The method behind the study is as revealing as its findings. The authors used VOSviewer, a widely adopted bibliometric mapping tool, to construct keyword co-occurrence networks, run thematic clustering, and generate time-weighted overlay maps. These techniques allow researchers to visualize how scientific terms cluster together and how those clusters evolve over time. In the early years of the study window, the dominant keywords were unmistakably computational: deep learning, classification, and algorithmic approaches filled the maps. The literature of that era reads like a computer science conference, with psychology often relegated to a supporting role as a source of data rather than a partner in design.

Then came the inflection point. According to the overlay analysis, after 2023 the thematic center of gravity shifted toward human-centered concepts: large language models, digital empathy, therapeutic alliance, and personalization surged in prominence. This is not merely a change in fashionable vocabulary. The therapeutic alliance — the collaborative bond between therapist and client — is one of the most robust predictors of psychotherapy outcomes, established in decades of meta-analytic research. Its appearance as a central theme in AI research signals that developers and scholars are no longer asking whether machines can process clinical language, but whether they can participate in the relational work that makes therapy effective.

To interpret this shift, the authors turned to Everett Rogers’ Diffusion of Innovations model, a classic framework describing how new technologies spread through communities. In Rogers’ schema, adoption proceeds through stages: innovators and early adopters experiment first, followed by the early majority, late majority, and laggards. The bibliometric evidence suggests that AI in psychology has crossed a critical threshold. The early phase, dominated by technical experimentation and proof-of-concept studies, corresponds to the innovator and early adopter stages. The post-2023 explosion of work on empathy, personalization, and therapeutic relationships marks the field’s entry into the early majority stage, where the question is no longer whether the technology works but how it should be integrated into everyday practice.

The geographical distribution of the literature tells its own story. Publication output was concentrated overwhelmingly in China and the United States, with the United Kingdom, Germany, and Australia forming a second tier. This concentration mirrors broader patterns in global AI research, where the two superpowers dominate output and impact. Notably, prior research cited in the study suggests that China and the United States produce more impactful AI research when they collaborate rather than compete — a finding with real implications for mental health technology, where shared standards and cross-cultural validation could determine whether AI tools work for patients everywhere or only for some.

Perhaps the most consequential finding concerns the direction of research intensity itself. The analysis shows a clear movement away from fully automated systems and toward hybrid models in which AI augments rather than replaces human practitioners. The authors describe this as a positioning of artificial intelligence around the axis of enhancement rather than replacement. It is a reframing with enormous stakes. Surveys of the field have repeatedly asked whether AI could replace psychotherapists, and the new evidence suggests the literature has largely answered: not replace, but extend. AI systems that draft empathic responses, flag risk, personalize interventions, or train novice counselors appear to be valued precisely because they keep humans in the loop.

The empirical work behind this shift lends it credibility. Studies cited in the bibliometric map include findings that ChatGPT outperformed humans on certain emotional awareness evaluations, that human-AI collaboration produced more empathic conversations in text-based peer-to-peer mental health support, and that large language models can help upskill novice counselors through simulated practice and feedback. Other research has compared responses from human therapists and LLM-based chatbots to assess therapeutic communication quality, and integrative reviews have begun asking whether a genuinely digital therapeutic alliance exists at all. Together, these studies form the intellectual scaffolding of the post-2023 thematic cluster that the bibliometric analysis identifies.

The timing matters. The World Health Organization’s 2022 world mental health report documented a staggering global gap between need and available care, with millions unable to access any form of treatment. Conversational agents and AI-based interventions have long been promoted as a scalable answer to that shortage, and meta-analyses of AI-based conversational agents for mental health and well-being have shown promising, if still preliminary, results. The bibliometric evidence suggests the field is now grappling seriously with the conditions under which such tools can deliver not just information but genuine relational benefit — and with the ethical questions, including bias in AI mental health applications, that scale-up inevitably raises.

For clinicians, researchers, and technology developers, the study offers a roadmap of where the field has been and where it is heading. The interdisciplinary structure it documents — spanning psychology, computer science, psychiatry, and engineering — reflects a maturing consensus that no single discipline can build trustworthy mental health AI alone. The transition from algorithmic dominance to human-centered themes suggests that the next wave of innovation will be judged not by benchmark accuracy alone but by whether systems can sustain something resembling a working alliance, adapt to individual users, and enhance the capacities of human caregivers rather than simulating them wholesale.

The decade from 2015 to 2025 began with machines learning to classify and ended with researchers asking machines to care. Whether digital empathy can ever be more than a well-engineered imitation remains an open and contested question. But the bibliometric record is unambiguous about the direction of travel: the AI revolution in psychology has moved from the laboratory to the consulting room, from replacement to enhancement, and from algorithms to empathy — and the early majority is now paying attention.

Subject of Research: Bibliometric mapping of artificial intelligence research trends in psychology and mental health from 2015 to 2025

Article Title: From algorithms to empathy: a bibliometric analysis of the AI revolution in psychology and related fields (2015–2025)

Article References: Kusci, İ., & Bakır, V. (2026). From algorithms to empathy: a bibliometric analysis of the AI revolution in psychology and related fields (2015–2025). Current Psychology, 45(18), Article 1510. https://doi.org/10.1007/s12144-026-10070-3

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10070-3

Keywords: artificial intelligence, psychology, bibliometric analysis, large language models, digital empathy, therapeutic alliance, mental health, ChatGPT, diffusion of innovations, psychotherapy, hybrid models, digital mental health