About

Ashleigh Golden is an Adjunct Clinical Assistant Professor in the Department of Psychiatry and Behavioral Sciences at Stanford University. She is affiliated with the Center for Artificial Intelligence in Medicine & Imaging (AIMI) at Stanford. Her role involves engaging in research and activities related to artificial intelligence applications in medicine and imaging, contributing to the center's mission to advance AI in healthcare. Specific details about her research focus, background, or key contributions are not provided on the page.

Research topics

• Computer Science
• Computer Security
• Psychiatry
• Psychology
• Sociology
• Artificial Intelligence
• Political Science
• Psychotherapist
• World Wide Web

Selected publications

• A transdiagnostic model for how general purpose AI chatbots can perpetuate OCD and anxiety disorders

  npj Digital Medicine · 2026-03-13 · 1 citations

  articleOpen access1st author

  Millions are turning to general-purpose AI chatbots for psychological support, potentially reinforcing symptoms such as intolerance of uncertainty, "need to know" compulsions, and perfectionism. Clinical observation and emerging research suggest chatbot features exacerbate transdiagnostic avoidance-a process integral to OCD and anxiety-perpetuating maladaptive cycles and hindering corrective learning. We propose a framework in which avoidance is reinforced through repeated chatbot interactions, and outline strategies for clinicians, users, developers, and policymakers to support healthier engagement.

  PublisherOA PDFDOI

• Artificial Intelligence in Obsessive-Compulsive Disorder: A Systematic Review

  Current Treatment Options in Psychiatry · 2025-06-14 · 6 citations

  reviewOpen access

  Purpose of Review: Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery. Recent Findings: While previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications. Summary: AI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care. Supplementary Information: The online version contains supplementary material available at 10.1007/s40501-025-00359-8.

  PublisherOA PDFDOI

• Generative AI–Powered Mental Wellness Chatbot for College Student Mental Wellness: Open Trial

  JMIR Formative Research · 2025-07-28 · 5 citations

  articleOpen access

  Background: Colleges have turned to digital mental health interventions to meet the increasing mental health treatment needs of their students. Among these, chatbots stand out as artificial intelligence-driven tools capable of engaging in human-like conversations that have demonstrated some effectiveness in reducing depression and anxiety symptoms. Objective: This study aimed to assess the feasibility and acceptability of using Wayhaven, an artificial intelligence chatbot, among college students with elevated depression or anxiety symptoms. We also aimed to examine the preliminary effectiveness of Wayhaven in improving symptoms of anxiety and depression, hopelessness, agency, and self-efficacy among students. Methods: Participants were 50 racially and ethnically diverse college students with elevated depression or anxiety symptoms (n=45, 80% female; mean age 22.12, SD 4.42 years). Students were asked to use Wayhaven over the course of 1 week and completed assessments at preintervention, after 1 session, and 1 week. Results: Wayhaven use was associated with a significant decrease in depression (β=-1.62; P<.001), anxiety (β=-2.15; P<.001), and hopelessness (β=-.64; P<.001) and a significant increase in agency (β=.64; P=.32), self-efficacy (β=.53; P=.02), and well-being (t40=2.90; P=.006; d=0.45) across the study period. Most students also reported being satisfied with Wayhaven and it being a tool they would recommend to their peers. Conclusions: Findings suggest that Wayhaven may be a viable mental wellness resource for diverse students with elevated depression or anxiety symptoms.

  PublisherDOI

• Describing the Framework for AI Tool Assessment in Mental Health and Applying It to a Generative AI Obsessive-Compulsive Disorder Platform: Tutorial

