Educational Strategies for Clinical Supervision of Artificial Intelligence Use

Abdulnour, R. E., Gin, B., & Boscardin, C. K. (2025).

New England Journal of Medicine, 393(8), 786–797.

https://doi.org/10.1056/nejmra2503232

Human–computer interactions have been occurring for decades, but recent technological developments in medical artificial intelligence (AI) have resulted in more effective and potentially more dangerous interactions. Although the hype around AI resonates with previous technological revolutions, such as the development of the Internet and the electronic health record, the appearance of large language models (LLMs) seems different. LLMs can simulate knowledge generation and clinical reasoning with humanlike fluency, which gives them the appearance of agency and independent information processing. Therefore, AI has the capacity to fundamentally alter medical learning and practice. As in other professions, the use of AI in medical training could result in professionals who are highly efficient yet less capable of independent problem solving and critical evaluation than their pre-AI counterparts.

Such a challenge presents educational opportunities and risks. AI can enhance simulation-based learning, knowledge recall, and just-in-time feedback and can be used for cognitive off-loading of rote tasks. With cognitive off-loading, learners rely on AI to reduce the load on their working memory, a strategy that facilitates mental engagement with more-demanding tasks. However, off-loading of complex tasks, such as clinical reasoning and decision making, can potentially lead to automation bias (overreliance on automated systems and risk of error), “deskilling” (loss of previously acquired skills), “never-skilling” (failure to develop essential competencies), and “mis-skilling” (reinforcement of incorrect behavior due to AI errors or bias). These risks are especially troubling because LLMs operate as unpredictable black boxes; they generate probabilistic responses with low reasoning transparency, which limits assessment of their reliability. For example, in one study, more than a third of advanced medical students missed erroneous LLM answers to clinical scenarios.

The article is paywalled, unfortunately.

Here is my brief summary: 

This article addresses the challenge of supervising medical trainees who use artificial intelligence (AI) tools, particularly large language models (LLMs), in clinical reasoning. It notes that while AI can enhance learning through simulation, knowledge recall, and cognitive off-loading of rote tasks, off-loading complex tasks like clinical reasoning risks automation bias, "deskilling," "never-skilling," and "mis-skilling." LLMs are unpredictable black boxes with low reasoning transparency, making their reliability hard to assess.

The authors argue that critical thinking is foundational to adaptive practice in the age of AI. They propose the DEFT-AI framework (Diagnosis, Evidence, Feedback, Teaching, and recommendation for AI engagement) as a structured approach for educators to promote critical thinking during learner-AI interactions. The framework guides educators to probe the learner's clinical reasoning and AI use, evaluate supporting and opposing evidence, foster self-reflection, provide targeted teaching, and recommend safe AI engagement.

The article also describes two human-AI collaboration behaviors: centaur (strategic division of tasks, with human judgment leading) and cyborg (tight intertwining of user and AI throughout a task). Adaptive AI practice requires shifting between these modes based on task complexity and risk. The authors emphasize promoting AI literacy through evidence-based evaluation of AI tools and outputs, effective prompt engineering, and a "verify and trust" paradigm, concluding that AI interactions are here to stay and must be met with critical thinking and structured educational strategies.

Artificial Intelligence and Psychotherapy: Opportunities, Challenges, and Recommendations

Cooper, S., Ardlan, F., Gavazzi, J., et al. (2026).

A Report of the AI and Psychotherapy Work Group, 

Society for the Advancement of Psychotherapy, 

American Psychological Association, Division 29.

Executive Summary

Psychologists are already using artificial intelligence (AI) to draft progress notes, screen literature, generate case conceptualizations, and rehearse clinical skills with simulated patients. Technology has shaped professional psychology for decades, but no earlier development moved this quickly into so many parts of the work at once. A model can be given every word a patient has spoken and still have nothing at stake in what happens next. That disparity organizes the analysis in this report.

Emerging from the October 2025 SAP Board discussion of mega-level issues and opportunities for the Society for the Advancement of Psychotherapy, the Presidential Work Group on AI and Psychotherapy was established in 2026 by Society President Joshua Swift to examine AI’s implications for psychotherapy practice, supervision, education and training, and research. What follows is guidance from a Society work group. It is not APA policy and not a practice guideline.

The authors of the four domain sections worked independently and drew on different literatures. Their analyses converged: AI can substantially augment psychological work, but it cannot replace the human judgment, relationships, and accountability on which psychotherapy depends.

Six principles follow.

