Neuropsychiatry Institute, Kolkata, India
| | - Roxanna Ruiz
- University of Francisco Moaroquin, Guatemala City, Guatemala
| | - Maria Oquendo
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, United States
| | | | - Ursula Werneke
- Department of Clinical Sciences, Psychiatry, Umeå University, Sunderby Research Unit, Umeå, Sweden
| | - Uma Suryadevara
- Geriatric Division, Department of Psychiatry, University of Florida, Gainesville, United States
| | - Michael Jibson
- Department of Psychiatry, University of Michigan, Ann Arbor, United States
| | - Jacqueline Hobbs
- Department of Psychiatry and Behavioral Sciences, University of Washington, Seattle, United States
| | | | - Muralidharan Nair
- Department of Neurology, Sree Chitra Tirunal Institute for Medical Sciences and Technology, Thiruvananthapuram, Kerala 695011, India
| | - Shekhar Seshadri
- Department of Child Psychiatry, National Institute of Mental Health and Neurosciences, Bengaluru, Karnataka 560029, India
| | - Alka Subramanyam
- Department of Psychiatry, Topiwala Nair Medical College, Mumbai, Maharashtra 400008, India
| | - Nanasaheb Patil
- Department of Psychiatry, J.N. Medical College, Belgavi, Karnataka 590010, India
| | - Prabha Chandra
- Behavioral Sciences, National Institute of Mental Health and Neurosciences, Bengaluru 560029, India
| | - Michael Liebrenz
- Department of Forensic Psychiatry, University of Bern, Bern, Switzerland
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11
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Espejo G, Reiner W, Wenzinger M. Exploring the Role of Artificial Intelligence in Mental Healthcare: Progress, Pitfalls, and Promises. Cureus 2023; 15:e44748. [PMID: 37809254 PMCID: PMC10556257 DOI: 10.7759/cureus.44748] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/05/2023] [Indexed: 10/10/2023] Open
Abstract
The rise of artificial intelligence (AI) heralds a significant revolution in healthcare, particularly in mental health. AI's potential spans diagnostic algorithms, data analysis from diverse sources, and real-time patient monitoring. It is essential for clinicians to remain informed about AI's progress and limitations. The inherent complexity of mental disorders, limited objective data, and retrospective studies pose challenges to the application of AI. Privacy concerns, bias, and the risk of AI replacing human care also loom. Regulatory oversight and physician involvement are needed for equitable AI implementation. AI integration and use in psychotherapy and other services are on the horizon. Patient trust, feasibility, clinical efficacy, and clinician acceptance are prerequisites. In the future, governing bodies must decide on AI ownership, governance, and integration approaches. While AI can enhance clinical decision-making and efficiency, it might also exacerbate moral dilemmas, autonomy loss, and issues regarding the scope of practice. Striking a balance between AI's strengths and limitations involves utilizing AI as a validated clinical supplement under medical supervision, necessitating active clinician involvement in AI research, ethics, and regulation. AI's trajectory must align with optimizing mental health treatment and upholding compassionate care.
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Affiliation(s)
- Gemma Espejo
- Psychiatry and Behavioral Sciences, University of California, Irvine School of Medicine, Irvine, USA
| | - Wade Reiner
- Psychiatry, University of Washington, Seattle, USA
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12
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Douet Vannucci V, Marchand T, Hennequin A, Caci H, Staccini P. The EPIDIA4Kids protocol for a digital epidemiology study on brain functioning in children, based on a multimodality biometry tool running on an unmodified tablet. Front Public Health 2023; 11:1185565. [PMID: 37325324 PMCID: PMC10267880 DOI: 10.3389/fpubh.2023.1185565] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2023] [Accepted: 04/28/2023] [Indexed: 06/17/2023] Open
Abstract
Introduction Neurodevelopment and related mental disorders (NDDs) are one of the most frequent disabilities among young people. They have complex clinical phenotypes often associated with transnosographic dimensions, such as emotion dysregulation and executive dysfunction, that lead to adverse impacts in personal, social, academic, and occupational functioning. Strong overlap exists then across NDDs phenotypes that are challenging for diagnosis and therapeutic intervention. Recently, digital epidemiology uses the rapidly growing data streams from various devices to advance our understanding of health's and disorders' dynamics, both in individuals and the general population, once coupled with computational science. An alternative transdiagnostic approach using digital epidemiology may thus better help understanding brain functioning and hereby NDDs in the general population. Objective The EPIDIA4Kids study aims to propose and evaluate in children, a new transdiagnostic approach for brain functioning examination, combining AI-based multimodality biometry and clinical e-assessments on an unmodified tablet. We will examine this digital epidemiology approach in an ecological context through data-driven methods to characterize cognition, emotion, and behavior, and ultimately the potential of transdiagnostic models of NDDs for children in real-life practice. Methods and analysis The EPIDIA4Kids is an uncontrolled open-label study. 