  JMIR Formative Research · 2024 · 17 citations

  1st authorCorresponding
  • Computer Science
  • Psychology
  • Computer Science

  As artificial intelligence (AI) technologies occupy a bigger role in psychiatric and psychological care and become the object of increased research attention, industry investment, and public scrutiny, tools for evaluating their clinical, ethical, and user-centricity standards have become essential. In this paper, we first review the history of rating systems used to evaluate AI mental health interventions. We then describe the recently introduced Framework for AI Tool Assessment in Mental Health (FAITA-Mental Health), whose scoring system allows users to grade AI mental health platforms on key domains, including credibility, user experience, crisis management, user agency, health equity, and transparency. Finally, we demonstrate the use of FAITA-Mental Health scale by systematically applying it to OCD Coach, a generative AI tool readily available on the ChatGPT store and designed to help manage the symptoms of obsessive-compulsive disorder. The results offer insights into the utility and limitations of FAITA-Mental Health when applied to "real-world" generative AI platforms in the mental health space, suggesting that the framework effectively identifies key strengths and gaps in AI-driven mental health tools, particularly in areas such as credibility, user experience, and acute crisis management. The results also highlight the need for stringent standards to guide AI integration into mental health care in a manner that is not only effective but also safe and protective of the users' rights and welfare.

  PublisherDOI

• Describing the Framework for AI Tool Assessment in Mental Health and Applying It to a Generative AI Obsessive-Compulsive Disorder Platform: Tutorial (Preprint)

  2024

  1st authorCorresponding
  • Computer Science
  • Artificial Intelligence
  • Psychology

  <sec> <title>UNSTRUCTURED</title> As artificial intelligence (AI) technologies occupy a bigger role in psychiatric and psychological care and become the object of increased research attention, industry investment, and public scrutiny, tools for evaluating their clinical, ethical, and user-centricity standards have become essential. In this paper, we first review the history of rating systems used to evaluate AI mental health interventions. We then describe the recently introduced Framework for AI Tool Assessment in Mental Health (FAITA-Mental Health), whose scoring system allows users to grade AI mental health platforms on key domains, including credibility, user experience, crisis management, user agency, health equity, and transparency. Finally, we demonstrate the use of FAITA-Mental Health scale by systematically applying it to OCD Coach, a generative AI tool readily available on the ChatGPT store and designed to help manage the symptoms of obsessive-compulsive disorder. The results offer insights into the utility and limitations of FAITA-Mental Health when applied to “real-world” generative AI platforms in the mental health space, suggesting that the framework effectively identifies key strengths and gaps in AI-driven mental health tools, particularly in areas such as credibility, user experience, and acute crisis management. The results also highlight the need for stringent standards to guide AI integration into mental health care in a manner that is not only effective but also safe and protective of the users’ rights and welfare. </sec>

  PublisherDOI

• The Framework for <scp>AI</scp> Tool Assessment in Mental Health (<scp>FAITA</scp> ‐ Mental Health): a scale for evaluating <scp>AI</scp>‐powered mental health tools

