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O’Leary A, Lahey T, Lovato J, Loftness B, Douglas A, Skelton J, Cohen JG, Copeland WE, McGinnis RS, McGinnis EW. Using Wearable Digital Devices to Screen Children for Mental Health Conditions: Ethical Promises and Challenges. SENSORS (BASEL, SWITZERLAND) 2024; 24:3214. [PMID: 38794067 PMCID: PMC11125700 DOI: 10.3390/s24103214] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Revised: 05/13/2024] [Accepted: 05/16/2024] [Indexed: 05/26/2024]
Abstract
In response to a burgeoning pediatric mental health epidemic, recent guidelines have instructed pediatricians to regularly screen their patients for mental health disorders with consistency and standardization. Yet, gold-standard screening surveys to evaluate mental health problems in children typically rely solely on reports given by caregivers, who tend to unintentionally under-report, and in some cases over-report, child symptomology. Digital phenotype screening tools (DPSTs), currently being developed in research settings, may help overcome reporting bias by providing objective measures of physiology and behavior to supplement child mental health screening. Prior to their implementation in pediatric practice, however, the ethical dimensions of DPSTs should be explored. Herein, we consider some promises and challenges of DPSTs under three broad categories: accuracy and bias, privacy, and accessibility and implementation. We find that DPSTs have demonstrated accuracy, may eliminate concerns regarding under- and over-reporting, and may be more accessible than gold-standard surveys. However, we also find that if DPSTs are not responsibly developed and deployed, they may be biased, raise privacy concerns, and be cost-prohibitive. To counteract these potential shortcomings, we identify ways to support the responsible and ethical development of DPSTs for clinical practice to improve mental health screening in children.
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Affiliation(s)
- Aisling O’Leary
- Department of Philosophy, Virginia Polytechnic Institute and State University, Blacksburg, VA 24060, USA;
| | - Timothy Lahey
- University of Vermont Medical Center, Burlington, VT 05401, USA; (T.L.); (A.D.)
| | - Juniper Lovato
- Complex Systems Center, University of Vermont, Burlington VT 05405, USA; (J.L.); (B.L.)
| | - Bryn Loftness
- Complex Systems Center, University of Vermont, Burlington VT 05405, USA; (J.L.); (B.L.)
| | - Antranig Douglas
- University of Vermont Medical Center, Burlington, VT 05401, USA; (T.L.); (A.D.)
| | - Joseph Skelton
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA;
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA
| | - Jenna G. Cohen
- Department of Electrical and Biomedical Engineering, University of Vermont, Burlington VT 05405, USA;
| | | | - Ryan S. McGinnis
- Department of Biomedical Engineering, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA
| | - Ellen W. McGinnis
- Department of Pediatrics, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA;
- Division of Public Health Sciences, Wake Forest University School of Medicine, Winston-Salem 27101, NC, USA
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2
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Hockley J, Irwin P, Kornhaber R, West S, Stanton R, Hungerford C, Cleary M. To AI or Not to AI: That Is the Question in Mental Health Nurse Recruitment. Issues Ment Health Nurs 2024:1-4. [PMID: 38684002 DOI: 10.1080/01612840.2024.2341043] [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)
- Janine Hockley
- School of Health, Medical, and Applied Sciences, CQUniversity, Rockhampton, Queensland, Australia
| | - Pauletta Irwin
- School of Nursing, Paramedicine and Healthcare Sciences, Charles Sturt University, Bathurst, New South Wales, Australia
| | - Rachel Kornhaber
- School of Nursing, Paramedicine and Healthcare Sciences, Charles Sturt University, Bathurst, New South Wales, Australia
| | - Sancia West
- School of Nursing, Midwifery & Social Sciences, CQUniversity, Sydney, New South Wales, 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
| | - Michelle Cleary
- School of Nursing, Midwifery & Social Sciences, CQUniversity, Sydney, New South Wales, Australia
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3
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Zhai Y, Chu L, Liu Y, Wang D, Wu Y. Using deep learning-based artificial intelligence electronic images in improving middle school teachers' literacy. PeerJ Comput Sci 2024; 10:e1844. [PMID: 38660146 PMCID: PMC11041997 DOI: 10.7717/peerj-cs.1844] [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: 09/28/2023] [Accepted: 01/09/2024] [Indexed: 04/26/2024]
