Can Artificial Intelligence Improve Psychotherapy Research and Practice?

2020 ◽  
Vol 47 (5) ◽  
pp. 852-855 ◽  
Author(s):  
Rachel L. Horn ◽  
John R. Weisz
Keyword(s):  
Artificial Intelligence ◽  
Psychotherapy Research ◽  
Research And Practice

scholarly journals Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021

AI and Ethics ◽  
2021 ◽  
Author(s):  
Muhammad Ali Chaudhry ◽  
Emre Kazim
Keyword(s):  
Artificial Intelligence ◽  
Intelligent Tutoring Systems ◽  
Policy Makers ◽  
Tutoring Systems ◽  
Research And Practice ◽  
The Past ◽  
Artificial Intelligence In Education ◽  
Potential Impact ◽  
High Level ◽  
Contextualized Learning

AbstractIn the past few decades, technology has completely transformed the world around us. Indeed, experts believe that the next big digital transformation in how we live, communicate, work, trade and learn will be driven by Artificial Intelligence (AI) [83]. This paper presents a high-level industrial and academic overview of AI in Education (AIEd). It presents the focus of latest research in AIEd on reducing teachers’ workload, contextualized learning for students, revolutionizing assessments and developments in intelligent tutoring systems. It also discusses the ethical dimension of AIEd and the potential impact of the Covid-19 pandemic on the future of AIEd’s research and practice. The intended readership of this article is policy makers and institutional leaders who are looking for an introductory state of play in AIEd.

The Ethics of Placebo and Nocebo in Psychotherapy

2021 ◽  
Author(s):  
Jens Gaab ◽  
Cosima Locher ◽  
Manuel Trachsel
Keyword(s):  
Psychotherapy Research ◽  
Psychological Problems ◽  
Effective Intervention ◽  
Ethical Challenge ◽  
Research And Practice ◽  
Ongoing Controversy

There is as little doubt as much as there is empirical proof that psychotherapy is an effective intervention for psychological problems and disorders. However, there is ongoing controversy about the mechanisms underlying these often impressive, but also often overestimated effects, reaching back to the very origins of psychotherapy research. While this “great psychotherapy debate” vivifies both psychotherapy research and practice, it finally poses an ethical challenge for both psychotherapists and psychotherapy scholars. Basically, the lack of agreed and validated mechanisms impedes the attempt to inform patients about how changes of psychotherapy are brought about. Thus, even though patients can readily be furnished with possible and expectable benefits, costs and strains, the situation becomes more complex and less certain with regard to the specific mechanisms and determinants of change. In this chapter, psychotherapy scholars’ strivings and troubles for specificity will be briefly covered, touching the uncomfortable relationship with placebo and nocebo and finishing with an ethical plea for transparency in psychotherapy and of psychotherapists.

Integrating Research and Practice

2019 ◽  
pp. 432-447
Author(s):  
Louis G. Castonguay ◽  
Michael J. Constantino ◽  
Henry Xiao
Keyword(s):  
Information Sharing ◽  
Psychotherapy Research ◽  
Practice Research ◽  
Clinical Settings ◽  
Research Practice ◽  
Research Networks ◽  
Future Directions ◽  
Research And Practice ◽  
Treatment Context ◽  
Oriented Research

This chapter reviews efforts to integrate psychotherapy research and practice through collaboration and information-sharing within naturalistic clinical settings. Specifically, the chapter focuses on three types of practice-oriented research that capitalize on the bidirectional partnership between researchers and practitioners: (1) patient-focused, (2) practice-based, and (3) practice-research networks. The authors provide examples of each type of integration, highlighting the ways in which the research is different, yet complementary to more traditional studies conducted in controlled settings. They submit that the researcher–practitioner partnership in an ecologically valid treatment context represents an optimal means to reduce the pervasive research–practice chasm and to promote genuine integration for enhancing the effectiveness and personalization of psychotherapy. The chapter also discusses future directions in this vein.

scholarly journals Machine Learning in Oncology: What Should Clinicians Know?

2020 ◽  
pp. 799-810
Author(s):  
Matthew Nagy ◽  
Nathan Radakovich ◽  
Aziz Nazha
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Clinical Data ◽  
Cancer Diagnosis ◽  
Health Data ◽  
Computer Processing ◽  
Research And Practice ◽  
New Methods ◽  
Oncology Research ◽  
Processing Power

The volume and complexity of scientific and clinical data in oncology have grown markedly over recent years, including but not limited to the realms of electronic health data, radiographic and histologic data, and genomics. This growth holds promise for a deeper understanding of malignancy and, accordingly, more personalized and effective oncologic care. Such goals require, however, the development of new methods to fully make use of the wealth of available data. Improvements in computer processing power and algorithm development have positioned machine learning, a branch of artificial intelligence, to play a prominent role in oncology research and practice. This review provides an overview of the basics of machine learning and highlights current progress and challenges in applying this technology to cancer diagnosis, prognosis, and treatment recommendations, including a discussion of current takeaways for clinicians.

scholarly journals Artificial Intelligence Imagery Analysis Fostering Big Data Analytics

2019 ◽  
Vol 11 (8) ◽  
pp. 178 ◽  
Author(s):  
Stefan Cremer ◽  
Claudia Loebbecke
Keyword(s):  
Artificial Intelligence ◽  
Big Data ◽  
Data Analytics ◽  
Big Data Analytics ◽  
Quality Data ◽  
Background Information ◽  
Research And Practice ◽  
Management Approaches ◽  
Thumbnail Images

In an era of accelerating digitization and advanced big data analytics, harnessing quality data and insights will enable innovative research methods and management approaches. Among others, Artificial Intelligence Imagery Analysis has recently emerged as a new method for analyzing the content of large amounts of pictorial data. In this paper, we provide background information and outline the application of Artificial Intelligence Imagery Analysis for analyzing the content of large amounts of pictorial data. We suggest that Artificial Intelligence Imagery Analysis constitutes a profound improvement over previous methods that have mostly relied on manual work by humans. In this paper, we discuss the applications of Artificial Intelligence Imagery Analysis for research and practice and provide an example of its use for research. In the case study, we employed Artificial Intelligence Imagery Analysis for decomposing and assessing thumbnail images in the context of marketing and media research and show how properly assessed and designed thumbnail images promote the consumption of online videos. We conclude the paper with a discussion on the potential of Artificial Intelligence Imagery Analysis for research and practice across disciplines.

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