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

Abstract

Many learners are more facile with the use of large language models in medicine than their supervisors are. The authors provide an approach to clinical supervision that can mitigate the perils and amplify the promise of AI.

The article is paywalled.

Here is how it opens:

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.

Here is a rather detailed summary:

This article provides a practical framework for supervising trainees who are using Artificial Intelligence (AI), specifically focusing on the risks to developing clinical reasoning skills. While the examples are medical, the core concepts of cognitive offloading, deskilling, and critical thinking are directly applicable to clinical psychology and psychotherapy supervision.

The Core Challenge: Balancing Efficiency with Skill Development

The authors argue that AI tools, particularly Large Language Models (LLMs), present a paradox. They can enhance learning through simulation and cognitive offloading of rote tasks, but they also pose significant risks when used to replace, rather than augment, complex clinical reasoning. The central concern is that over-reliance on AI for tasks like diagnosis, case formulation, or treatment planning can lead to:

• Deskilling: Loss of newly acquired clinical reasoning skills.

• Never-skilling: Failure to develop essential competencies in the first place.

• Mis-skilling: Reinforcement of incorrect or biased clinical behavior due to flawed AI output.

This is especially dangerous because AI operates as a "black box," generating persuasive but potentially biased or inaccurate responses without transparent reasoning.

The "Leap of Faith" and the Supervisor's Role

A key concept is the AI interaction: a moment when a clinician receives an AI-generated judgment that cannot be fully retraced, requiring a "leap of faith" to trust it. The supervisor's job is to teach trainees to recognize these moments and pause for critical evaluation, rather than passively accepting the output.

The supervisor-learner dynamic may be inverted, as trainees are often more adept with the technology. The article reframes this as a shared learning opportunity, where supervisors and learners co-explore AI's capabilities and limitations in a "community of practice."

The DEFT-AI Framework for Supervision

The authors propose a structured, stepwise approach called DEFT-AI (Diagnosis, Evidence, Feedback, Teaching, and AI Recommendation) to turn an AI interaction into an educational moment that builds critical thinking. Here is how it can be applied in a psychology context:

• Diagnosis, Discussion, and Discourse: The supervisor asks the trainee to verbalize their own clinical reasoning before revealing the AI's input. Questions include: "What is your formulation and differential? What prompts did you use with the AI? Did the AI's output change your thinking, and how?"

• Evidence: The supervisor probes the trainee’s ability to support their clinical reasoning with psychological theory, evidence-based practice, and knowledge of the patient’s unique context. Simultaneously, the supervisor probes AI literacy: "How do you think the AI reached this conclusion? What are the known biases or weaknesses of this tool for this specific clinical question?"

• Feedback: The supervisor guides the trainee in self-reflection on gaps in their clinical knowledge, potential biases, and their interaction with the AI tool.

• Teaching: The supervisor provides targeted teaching to address identified gaps, reinforcing foundational clinical reasoning and AI literacy.

• AI Engagement Recommendation: The supervisor makes a clear recommendation on the appropriate future use of AI for the trainee, ranging from supervised practice to independent use with self-monitoring.

Cyborg vs. Centaur: Two Styles of AI Use

The article identifies two distinct collaboration styles that supervisors should help trainees recognize and shift between:

• Centaur Strategy: A strategic division of labor. The human delegates specific, well-defined tasks to AI (e.g., drafting psychoeducational materials, summarizing session notes) but relies on their own clinical judgment for core tasks like diagnosis and treatment planning. This is the preferred strategy for high-risk tasks.

• Cyborg Strategy: A tight, iterative interweaving with AI for every step of a task (e.g., co-constructing a case formulation by prompting, correcting, and refining with an LLM). This is efficient for low-risk, creative, or well-defined tasks but carries a high risk of deskilling.

Adaptive AI practice is the ability to fluidly switch between centaur, cyborg, and AI-independent modes based on the complexity and risk of the clinical task at hand.

Promoting AI Literacy: The "Verify and Trust" Paradigm

Ultimately, the goal is to foster a "verify and trust" mindset over blind trust. Supervisors must teach two key skills:

1. Critical Appraisal of AI Output: Trainees must independently acquire and appraise evidence (e.g., clinical guidelines, therapeutic literature) for a clinical question and compare their own conclusions to the AI's output before accepting it.
2. Effective Prompting: Trainees need to learn how to craft specific, context-rich, and unbiased prompts. Techniques like asking the AI to "think out loud" (chain-of-thought prompting) can expose the AI's reasoning and facilitate critical assessment.