Abstract
With the rapid development of societal information, electronic educational resources have become an indispensable component of modern education. In response to the increasingly formidable challenges faced by secondary school teachers, this study endeavors to analyze and explore the application of artificial intelligence (AI) methods to enhance their cognitive literacy. Initially, this discourse delves into the application of AI-generated electronic images in the training and instruction of middle school educators, subjecting it to thorough analysis. Emphasis is placed on elucidating the pivotal role played by AI electronic images in elevating the proficiency of middle school teachers. Subsequently, an integrated intelligent device serves as the foundation for establishing a model that applies intelligent classification and algorithms based on the Structure of the Observed Learning Outcome (SOLO). This model is designed to assess the cognitive literacy and teaching efficacy of middle school educators, and its performance is juxtaposed with classification algorithms such as support vector machine (SVM) and decision trees. The findings reveal that, following 600 iterations of the model, the SVM algorithm achieves a 77% accuracy rate in recognizing teacher literacy, whereas the SOLO algorithm attains 80%. Concurrently, the spatial complexities of the SVM-based and SOLO-based intelligent literacy improvement models are determined to be 45 and 22, respectively. Notably, it is discerned that, with escalating iterations, the SOLO algorithm exhibits higher accuracy and reduced spatial complexity in evaluating teachers' pedagogical literacy. Consequently, the utilization of AI methodologies proves highly efficacious in advancing electronic imaging technology and enhancing the efficacy of image recognition in educational instruction.
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Affiliation(s)
- Yixi Zhai
- School of Foreign Studies, Tangshan Normal University, Tangshan City, China
| | - Liqing Chu
- School of Foreign Studies, Tangshan Normal University, Tangshan City, China
| | - Yanlan Liu
- School of Foreign Studies, Tangshan Normal University, Tangshan City, China
| | - Dandan Wang
- School of Foreign Studies, Tangshan Normal University, Tangshan City, China
| | - Yufei Wu
- School of Foreign Studies, Tangshan Normal University, Tangshan City, China
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Cardenas-Iniguez C, Schachner JN, Ip KI, Schertz KE, Gonzalez MR, Abad S, Herting MM. Building towards an adolescent neural urbanome: Expanding environmental measures using linked external data (LED) in the ABCD study. Dev Cogn Neurosci 2024; 65:101338. [PMID: 38195369 PMCID: PMC10837718 DOI: 10.1016/j.dcn.2023.101338] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2023] [Revised: 12/20/2023] [Accepted: 12/31/2023] [Indexed: 01/11/2024] Open
Abstract
Many recent studies have demonstrated that environmental contexts, both social and physical, have an important impact on child and adolescent neural and behavioral development. The adoption of geospatial methods, such as in the Adolescent Brain Cognitive Development (ABCD) Study, has facilitated the exploration of many environmental contexts surrounding participants' residential locations without creating additional burdens for research participants (i.e., youth and families) in neuroscience studies. However, as the number of linked databases increases, developing a framework that considers the various domains related to child and adolescent environments external to their home becomes crucial. Such a framework needs to identify structural contextual factors that may yield inequalities in children's built and natural environments; these differences may, in turn, result in downstream negative effects on children from historically minoritized groups. In this paper, we develop such a framework - which we describe as the "adolescent neural urbanome" - and use it to categorize newly geocoded information incorporated into the ABCD Study by the Linked External Data (LED) Environment & Policy Working Group. We also highlight important relationships between the linked measures and describe possible applications of the Adolescent Neural Urbanome. Finally, we provide a number of recommendations and considerations regarding the responsible use and communication of these data, highlighting the potential harm to historically minoritized groups through their misuse.