1. AI augments rather than replaces humans.
2. Human accountability remains essential.
3. AI output requires critical evaluation.
4. Implementation must be ethical and safe.
5. Human relationships remain central.
6. Continuous learning and governance are necessary.

Across all four domains the Work Group identified the same risks: automation bias, professional deskilling, cultural and demographic bias, threats to privacy and confidentiality, and overreliance on AI-generated recommendations. Beneath them sits a single concern. AI generates plausible interpretations from patterns in data, while psychotherapy requires understanding a particular person within a particular cultural, historical, and relational context. Pattern recognition can look like clinical understanding and be something else entirely.

The Work Group recommends evaluating any AI application against three questions: whether it enhances or erodes the human relationships the work depends on, whether it preserves psychologist accountability, and whether it respects the patient’s narrative integrity and cultural context. Evaluate a tool before it becomes embedded in practice, because a tool is far easier to decline than to remove.

Four practices follow. Psychologists should reason independently before consulting AI models whenever feasible, verify what AI produces, protect confidentiality in data handling, and review AI-assisted documentation before it enters a record.

Underlying these recommendations is a commitment to human dignity. Persons must not be reduced to data, classifications, or algorithmic predictions. The tools named in this report will be superseded and the studies cited will be replaced by better ones, but the question they raise will not date: what parts of psychological work can be augmented by technology, and what responsibilities cannot be delegated at all.

The entire work group report is here.

Would You Talk to Your Therapist’s AI ‘Twin’?

Isabel Woodford

Bloomberg.com

Originally posted September 11, 2026

Vanessa Marin is impressively responsive. The renowned sex therapist and New York Times bestselling author replies immediately to messages — even in the early morning hours. She doesn’t hesitate to reply when asked what issues she encounters most often in her work. While everyone’s sex life is different, she explains, the dilemmas people bring to her tend to be remarkably familiar.

Mostly it’s “things like whether it’s needy to want to feel desired by your partner,” she writes. “But it’s totally reasonable. You want your partner to want you — that’s not clingy, that’s human.”

This isn’t the real Vanessa Marin. It’s a digital twin she created using Delphi AI, a startup backed by Sequoia Capital that specializes in turning experts into chatbots. The bots, which are trained on material supplied by their human counterparts, including books, websites, videos and social media, have proved popular with celebrities including Arnold Schwarzenegger, who’s created a digital replica of his mind.

The article is here.

Ethical Dilemmas in End-of-Life Care: A Psychological and Anesthetic Perspective

Nmezi J. (2026).

Cureus, 18(6), e111202.

https://doi.org/10.7759/cureus.111202

Abstract

End-of-life care presents some of the most complex and emotionally challenging ethical dilemmas in modern medicine. For anesthetists and clinical psychologists, these dilemmas intersect at the critical juncture where physiological support meets psychological suffering. This editorial advances the novel argument that the psychological distress inherent to the dying process constitutes a specific clinical entity (existential suffering), which requires interdisciplinary management distinct from standard symptom control. It explores the major ethical challenges encountered in end-of-life care, including decisions about withholding and withdrawing life-sustaining treatment, do-not-resuscitate orders, medically administered nutrition and hydration, palliative sedation, and requests for assisted dying. The specific, practical roles of the anesthetist and clinical psychologist are detailed within a collaborative model, moving beyond generalities of "symptom management" and "grief support." Principles of biomedical ethics (autonomy, beneficence, non-maleficence, and justice) provide a framework for analyzing these dilemmas. Culturally specific decision-making frameworks for Nigerian and African contexts, including family-centric consensus models and communication scripts, are introduced. Practical recommendations are offered in the form of specific communication tools and assessment prompts for clinicians navigating these challenging situations and self-care to prevent moral distress and burnout.

The full editorial is here.

Here are some thoughts:

This editorial argues that existential suffering in end-of-life care is a distinct clinical entity requiring interdisciplinary management beyond standard symptom control. It outlines specific roles for anesthetists (e.g., translating physiological decline, managing terminal weaning) and clinical psychologists (e.g., dignity therapy, screening for demoralization) within a collaborative tiered model. Using biomedical ethics principles (autonomy, beneficence, non-maleficence, justice), it analyzes dilemmas like withholding treatment, DNR orders, palliative sedation, and assisted dying. The piece introduces culturally specific frameworks for Nigerian/African contexts, including family-centric consensus models and communication scripts. Practical tools, communication scripts, and self-care strategies are offered to help clinicians navigate these challenges and prevent moral distress.