For psychologists and clinical supervisors, this framework offers a clear, theory-grounded method to proactively integrate AI into supervision while safeguarding the development of independent, adaptive, and critical clinical judgment in trainees.

Living intelligence toward human-level models (HLMs) via Organoid-AI integration

Bai, L., Wang, J., Lai, Y., & Su, J. (2025).

EngMedicine, 2(4), 100106.

https://doi.org/10.1016/j.engmed.2025.100106

Abstract

The convergence of brain organoids and artificial intelligence (AI) has driven the development of organoid intelligence (OI), a new paradigm for constructing human-level cognitive models. Brain organoids derived from human stem cells exhibit self-organizing neural networks with dynamic activity and plasticity, offering a biologically based alternative to conventional AI systems. The integration of living networks with computational frameworks enables the design of closed-loop systems that combine the adaptability of biological tissues with the scalability and interpretability of AI. This approach not only provides a novel model for studying human cognition but also opens new pathways for biologically inspired computing. The development of such hybrid systems requires interdisciplinary collaboration among stem cell biology, bioengineering, neuroscience, and machine learning. The long-term goal is to establish biohybrid platforms capable of learning, memory formation, and task-specific computation, thereby redefining our understanding of intelligence and enabling the next generation of neurotechnologies.

Highlights

• Organoid Intelligence (OI) combines brain organoids and AI.

• OI creates biologically embodied models for human-level cognition.

• Biohybrid platforms can learn, remember, and perform computations.

• OI requires interdisciplinary collaboration for development.

Here are some general thoughts:

We are witnessing the infancy of true synthetic biological intelligence. While current applications are constrained to pattern recognition and disease modeling, the long-term trajectory completely disrupts the binary view of technology as "artificial" and biology as "natural." It forces tech developers and ethicists alike to confront a reality where the next generation of advanced intelligence might not be coded, but grown.

New 3D device harnesses living brain cells for computing

Princeton University

Office of Engineering

Originally posted April 27, 2026

Princeton researchers have combined brain cells and advanced electronics into a 3D device that can be programmed to recognize patterns using computational techniques.

Past attempts at using brain cells to do computation have relied on 2D cultures grown in a petri dish or 3D clusters that are probed and monitored from outside. The Princeton device takes a different approach, working from the inside out.

Using advanced fabrication techniques, the team created a 3D mesh made of microscopic metal wires and electrodes supported by a thin epoxy coating. Because the coating is so thin, it has just the right amount of flexibility to interface with the soft neurons that grow around it. The team used the mesh as a scaffold to culture tens of thousands of neurons into a vast 3D network that can be used to do computation.

The info is here.

The research paper is here.

Here are some thoughts:

Princeton University researchers have developed an innovative 3D device that integrates roughly 70,000 living biological neurons with advanced electronics to perform computational tasks, such as recognizing spatial and temporal electrical pulse patterns. Published in Nature Electronics, the study details a novel "inside-out" approach where an ultra-thin, flexible epoxy-coated mesh of microscopic metal wires and electrodes serves as a scaffold for the soft brain cells to grow around, allowing scientists to record and stimulate electrical activity at an unprecedentedly fine scale. By tracking and manipulating these neural connections over a six-month period, the team successfully trained an algorithm to distinguish between different pattern inputs, demonstrating a crucial first step toward creating highly energy-efficient 3D biological neural networks that could eventually alleviate the immense power demands of modern AI while providing deeper insights into neuroscience and neurological diseases.

Benchmarking Large Language Models Against Psychiatry Residents Using Traditional Institutional Assessments

Sethi, M. I. S. et al. (2026).

Indian Journal of Psychological Medicine, 

02537176261435658.

https://doi.org/10.1177/02537176261435658

Background:Artificial intelligence (AI) models demonstrate remarkable capabilities in healthcare applications, yet their performance compared to medical trainees in psychiatric education remains unexplored. This study evaluated the comparative performance of large language models (LLMs) against first-year psychiatry residents in standardized assessments at a premier Indian medical educational institute.

Methods:For this study, the already-scored answer sheets for Theory Papers I and II, as well as unmanned, non-interactive Objective Structured Clinical Examinations (OSCEs) with image-based tasks, from all 25 first-year psychiatry residents (March 2024 exam) were obtained from the examination section of the institute. The same question papers were then uploaded into three AI models (ChatGPT−3.5, Gemini Advanced, and Claude Sonnet). Four blinded faculty members evaluated the responses generated by the AI models. Final, the scores of the AI models and psychiatry residents were analyzed for comparison. Statistical analysis employed Kruskal–Wallis tests with post hoc Mann–Whitney U comparisons.

