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.