Navigating increasing complexity in mental health practice: ETHICA-4P, a framework and toolkit to promote reflective skills for ethical clinical decision making

Calia, C., Grant, L., Guerra, C., & Reid, C. (2025).

Reflective Practice, 1–23.

https://doi.org/10.1080/14623943.2025.2586133

Abstract

Clinical practice for mental healthcare can be challenging. Often clinicians must make impactful decisions with little opportunity for reflection or consultation. In response to rich conversations with more than 900 local and global researchers and clinical colleagues (from more than 40 countries) about the increasing complexity of global research and clinical mental health practice, we developed the ETHICA-4P framework and toolkit for supporting clinicians in scaffolded reflection for ethical decision-making in daily practice (https://www.ethical-action.ed.ac.uk/clinical-practice). Ethical clinical interventions sit at the heart of the toolkit but practitioner-participants emphasised that ethical issues also arise during other stages of the clinical journey such as referral processes, assessment and during professional collaboration – with legacy effects long after therapy is complete. To navigate these multilayered challenges, we propose the ETHICA-4P framework and clinical toolkit centred on reflective analysis of ‘4P’s’:

• Place: Social, political, cultural, and historical factors shaping practice.

• People: Stakeholders involved in the ethical conflict, from clients to practitioners.

• Principles: Ethical guidelines and legal frameworks.

• Precedents: How past conflicts were addressed.

This paper reports on the practice-driven, co-design and iterative feedback process that led to the framework and toolkit, as well as spotlighting examples of clinical application.

Here are some thoughts:

U.S. psychologists operate in a high-stakes, high-diversity, high-burnout environment where ethical challenges regularly outpace formal guidance. ETHICA-4P offers a reflective, culturally attuned, practical supplement to the APA Ethics Code—one that supports both client welfare and clinician sustainability, and that can be used immediately in everyday practice, supervision, and training.

AI-enabled therapy platforms: Clinical promise, ethical challenges, and cultural considerations in mental health care

Srivastava, A., & Bita, M. (2026).

Asian Journal of Psychiatry, 123, 105087.

https://doi.org/10.1016/j.ajp.2026.105087

Abstract

The integration of artificial intelligence (AI) into mental health care presents significant opportunities to address gaps in access, affordability, and scalability, particularly in resource-constrained settings such as India. AI-enabled therapy platforms, including conversational agents and virtual therapists, promise personalized and continuous psychological support. However, alongside these clinical advantages, critical ethical and contextual challenges emerge, including concerns related to diagnostic validity, data privacy, informed consent, therapeutic alliance, and cultural misalignment.

This narrative review critically synthesizes contemporary global and regional literature (2019–2025) to examine the clinical potential, ethical risks, and cultural implications of AI-driven psychotherapy. Drawing on a psychologist-centered perspective, the review emphasizes the necessity of rigorous validation, transparent governance, culturally adaptive design, and a collaborative therapist–AI model rather than full automation. Particular attention is given to the Indian sociocultural context, where mental health experiences are deeply embedded in relational, linguistic, and indigenous psychological frameworks.

The review argues that ethically grounded and culturally informed AI-enabled therapy platforms can enhance mental health service delivery when integrated as adjunctive tools within professional care systems. By situating AI innovation within India’s pluralistic psychological landscape, this paper offers a context-sensitive, evidence-informed set of recommendations for responsible and clinically meaningful AI integration in mental health care.

Highlights

• Reviews the clinical applications of AI-enabled therapy platforms within mental health care.

• Examines the ethical risks and governance challenges associated with AI-supported psychotherapy.

• Emphasizes a psychologist-centered model of AI use that incorporates a human-in-the-loop approach.

• Discusses cultural considerations relevant to Asian and Indian contexts.

• Proposes a balanced framework that integrates AI with the therapeutic alliance.

Racial, Ethnic, and Cultural Expressions of Interpersonal Psychological Theory of Suicide (RECEIPTS): An Integrated Model of Structural Racism and Suicide RiskIn Section American Psychologist

Adams, L. B.,  et al. (2025).