Results:AI models outperformed residents in theoretical assessments. In Paper I (theory), AI models achieved mean scores (standard deviation) of Claude Sonnet 67.88 (10.63), ChatGPT−3.5 70.38 (3.95), and Gemini Advanced 71.25 (3.86), compared to residents’ 58.0 (2.58). Paper II (theory) assessments showed even larger gaps, with AI models scoring Claude Sonnet 72.88 (3.77), ChatGPT−3.5 71.0 (3.56), and Gemini Advanced 69.63 (12.86), compared to residents’ 50.96 (2.49). OSCE performance patterns differed markedly. Paper I OSCEs showed equivalent performance: AI: 13.0; residents’: 13.16 (1.49), while Paper II OSCEs revealed variable results: Claude Sonnet excelled at 20.0 (1.41), but ChatGPT−3.5 underperformed at 15.0 (0.50), compared to residents at 16.6 (1.55). Inter-rater reliability coefficients remained excellent ( intraclass correlation coefficients [ICC]: 0.810–0.934).

Conclusions:While AI demonstrated superior theoretical knowledge, equivalent or variable practical skills performance reveals fundamental limitations in clinical reasoning and contextual understanding. These findings necessitate reconceptualizing psychiatric education to emphasize uniquely human competencies while leveraging AI’s capabilities for knowledge synthesis.

Here are some thoughts:

This study compared three large language models (LLMs) to first-year psychiatry residents using real institutional exams in India. The LLMs consistently outperformed residents on theoretical assessments (by 17–43%) but showed equivalent or inconsistent performance on practical OSCEs, revealing critical gaps in clinical reasoning and cultural contextualization. The authors conclude that psychiatric education should shift focus toward uniquely human skills like empathy and judgment, while using AI as a tool for knowledge synthesis.

Adversarial AI reveals mechanisms and treatments for disorders of consciousness

Toker, D. et al. (2026).

Nature Neuroscience, 29(4), 964–977.

https://doi.org/10.1038/s41593-026-02220-4

Abstract

Understanding disorders of consciousness (DOC) remains one of the most challenging problems in neuroscience, hindered by the lack of experimental models for probing mechanisms or testing interventions. Here, to address this, we introduce a generative adversarial artificial intelligence (AI) framework that pits deep neural networks—trained to detect consciousness across more than 680,000 ten-second neuroelectrophysiology samples and validated on 565 patients, healthy volunteers and animals—against interpretable, machine learning-driven neural field models. This adversarial architecture produces biologically realistic simulations of both conscious and comatose brains that recapitulate empirical neurophysiological features across humans, monkeys, rats and bats. Without explicit programming, the AI model retrodicts known DOC responses to brain stimulation and generates testable predictions about the mechanisms of unconsciousness. Two such predictions are validated here: selective disruption of the basal ganglia indirect pathway, supported by diffusion magnetic resonance imaging in 51 patients with DOC, and increased cortical inhibitory-to-inhibitory synaptic coupling, supported by RNA sequencing of resected brain tissue from 6 human patients with coma and a rat stroke model. The model also identifies high-frequency stimulation of the subthalamic nucleus as a promising intervention for DOC, supported by electrophysiological data from human patients. This work introduces an AI framework for causal inference and therapeutic discovery in consciousness research, as well as in complex systems more broadly.

Here are some thoughts:

This work gives psychologists a more concrete, brain-circuit-level understanding of why coma and related states happen, and points to a specific, testable new treatment approach, moving the field beyond “we don’t know what’s happening inside” toward identifiable mechanisms that may one day guide rehabilitation and family education.

Automation bias: a systematic review of frequency, effect mediators, and mitigators

Goddard, K., Roudsari, A., & Wyatt, J. C. (2011).

Journal of the American Medical

Informatics Association, 19(1), 121–127.

https://doi.org/10.1136/amiajnl-2011-000089

Abstract

Automation bias (AB)—the tendency to over-rely on automation—has been studied in various academic fields. Clinical decision support systems (CDSS) aim to benefit the clinical decision-making process. Although most research shows overall improved performance with use, there is often a failure to recognize the new errors that CDSS can introduce. With a focus on healthcare, a systematic review of the literature from a variety of research fields has been carried out, assessing the frequency and severity of AB, the effect mediators, and interventions potentially mitigating this effect. This is discussed alongside automation-induced complacency, or insufficient monitoring of automation output. A mix of subject specific and freetext terms around the themes of automation, human–automation interaction, and task performance and error were used to search article databases. Of 13 821 retrieved papers, 74 met the inclusion criteria. User factors such as cognitive style, decision support systems (DSS), and task specific experience mediated AB, as did attitudinal driving factors such as trust and confidence. Environmental mediators included workload, task complexity, and time constraint, which pressurized cognitive resources. Mitigators of AB included implementation factors such as training and emphasizing user accountability, and DSS design factors such as the position of advice on the screen, updated confidence levels attached to DSS output, and the provision of information versus recommendation. By uncovering the mechanisms by which AB operates, this review aims to help optimize the clinical decision-making process for CDSS developers and healthcare practitioners.

