Current Status and Future Directions of Artificial Intelligence in Post-Traumatic Stress Disorder: A Literature Measurement Analysis

Abstract

This study aims to explore the current state of research and the applicability of artificial intelligence (AI) at various stages of post-traumatic stress disorder (PTSD), including prevention, diagnosis, treatment, patient self-management, and drug development. We conducted a bibliometric analysis using software tools such as Bibliometrix (version 4.1), VOSviewer (version 1.6.19), and CiteSpace (version 6.3.R1) on the relevant literature from the Web of Science Core Collection (WoSCC). The analysis reveals a significant increase in publications since 2017. Kerry J. Ressler has emerged as the most influential author in the field to date. The United States leads in the number of publications, producing seven times more papers than Canada, the second-ranked country, and demonstrating substantial influence. Harvard University and the Veterans Health Administration are also key institutions in this field. The Journal of Affective Disorders has the highest number of publications and impact in this area. In recent years, keywords related to functional connectivity, risk factors, and algorithm development have gained prominence. The field holds immense research potential, with AI poised to revolutionize PTSD management through early symptom detection, personalized treatment plans, and continuous patient monitoring. However, there are numerous challenges, and fully realizing AI's potential will require overcoming hurdles in algorithm design, data integration, and societal ethics. To promote more extensive and in-depth future research, it is crucial to prioritize the development of standardized protocols for AI implementation, foster interdisciplinary collaboration-especially between AI and neuroscience-and address public concerns about AI's role in healthcare to enhance its acceptance and effectiveness.

Keywords: Bibliometrix; CiteSpace; VOSviewer; algorithm; artificial intelligence; bibliometric analysis; digital psychiatry; post-traumatic stress disorder.

Figure 1

The types and applications of AI in PTSD visualization.

Figure 2

Summary of related works versus our survey (Turvey & Fortney, 2017; Bourla et al., 2018; Reinertsen & Clifford, 2018; Lombardero et al., 2019; Dennis et al., 2019; Low et al., 2020; Rashid & Calhoun, 2020; Sadeghi et al., 2020; Schultebraucks & Chang, 2021; Zafari et al., 2021; van Rooij et al., 2021; Vieira et al., 2022; E. J. Kim et al., 2015; Church et al., 2022; Abd-Alrazaq et al., 2022; Bertl et al., 2022a; Iyortsuun et al., 2023; Laugharne et al., 2023; Govindula et al., 2023; Othmani et al., 2023; Galatzer-Levy & Onnela, 2023; Jia et al., 2024; L. Wang et al., 2023).

Figure 3

Bibliometrix analysis framework.

Figure 4

The overall trend of publications and average citations. The annual trend of publications and average citations per article on research on the application of AI in the diagnosis and treatment of PTSD indexed in the Web of Science Core Collection (WoSCC) database from 1999 to 2023 using Bibliometrix.

Figure 5

The overall trend of publications and average citations. The annual trend of publications and average citations per article on research on the application of AI in the diagnosis and treatment of PTSD indexed in the Web of Science Core Collection (WoSCC) database from 1999 to 2023 using Bibliometrix.

Figure 6

Country collaboration map, with a publication threshold of 1 and a collaboration threshold of 2. This figure was created using Bibliometrix.

Figure 7

Affiliation relationship between authors and countries. Overview tree-field plot of affiliations (items 8), authors (items 10), and countries (items 10) using Bibliometrix.

Figure 8

Journal productivity and core journal distribution analysis. This figure was created using Bibliometrix.

Figure 9

Publication co-citation network with impact information. This visualization is based on the “co-citation” file type, with the unit set to “cited sources”; among the 5708 data points, we set a minimum citation threshold of 70. A total of 62 journals exceeded this threshold, resulting in 62 nodes. This analysis was conducted using Vosviewer. The sources’ impact information analysis was performed using Bibliometrix.

Figure 10

Keyword co-occurrence network and trend. Select the “Co-occurrence” file type and set the unit to “All Keywords”. Out of 2230 keywords, we set the minimum occurrence threshold to 15, resulting in 39 keywords meeting this criterion using VOSviewer. The keyword trend timeline was created using Bibliometrix.

Figure 11

Keyword cluster map. The keyword clustering analysis considered all keywords, using the g-index (k = 10) as the selection criterion, and resulted in 11 clusters based on data from 431 documents. These clusters form the basic components of Figure 10, with each cluster having its own color and label. This figure was created using CiteSpace.

Figure 12

Thematic evolution map. This map illustrates the different states of research themes in the field of AI’s role in PTSD across three distinct periods: (i) 1999–2006, (ii) 2007–2014, and (iii) 2015–2023. This figure was created using Bibliometrix.

Figure 13

The top twenty-one keywords with the strongest citation bursts using CiteSpace.