  World Psychiatry · 2024-09-16 · 11 citations

  letterOpen access1st authorCorresponding

  Even within the ever-evolving landscape of digital mental health interventions, the advent of generative artificial intelligence (GAI), large language models (LLMs), and generative pre-trained transformers (GPTs) represents a paradigm shift. These technologies bring the promise of scalable and personalized diagnostics, psychoeducation and treatment that may help close a stubborn access-to-care gap1. At the same time, the risk to patients’ health from unmonitored AI-powered care, and to users’ data from insecure platforms, presents unprecedented challenges. The enthusiasm and fear that AI mental health offerings simultaneously generate make a comprehensive tool for their systematic assessment a timely necessity. To our knowledge, no comprehensive scale exists for systematically evaluating AI interventions. Abbasian et al2 suggested helpful metrics for assessing AI health care conversations, without explicitly tailoring them to mental health. AI scholar L. Eliot3 advocated rating mental health chatbots by their autonomy or degree of independence from human oversight. Pfohl et al4 put the focus squarely on evaluating equity and bias. These efforts highlight the need for a comprehensive toolbox for evaluating AI interventions in mental health – one that encompasses autonomy and equity, but also efficacy, user experience, safety and ethical integrity, among other crucial dimensions5. Evaluative digital mental health tools that predate the rise of AI provide valuable lessons. The now discontinued nonprofit One Mind PsyberGuide6 offered reviews of digital mental health apps with a focus on three dimensions: credibility, user experience, and transparency. This framework seemed to fulfill an important role across several constituencies: Psihogios et al7 praised it in their paper on pediatric mobile health apps; Nesamoney8 endorsed it for helping app developers and designers; and Garland et al9 described it as more comprehensive and user-friendly than other app review platforms, including that by the American Psychological Association. In creating an assessment framework for AI-powered mental health tools, PsyberGuide is a reasonable starting point. Besides short app reviews by users and lengthier expert reviews, it offered scoring guidelines for its dimensions. Given the importance of AI tools “learning” from ongoing feedback and reviews, and of a scoring system that facilitates comparisons across AI offerings, it forms a helpful basis. Here we introduce the Framework for AI Tool Assessment in Mental Health (FAITA - Mental Health), a structured scale developed by updating PsyberGuide's “credibility”, “user experience” and “transparency” dimensions for the AI “age”, and incorporating three crucial new dimensions: “user agency”, “diversity and inclusivity” and “crisis management” (see supplementary information for the full structured FAITA - Mental Health form). Our framework reflects awareness of both the potential and challenges of AI tools, and emphasizes evidence base, user-centric design, safety, personalization, cultural sensitivity, and the ethical use of technology. Ultimately, the framework aims to promote “best practices” and to guide industry development of AI technologies that benefit users while respecting their rights. Additionally, the framework seeks to be sufficiently flexible to accommodate continued evolution in the field and, with some minor modifications, adaptation to other medical disciplines impacted by AI (e.g., “FAITA - Genetics”). The framework's first dimension, “credibility”, evaluates AI-powered mental health tools according to their scientific underpinnings and user goal achievement capabilities. Integrating the three subdimensions of “proposed goal”, “evidence-based content” and “retention”, this dimension advocates for interventions that have clear and measurable goals, are grounded in validated research and practices, and can keep users meaningfully engaged over time. Each subdimension is awarded up to 2 points, for a maximum dimension score of 6 for the most “credible” tool. The second dimension for assessing AI mental health tools, “user experience”, addresses more complex interactions than those encountered in static mental health apps. As such, PsyberGuide's “user experience” dimension – with its focus on engagement, functionality and esthetics – was found to be insufficient, and three new subdimensions were incorporated: “personalized adaptability”, to evaluate the AI's ability to improve from user feedback over time; “quality of interactions”, to evaluate the naturalness of exchanges; and “mechanisms for feedback”, to underscore the importance of users’ ability to report issues, suggest improvements, and seek assistance. Each subdimension on the “user experience” dimension is awarded up to 2 points, for a maximum dimension score of 6. The third dimension, “user agency”, is new and underlines the importance of empowering users to manage their personal data and treatment choices. It is divided into two subdimensions. The first, “user autonomy, data protection, and privacy”, focuses on control over personal health data, clearly worded and user-friendly consent processes, robust data protection protocols, secure storage, and users' ability to actively manage their data. The second, “user empowerment”, focuses on users’ self-efficacy and capacity for self-management, gauging AI interventions’ inclusion of tools that support users' independence, as well as encouraging the application of skills learned using the tool to real-life contexts in ways that prevent dependency on the tool. Each subdimension is awarded up to 2 points, for a maximum “user agency” dimension score of 4. The fourth dimension, “equity and inclusivity”, is also new and consists of two subdimensions: “cultural sensitivity and inclusivity”, which assesses a tool's capability to engage with users from diverse cultural backgrounds and emphasizes the need for content recognizing cultural and other identity differences; and “bias and fairness”, which addresses the tool's commitment to diversify its training material and remove biases that might impact fairness and equity. Each subdimension is awarded up to 2 points, for a maximum “equity and inclusivity” dimension score of 4. The fifth dimension, “transparency”, remains from PsyberGuide, but now extends beyond data management to include the AI's ownership, funding, business model, development processes, and primary stakeholders. It highlights the importance of providing clear and comprehensive information about operational and business practices, so that users are better equipped to make informed decisions on using such technologies. It also aims to help developers adhere to best practices by disclosing information regarding their tools’ intention and governance. The “transparency” dimension carries a maximum score of 2. Finally, the new sixth dimension of “crisis management” evaluates the safeguarding of user well-being and whether the mental health AI tool provides immediate, effective support in emergencies. It emphasizes comprehensive safety protocols and crisis management features that not only steer users to relevant local resources during crises, but also facilitate follow-through with these resources. The “crisis management” dimension carries a maximum score of 2. Integrating GAI, LLMs and GPTs into mental health care heralds a promising but complicated new era. The promise of these technologies for delivering personalized, accessible and scalable mental health support is immense. So, unfortunately, are the challenges. We developed the FAITA - Mental Health to equip users, clinicians, researchers, and industry and public health stakeholders with a scale for comprehensively evaluating the quality, safety, integrity and user-centricity of AI-powered mental health tools. With an overall score ranging from 0 to 24, this scale attempts to capture the complexities of AI-driven mental health care, while accommodating ongoing evolution in the field and possible adaptations to other medical disciplines. Formal research is required to empirically test its strengths, weaknesses, and most pertinent components.