786 participants will be recruited and enrolled if eligible: they are (1) aged 7 to 12 years and (2) are French speaker/reader; (3) have no severe intellectual deficiencies. Legal representative and children will complete online demographic, psychosocial and health assessments. During the same visit, children will perform additionally a paper/pencil neuro-assessments followed by a 30-min gamified assessment on a touch-screen tablet. Multi-stream data including questionnaires, video, audio, digit-tracking, will be collected, and the resulting multimodality biometrics will be generated using machine- and deep-learning algorithms. The trial will start in March 2023 and is expected to end by December 2024. Discussion We hypothesize that the biometrics and digital biomarkers will be capable of detecting early onset symptoms of neurodevelopment compared to paper-based screening while as or more accessible in real-life practice.
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Affiliation(s)
- Vanessa Douet Vannucci
- R&D Lab, O-Kidia, Nice, France
- URE Risk Epidemiology Territory INformatics Education and Health (URE RETINES), Université Côte d’Azur, Nice, France
| | - Théo Marchand
- R&D Lab, O-Kidia, Nice, France
- Bioelectronic Lab, Ecole des Mines de Saint-Étienne, Gardanne, France
| | | | - Hervé Caci
- Hôpitaux Pédiatriques de Nice CHU Lenval, Nice, France
- Centre de Recherche en Épidémiologie and Santé des Populations (CESP), INSERM U1018, Villejuif, France
| | - Pascal Staccini
- URE Risk Epidemiology Territory INformatics Education and Health (URE RETINES), Université Côte d’Azur, Nice, France
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13
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Lagera PGD, Chan SR, Yellowlees PM. Asynchronous Technologies in Mental Health Care and Education. CURRENT TREATMENT OPTIONS IN PSYCHIATRY 2023; 10:1-13. [PMID: 37360962 PMCID: PMC10157570 DOI: 10.1007/s40501-023-00286-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Accepted: 04/12/2023] [Indexed: 06/28/2023]
Abstract
Purpose of review Patients, providers, and trainees should understand the current types of asynchronous technologies that can be used to enhance the delivery and accessibility of mental health care. Asynchronous telepsychiatry (ATP) removes the need for real time communication between the clinician and patient, which improves efficiency and enables quality specialty care. ATP can be applied as distinct consultative and supervisory models in clinician-to-clinician, clinician-to-patient, and patient-to-mobile health settings. Recent findings This review is based on research literature and the authors' clinical and medical training, using experiences with asynchronous telepsychiatry from before, during, and after the COVID-19 pandemic. Our studies demonstrate that ATP provides positive outcomes in the clinician-to-patient model with demonstrated feasibility, outcomes and patient satisfaction. One author's medical education experience in the Philippines during COVID-19 highlights the potential to utilize asynchronous technology in areas with limitations to online learning. We emphasize the need to teach media skills literacy around mental health to students, coaches, therapists, and clinicians when advocating for mental well-being. Several studies have demonstrated the feasibility of incorporating asynchronous e-tools such as self-guided multimedia and artificial intelligence for data collection at the clinician-to-clinician and patient-to-mobile health level. In addition, we offer fresh perspectives on recent trends in asynchronous telehealth in wellness, applying concepts such as "tele-exercise" and "tele-yoga." Summary Asynchronous technologies continue to be integrated into mental health care services and research. Future research must ensure that the design and the usability of this technology puts the patient and provider first.
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Affiliation(s)
- Pamela Gail D. Lagera
- Division of Hospital Medicine, Clinical Informatics, University of California, San Francisco, CA USA
| | - Steven R. Chan
- Division of Hospital Medicine, Clinical Informatics, University of California, San Francisco, CA USA
- Department of Psychiatry, University of California, San Francisco, CA USA
- Department of Psychiatry, University of California, Davis, CA USA
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