American Psychologist, 81(5), 597–610.

https://doi.org/10.1037/amp0001545

Abstract

Suicide risk is a significant public health concern for individuals and communities across the United States, and the rates of suicidality are disproportionately rising for Black Americans. Recent frameworks have articulated the significance of structural racism as a mechanism that may explain the increasing rates of suicide among Black Americans, in part, through its exacerbating effects on salient risk conferring pathways. However, existing scholarship in this area has been developmentally limited in scope and does not specify how structural racism operates as a macrolevel determinant of suicide across the lifespan. To address this gap, we present the Racial, Ethnic, and Cultural Expressions of Interpersonal Psychological Theory of Suicide (RECEIPTS), which highlights how structural racism catalyzes suicide risk for Black Americans. The RECEIPTS model supports and extends tenets of the interpersonal theory of suicide and provides a generalizable and comprehensive framework to understand the complex and intersecting factors that contribute to suicidality among Black Americans across the life course. The RECEIPTS framework highlights structural racism’s impact on suicide risk, offering implications for culturally informed prevention, research, and clinical practice.

Public Significance Statement

The article addresses the interplay between structural racism and suicide risk among Black Americans by introducing the Racial, Ethnic, and Cultural Expressions of Interpersonal Psychological Theory of Suicide. This work highlights and presents an explanatory framework to address a critical gap in the understanding of suicide prevention by emphasizing structural racism as a central factor in delineating mental health disparities among Black Americans.

The basic psychological needs in excellencism and perfectionism: A dual perspective with the need-as-motives and the need-as-nutriments frameworks

Andrade, G., et al. (2025).

Personality and Individual Differences, 

245, 113286.

https://doi.org/10.1016/j.paid.2025.113286

Abstract

Perfectionism has been theorized as a risk factor for psychological need frustration. However, past studies on basic psychological needs often reported ambiguous and unexpected findings for perfectionistic standards. The Model of Excellencism and Perfectionism (MEP) recently distinguished between perfectionistic standards and the pursuit of high yet attainable standards (excellencism). This study investigated their distinct associations with basic psychological needs, using measures taken from the need-as-motives and the need-as-nutriments perspectives. Young adults (n = 305) completed the Scale of Perfectionism and Excellencism and various measures of need-related constructs. A multivariate multiple regression supported the hypothesis that excellencism and perfectionism are differentially linked with psychological needs. Excellencism was positively associated with three approach-oriented motives (need for achievement, affiliation, power) and satisfaction with the need for autonomy, relatedness, and competence. Conversely, pursuing perfectionistic standards was positively linked to two avoidance-oriented motives (e.g., fear of failure and losing control) and frustration with the three basic psychological needs. These findings reconcile research and theories by showing that pursuing perfection is not associated with adaptive psychological needs. Perfectionistic standards are linked to two avoidance-oriented motives (i.e., fear of failure and losing control) and frustration of basic psychological needs when properly distinguished from excellencism.

A pdf is here.

Here are some thoughts:

Clinically, these findings suggest that interventions should promote excellencism (the pursuit of high yet attainable standards) rather than attempting to harness perfectionism as a “healthy” or adaptive trait. Perfectionistic standards offer no additional motivational benefits over excellencism (e.g., no greater need for achievement or need satisfaction) while reliably adding need frustration (competence, relatedness, and autonomy) and avoidance-based fears (failure, losing control). This reframes perfectionism as an unnecessary risk factor rather than a useful drive. Practitioners should also note perfectionists’ antagonistic motive profile, specifically heightened need for power paired with fear of losing control, which may manifest as hypercompetitiveness and interpersonal hostility. Finally, clinicians should reinterpret past research cautiously, as many previously “adaptive” effects attributed to perfectionistic standards were likely artifacts of conflating perfectionism with excellencism.

Prompt Engineering in Clinical Practice: Tutorial for Clinicians

Liu, J., Liu, F., Wang, C., & Liu, S. (2025).

Journal of Medical Internet Research, 27, e72644.

https://doi.org/10.2196/72644

Abstract

Large language models (LLMs), such as OpenAI’s GPT series and Google’s PaLM, are transforming health care by improving clinical decision-making, enhancing patient communication, and simplifying administrative tasks. However, their performance relies heavily on prompt design, as small changes in wording or structure can greatly impact output quality. This presents challenges for clinicians who are not experts in natural language processing (NLP). This tutorial combines prompt engineering techniques tailored for clinical use, covering methods like zero-shot prompting, one-shot prompting, few-shot prompting, chain-of-thought prompting, self-consistency prompting, generated knowledge prompting, and meta-prompting. We provide actionable guidance on defining objectives, applying core principles, iterative prompt refinement, and integration into interoperable electronic health record (EHR) systems. This framework helps clinicians leverage LLMs to improve decision-making, streamline documentation, and enhance patient communication while maintaining ethical standards and ensuring patient safety.

This is an excellent start your journey on prompt engineering and prompt competency.