Leveraging Novel Technologies and Artificial Intelligence to Advance Practice-Oriented Research

被引:6
作者
Atzil-Slonim, Dana [1 ]
Penedo, Juan Martin Gomez [2 ]
Lutz, Wolfgang [3 ]
机构
[1] Bar Ilan Univ, Dept Psychol, Ramat Gan, Israel
[2] Univ Buenos Aires, Dept Psychol, Buenos Aires, Argentina
[3] Univ Trier, Dept Psychol, Trier, Germany
关键词
Artificial intelligence; Practice-oriented research; Machine learning; Psychotherapy research; MENTAL-HEALTH; STEPPED CARE; DEPRESSION; MACHINE; CLIENTS; OUTCOMES; UTILITY;
D O I
10.1007/s10488-023-01309-3
中图分类号
R19 [保健组织与事业(卫生事业管理)];
学科分类号
摘要
Mental health services are experiencing notable transformations as innovative technologies and artificial intelligence (AI) are increasingly utilized in a growing number of studies and services.These cutting-edge technologies carry the promise of substantial improvements in the field of mental health. Nevertheless, questions emerge about the alignment of novel technologies and AI systems with human needs, especially in the context of vulnerable populations receiving mental healthcare. The practice-oriented research (POR) model is pivotal in seamlessly integrating these emerging technologies into clinical research and practice. It underscores the importance of tight collaboration between clinicians and researchers, all driven by the central goal of ensuring and elevating client well-being. This paper focuses on how novel technologies can enhance the POR model and highlights its pivotal role in integrating these technologies into clinical research and practice. We discuss two key phases: pre-treatment, and during treatment. For each phase, we describe the challenges, present the major technological innovations, describe recent studies exemplifying technology use, and suggest future directions. Ethical concerns and the importance of aligning humans and technology are also considered, in addition to implications for practice and training.
引用
收藏
页码:306 / 317
页数:12
相关论文
共 94 条
[91]   Cost-Utility and Cost-Effectiveness of Internet-Based Treatment for Adults With Depressive Symptoms: Randomized Trial [J].
Warmerdam, Lisanne ;
Smit, Filip ;
van Straten, Annemieke ;
Riper, Heleen ;
Cuijpers, Pim .
JOURNAL OF MEDICAL INTERNET RESEARCH, 2010, 12 (05) :e53p.1-e53p.11
[92]  
WHO, 2020, ICD 11
[93]  
Yeung K., 2018, MSI AUT, V5
[94]   The utility of smartphone-based, ecological momentary assessment for depressive symptoms [J].
Yim, Samantha J. ;
Lui, Leanna M. W. ;
Lee, Yena ;
Rosenblat, Joshua D. ;
Ragguett, Renee-Marie ;
Park, Caroline ;
Subramaniapillai, Mehala ;
Cao, Bing ;
Zhou, Aileen ;
Rong, Carola ;
Li, Kangguang ;
Ho, Roger C. ;
Coles, Alexandria S. ;
Majeed, Amna ;
Wong, Elizabeth R. ;
Phan, Lee ;
Nasri, Flora ;
McIntyre, Roger S. .
JOURNAL OF AFFECTIVE DISORDERS, 2020, 274 :602-609