  PublisherOA PDFDOI

• Physical Activity and Survival in Women With Advanced Breast Cancer

  Cancer Nursing · 2017-07-20 · 67 citations

  article

  BACKGROUND: Several empirical investigations have attempted to characterize the effect of physical activity on cancer mortality, but these investigations have rarely focused on patients with advanced breast cancer. OBJECTIVE: The current study examined the hypothesis that greater physical activity is associated with longer survival among women with advanced breast cancer. METHODS: We conducted a secondary data analysis of a prospective study of 103 patients with stage IV (n = 100) or locally recurrent (n = 3) breast cancer involved in a group psychotherapy trial. Physical activity was assessed at baseline using the Seven-Day Physical Activity Recall questionnaire, and patients were followed until April 1, 2016, at which time 93 of 103 had died. RESULTS: Greater physical activity level at baseline was significantly associated with longer subsequent survival time in a Cox proportional hazards model (hazard ratio [HR], 0.90; 95% confidence interval [CI], 0.84-0.97; P < .01). Engaging in 1 additional hour per day of moderate activity reduced the hazard of subsequent mortality by 23% (HR, 0.77; 95% CI, 0.65-0.92; P < .01). These results remained significant even after controlling for demographic, medical, cancer, depression, and cortisol variables (HR, 0.91; 95% CI, 0.84-0.99; P < .05). CONCLUSIONS: Women with advanced breast cancer who engaged in physical activity for 1 or more hours per day at baseline had an increased likelihood of survival compared with those who exercised less than 1 hour per day. IMPLICATIONS FOR PRACTICE: Nurses should consider recommending moderate physical activity for women with advanced breast cancer. Randomized trials of physical activity interventions for this population are needed.

  PublisherDOI

• Treatment of Aggressive Obsessions in an Adult with Obsessive-Compulsive Disorder

  2015-07-03 · 5 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• Cognitive Restructuring With a Focus on Social Anxiety DisorderCognitive Restructuring With a Focus on Social Anxiety Disorder

  PsycCRITIQUES · 2014-01-01

  article1st authorCorresponding
  PublisherDOI

• Values-Focused Exposure and Response Prevention in the Treatment of Comorbid Schizophrenia and Obsessive-Compulsive Disorder: The Case of "Mr. H"

  Pragmatic Case Studies in Psychotherapy · 2013-02-17 · 2 citations

  articleOpen access1st authorCorresponding

  We describe a subgroup of individuals with comorbid diagnoses of paranoid schizophrenia and obsessive-compulsive (OCD) disorder. It is proposed that traditional exposure and response prevention (ERP) may need to be altered given the unique characteristics of this subgroup. We suggest that providers may approach treatment for OCD from within the context of a patient’s delusion. The case study of "Mr. H," a veteran of the U.S. military diagnosed with schizophrenia and OCD, is described. We propose that attenuating or eliminating obsessions and compulsions may not result in the achievement of a goal stemming from a delusional belief, but may contribute to an increased sense of mastery and independence.

  PublisherOA PDFDOI

Frequent coauthors

• Elias Aboujaoude

  Cedars-Sinai Medical Center

  3 shared

• Eric Neri

  2 shared

• Melanie M. VanDyke

  2 shared

• C. Alec Pollard

  Saint Louis University

  2 shared

• Cheryl Koopman

  Stanford University

  2 shared

• David Spiegel

  Stanford University

  2 shared

• Oxana Palesh

  Virginia Commonwealth University

  1 shared

• Charles Kamen

  University of Rochester

  1 shared