Here are some thoughts:

This systematic review examines the frequency, mediators, and mitigators of automation bias, which is the tendency for users to over rely on automated decision support systems as a heuristic replacement for vigilant information seeking and processing. The authors reviewed 74 studies across healthcare, aviation, and human computer interaction fields and found that automation bias is a robust effect, with a meta analysis showing that erroneous clinical decision support system advice increased the risk of incorrect decisions by 26%. Key mediators include user factors such as cognitive style, trust, confidence, and task specific experience, as well as environmental factors like workload, task complexity, and time pressure that strain cognitive resources. Mitigators include increasing user accountability, providing training, updating confidence levels alongside advice, positioning advice less prominently on screen, and presenting information rather than direct recommendations. The review concludes that automation bias and the related concept of automation induced complacency represent distinct but overlapping attentional phenomena that can introduce new errors even when decision support systems improve overall performance, highlighting the need for careful design and implementation strategies.

Transforming clinical reasoning—the role of AI in supporting human cognitive limitations

Greengrass C. J. (2026).

Frontiers in digital health, 7, 1715440.

https://doi.org/10.3389/fdgth.2025.1715440

Abstract

Clinical reasoning is foundational to medical practice, requiring clinicians to synthesise complex information, recognise patterns, and apply causal reasoning to reach accurate diagnoses and guide patient management. However, human cognition is inherently limited by factors such as limitations in working memory capacity, constraints in cognitive load, a general reliance on heuristics; with an inherent vulnerability to biases including anchoring, availability bias, and premature closure. Cognitive fatigue and cognitive overload, particularly apparent in high-pressure environments, further compromise diagnostic accuracy and efficiency. Artificial intelligence (AI) presents a transformative opportunity to overcome these limitations by supplementing and supporting decision-making. With AI's advanced computational capabilities, these systems can analyse large datasets, detect subtle or atypical patterns, and provide accurate evidence-based diagnoses. Furthermore, by leveraging machine learning and probabilistic modelling, AI reduces dependence on incomplete heuristics and potentially mitigates cognitive biases. It also ensures consistent performance, unaffected by fatigue or information overload. These attributes likely make AI an invaluable tool for enhancing the accuracy and efficiency of diagnostic reasoning. Through a narrative review, this article examines the cognitive limitations inherent in diagnostic reasoning and considers how AI can be positioned as a collaborative partner in addressing them. Drawing on the concept of Mutual Theory of Mind, the author identifies a set of indicators that should inform the design of future frameworks for human–AI interaction in clinical decision-making. These highlight how AI could dynamically adapt to human reasoning states, reduce bias, and promote more transparent and adaptive diagnostic support in high-stakes clinical environments.

Here are some thoughts:

This article examines how artificial intelligence can support clinical diagnostic reasoning by compensating for inherent human cognitive limitations such as limited working memory capacity, cognitive load, reliance on heuristics, and susceptibility to biases like anchoring and premature closure. The author integrates cognitive psychology concepts including dual process theory (System 1 intuitive pattern recognition versus System 2 analytical reasoning), cognitive load theory, and Bayesian reasoning to analyze how AI systems can reduce cognitive burden, provide external schema repositories, offer transparent explainable outputs, and support metacognitive monitoring. While AI offers advantages in processing vast data streams, maintaining multiple hypotheses, and performing consistently without fatigue, the review acknowledges current limitations of large language models including poor probabilistic reasoning and potential for algorithmic or transferred bias. The article concludes that AI should function as a collaborative partner within a Mutual Theory of Mind framework, enhancing rather than replacing human judgment, provided that ethical standards and clinician training keep pace with technological development.

Using AI-Based Virtual simulated patients for training in psychopathological interviewing: Cross-Sectional Observational study.

García-Torres, D., et al. (2025).

JMIR Medical Education, 11, e78857.

https://doi.org/10.2196/78857

Abstract

Background:

Virtual simulated patients (VSPs) powered by generative artificial intelligence (GAI) offer a promising tool for training clinical interviewing skills; yet, little is known about how different system- and user-level variables shape students’ perceptions of these interactions.