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Affiliation(s)
- Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
| | - Jared N Schachner
- Price School of Public Policy, University of Southern California, Los Angeles, CA, USA
| | - Ka I Ip
- Institute of Child Development, University of Minnesota, MN, USA
| | - Kathryn E Schertz
- Department of Psychology, University of Michigan, Ann Arbor, MI, USA
| | - Marybel R Gonzalez
- Department of Psychiatry and Behavioral Health, The Ohio State University, Columbus, OH, USA
| | - Shermaine Abad
- Department of Radiology, University of California, San Diego, CA, USA
| | - Megan M Herting
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
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Green BL, Murphy A, Robinson E. Accelerating health disparities research with artificial intelligence. Front Digit Health 2024; 6:1330160. [PMID: 38322109 PMCID: PMC10844447 DOI: 10.3389/fdgth.2024.1330160] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/10/2024] [Indexed: 02/08/2024] Open
Affiliation(s)
- B. Lee Green
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - Anastasia Murphy
- Department of Health Outcomes and Behavior, Moffitt Cancer Center, Tampa, FL, United States
| | - Edmondo Robinson
- Center for Digital Health, Moffitt Cancer Center, Tampa, FL, United States
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Cunningham PB, Gilmore J, Naar S, Preston SD, Eubanks CF, Hubig NC, McClendon J, Ghosh S, Ryan-Pettes S. Opening the Black Box of Family-Based Treatments: An Artificial Intelligence Framework to Examine Therapeutic Alliance and Therapist Empathy. Clin Child Fam Psychol Rev 2023; 26:975-993. [PMID: 37676364 PMCID: PMC10845126 DOI: 10.1007/s10567-023-00451-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 08/15/2023] [Indexed: 09/08/2023]
Abstract
The evidence-based treatment (EBT) movement has primarily focused on core intervention content or treatment fidelity and has largely ignored practitioner skills to manage interpersonal process issues that emerge during treatment, especially with difficult-to-treat adolescents (delinquent, substance-using, medical non-adherence) and those of color. A chief complaint of "real world" practitioners about manualized treatments is the lack of correspondence between following a manual and managing microsocial interpersonal processes (e.g. negative affect) that arise in treating "real world clients." Although family-based EBTs share core similarities (e.g. focus on family interactions, emphasis on practitioner engagement, family involvement), most of these treatments do not have an evidence base regarding common implementation and treatment process problems that practitioners experience in delivering particular models, especially in mid-treatment when demands on families to change their behavior is greatest in treatment - a lack that characterizes the field as a whole. Failure to effectively address common interpersonal processes with difficult-to-treat families likely undermines treatment fidelity and sustained use of EBTs, treatment outcome, and contributes to treatment dropout and treatment nonadherence. Recent advancements in wearables, sensing technologies, multivariate time-series analyses, and machine learning allow scientists to make significant advancements in the study of psychotherapy processes by looking "under the skin" of the provider-client interpersonal interactions that define therapeutic alliance, empathy, and empathic accuracy, along with the predictive validity of these therapy processes (therapeutic alliance, therapist empathy) to treatment outcome. Moreover, assessment of these processes can be extended to develop procedures for training providers to manage difficult interpersonal processes while maintaining a physiological profile that is consistent with astute skills in psychotherapeutic processes. This paper argues for opening the "black box" of therapy to advance the science of evidence-based psychotherapy by examining the clinical interior of evidence-based treatments to develop the next generation of audit- and feedback- (i.e., systemic review of professional performance) supervision systems.
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Affiliation(s)
- Phillippe B Cunningham
- Division of Global and Community Health, Department of Psychiatry and Behavioral Sciences, Medical University of South Carolina, 176 Croghan Spur Rd. Ste. 104, Charleston, SC, 29407, USA.
| | - Jordon Gilmore
- Department of Bioengineering, Clemson University, 401-3 Rhodes Research Center, Clemson, SC, USA
| | - Sylvie Naar
- Center for Translational Behavioral Science, Florida State University, 2010 Levy Avenue Building B, Suite B0266, Tallahassee, FL, USA
| | - Stephanie D Preston
- Department of Psychology, University of Michigan, 530 Church Street, Ann Arbor, MI, 48109, USA
| | - Catherine F Eubanks
- Gordon F. Derner School of Psychology, Adelphi University, One South Avenue, Garden City, NY, USA
| | - Nina Christina Hubig
- School of Computing, Clemson University, 1240 Supply Street, Charleston, SC, 29405, USA
| | - Jerome McClendon
- Department of Automotive Engineering, Clemson University, 4 Research Drive, Greenville, SC, USA
| | - Samiran Ghosh
- Department of Biostatistics and Data Science & Coordinating Center for Clinical Trials (CCCT), University of Texas School of Public Health, University Texas Health Sciences , RAS W-928, 1200 Pressler Street, Houston, TX, 77030, USA
| | - Stacy Ryan-Pettes
- Department of Psychology and Neuroscience, Baylor University, One Bear Place #97334, Waco, TX, 76798, USA
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Nashwan AJ, Gharib S, Alhadidi M, El-Ashry AM, Alamgir A, Al-Hassan M, Khedr MA, Dawood S, Abufarsakh B. Harnessing Artificial Intelligence: Strategies for Mental Health Nurses in Optimizing Psychiatric Patient Care. Issues Ment Health Nurs 2023; 44:1020-1034. [PMID: 37850937 DOI: 10.1080/01612840.2023.2263579] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 10/19/2023]
Abstract
This narrative review explores the transformative impact of Artificial Intelligence (AI) on mental health nursing, particularly in enhancing psychiatric patient care. AI technologies present new strategies for early detection, risk assessment, and improving treatment adherence in mental health. They also facilitate remote patient monitoring, bridge geographical gaps, and support clinical decision-making. The evolution of virtual mental health assistants and AI-enhanced therapeutic interventions are also discussed. These technological advancements reshape the nurse-patient interactions while ensuring personalized, efficient, and high-quality care. The review also addresses AI's ethical and responsible use in mental health nursing, emphasizing patient privacy, data security, and the balance between human interaction and AI tools. As AI applications in mental health care continue to evolve, this review encourages continued innovation while advocating for responsible implementation, thereby optimally leveraging the potential of AI in mental health nursing.
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Affiliation(s)
- Abdulqadir J Nashwan
- Nursing Department, Hamad Medical Corporation, Doha, Qatar
- Department of Public Health, College of Health Sciences, QU Health, Qatar University, Doha, Qatar
| | - Suzan Gharib
- Nursing Department, Al-Khaldi Hospital, Amman, Jordan
| | - Majdi Alhadidi
- Psychiatric & Mental Health Nursing, Faculty of Nursing, Al-Zaytoonah University of Jordan, Amman, Jordan
| | | | | | | | | | - Shaimaa Dawood
- Faculty of Nursing, Alexandria University, Alexandria, Egypt
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Hadar-Shoval D, Elyoseph Z, Lvovsky M. The plasticity of ChatGPT's mentalizing abilities: personalization for personality structures. Front Psychiatry 2023; 14:1234397. [PMID: 37720897 PMCID: PMC10503434 DOI: 10.3389/fpsyt.2023.1234397] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/04/2023] [Accepted: 08/22/2023] [Indexed: 09/19/2023] Open
Abstract
This study evaluated the potential of ChatGPT, a large language model, to generate mentalizing-like abilities that are tailored to a specific personality structure and/or psychopathology. Mentalization is the ability to understand and interpret one's own and others' mental states, including thoughts, feelings, and intentions. Borderline Personality Disorder (BPD) and Schizoid Personality Disorder (SPD) are characterized by distinct patterns of emotional regulation. Individuals with BPD tend to experience intense and unstable emotions, while individuals with SPD tend to experience flattened or detached emotions. We used ChatGPT's free version 23.3 and assessed the extent to which its responses akin to emotional awareness (EA) were customized to the distinctive personality structure-character characterized by Borderline Personality Disorder (BPD) and Schizoid Personality Disorder (SPD), employing the Levels of Emotional Awareness Scale (LEAS). ChatGPT was able to accurately describe the emotional reactions of individuals with BPD as more intense, complex, and rich than those with SPD. This finding suggests that ChatGPT can generate mentalizing-like responses consistent with a range of psychopathologies in line with clinical and theoretical knowledge. However, the study also raises concerns regarding the potential for stigmas or biases related to mental diagnoses to impact the validity and usefulness of chatbot-based clinical interventions. We emphasize the need for the responsible development and deployment of chatbot-based interventions in mental health, which considers diverse theoretical frameworks.
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Affiliation(s)
- Dorit Hadar-Shoval
- Department of Psychology and Educational Counseling, The Center for Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, Israel
| | - Zohar Elyoseph
- Department of Psychology and Educational Counseling, The Center for Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, Israel
- Department of Brain Sciences, Faculty of Medicine, Imperial College London, London, United Kingdom
- Educational Psychology Department, Center for Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, Israel
| | - Maya Lvovsky
- Educational Psychology Department, Center for Psychobiological Research, Max Stern Yezreel Valley College, Emek Yezreel, Israel
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