Objective:

We aim to study psychology students’ perceptions of GAI-driven VSPs and examine how demographic factors, system parameters, and interaction characteristics influence such perceptions.

Methods:

We conducted a total of 1832 recorded interactions involving 156 psychology students with 13 GAI-generated VSPs configured with varying temperature settings (0.1, 0.5, 0.9). For each student, we collected age and sex; for each interview, we recorded interview length (total number of question–answer turns), number of connectivity failures, the specific VSP consulted, and the model temperature. After every interview, students provided a 1-10 global rating and open-ended comments regarding strengths and areas for improvement. At the end of the training sequence, they also reported perceived improvement in diagnostic ability. Statistical analyses assessed the influence of different variables on global ratings: demographics, interaction-level data, and GAI temperature setting. Sentiment analysis was conducted to evaluate the VSPs’ clinical realism.

Results:

Statistical analysis showed that female students rated the tool significantly higher (mean rating 9.25/10) than male students (mean rating 8.94/10; Kruskal-Wallis test, H=8.7; P=.003). On the other side, no significant correlation was found between global rating and age (r=0.02, 95% CI –0.03 to 0.06; P=.42), interview length (r=0.04, 95% CI –0.2 to 0.10; P=.18), or frequency of participation (Kruskal-Wallis test, H=4.62; P=.20). A moderate negative correlation emerged between connectivity failures and ratings (r=–0.26, 95% CI –0.41 to –0.10; P=.002). Temperature settings significantly influenced ratings (Kruskal-Wallis test, H=6.93; P=.03; η²=0.02), with higher scores at temperature 0.9 compared with 0.1 (Dunn’s test, P=.04). Concerning learning outcomes, self-perceived improvement in diagnostic ability was reported by 94% (94/100) of students; however, final practical examination scores (mean 6.67, SD 1.42) did not differ significantly from those of the previous cohort without VSP training (mean 6.42, SD 1.56). Sentiment analysis indicated predominantly negative sentiment in GAI responses (median negativity 0.8903, IQR 0.306-0.961), consistent with clinical realism.

Conclusions:

GAI-driven VSPs were well-received by psychology students, with student gender and system-level variables (particularly temperature settings and connection stability) shaping user evaluations. Although participants perceived the training as beneficial for their diagnostic skills, objective examination performance did not significantly differ from the previous cohort. However, lack of randomization limits the generalization of the results obtained, and further experiments are required.

Here are some thoughts:

This study is important because it demonstrates a promising application of AI in clinical training, using generative AI-powered virtual simulated patients to help psychology students practice psychopathological interviewing in a safe, low-stakes environment. The platform was highly rated by students and 94% reported meaningful improvement in their ability to identify clinically relevant symptoms. Higher AI temperature settings, which produce more natural and varied responses, were associated with greater student satisfaction, while connectivity failures reduced ratings, underscoring the importance of technical reliability. Although students found VSP-based sessions more challenging than traditional paper cases, final exam scores were comparable between cohorts, suggesting the AI simulation provides a more realistic learning experience rather than a less effective one. For practicing psychologists and educators, this study offers early empirical support for integrating AI-driven patient simulation into clinical training, while highlighting the need for randomized studies and careful calibration of AI parameters before broad adoption.

The moon, the ghetto and artificial intelligence: Reducing systemic racism in computational algorithms.

Fountain, J. (2022).

Government Information Quarterly, 39(2), 101645.

https://doi.org/10.1016/j.giq.2021.101645 

Abstract

Computational algorithms and automated decision making systems that include them offer potential to improve public policy and organizations. But computational algorithms based on biased data encode those biases into algorithms, models and their outputs. Systemic racism is institutionalized bias with respect to race, ethnicity and related attributes. Such bias is located in data that encode the results and outputs of decisions that have been discriminatory, in procedures and processes that may intentionally or unintentionally disadvantage people based on race, and in policies that may discriminate by race. Computational algorithms may exacerbate systemic racism if they are not designed, developed, and used–that is, enacted–with attention to identifying and remedying bias specific to race. Advancing social equity in digital governance requires systematic, ongoing efforts to assure that automated decision making systems, and their enactment in complex public organizational arrangements, are free from bias.

Highlights

• Computational algorithms are powerful tools but may replicate biases.

• Biases, including systemic racism, in underlying data bias algorithms

• Automated decision making systems that discriminate harm people.

• Careful scrutiny of data, processes, variables and algorithms may reduce bias.