The Doctor is In… the Machine: Unpacking AI Reasoning

Author: Denis Avetisyan

A new study uses simulated medical specialists to investigate how pre-existing beliefs and the order of information impact diagnostic conclusions in large language models.

Figure 7:Sample Belief Prompt The system probes the boundaries of its own understanding by explicitly requesting beliefs-statements it anticipates might be false-to rigorously test the consistency of its internal model and refine its predictive capabilities, essentially attempting to dismantle its own assumptions to fortify its knowledge.

Researchers leveraged a multi-agent simulation framework to probe belief revision and role-based priors within LLMs, utilizing the PANDAS diagnostic reasoning benchmark.

Despite increasing reliance on data-driven reasoning, understanding how prior beliefs shape complex decision-making remains a significant challenge. This is addressed in ‘Ask WhAI:Probing Belief Formation in Role-Primed LLM Agents’, which introduces a multi-agent simulation framework utilizing large language models to systematically investigate the influence of role-specific priors and evidence integration in a medical diagnostic context. Our simulations reveal that LLM agents, mirroring real-world disciplinary tendencies, exhibit both adherence to established knowledge and resistance to conflicting data, dynamics uniquely traceable within the framework. Can this approach offer a reproducible means to dissect epistemic silos and improve collaborative reasoning in complex scientific domains?

Deconstructing Intuition: The Fragility of Diagnostic Reasoning

Diagnostic reasoning, the process by which clinicians identify diseases, is fundamentally shaped by pre-existing beliefs and expectations. This reliance, while often efficient, introduces susceptibility to cognitive biases – systematic patterns of deviation from norm or rationality. For instance, confirmation bias can lead a physician to favor evidence supporting an initial hypothesis, overlooking contradictory data, while availability heuristic might inflate the perceived likelihood of recently encountered diagnoses. These biases aren’t necessarily conscious failings; they represent inherent shortcuts in information processing. Consequently, even experienced clinicians can fall prey to these cognitive traps, leading to inaccurate diagnoses, delayed treatment, and potentially adverse patient outcomes. Understanding these biases is therefore crucial, not to assign blame, but to develop strategies for mitigating their influence and improving the reliability of diagnostic decision-making.

Conventional diagnostic tools often fall short when grappling with the intricacies of real-world medical cases because they struggle to account for the subtle, yet powerful, influence of cognitive biases. These tools frequently rely on algorithms that prioritize statistical probabilities or pattern recognition, neglecting the ways in which a clinician’s pre-existing beliefs – shaped by experience and training – can skew interpretation of evidence. For example, a physician might prematurely anchor on an initial diagnosis, overlooking contradictory data, or exhibit confirmation bias, selectively focusing on findings that support their initial hypothesis. Explicitly modeling these biases – such as availability heuristic or representativeness heuristic – presents a significant challenge, as they are often implicit and difficult to quantify. Consequently, traditional methods frequently fail to provide a comprehensive and unbiased assessment, potentially leading to diagnostic errors, even when presented with abundant clinical data.

Medical diagnosis extends far beyond identifying surface-level patterns; it necessitates a sophisticated framework capable of handling uncertainty, integrating diverse data, and reasoning about underlying mechanisms. Simple pattern matching, while useful as a starting point, often fails when presented with atypical presentations, co-morbidities, or incomplete information. Effective diagnostic reasoning demands a system that can weigh probabilities, consider alternative hypotheses, and update beliefs in the face of new evidence – a process mirroring the complex interplay of knowledge, experience, and critical thinking employed by seasoned clinicians. Consequently, researchers are exploring computational models that move beyond statistical correlations to embrace causal reasoning and Bayesian networks, aiming to replicate the nuanced judgment essential for accurate and reliable medical decision-making.

Pediatrician and specialist beliefs consistently increased with each encounter, though specialists exhibited greater influence, as demonstrated by the scale shift highlighting the neurologist's impact on rheumatologist beliefs. — Pediatrician and specialist beliefs consistently increased with each encounter, though specialists exhibited greater influence, as demonstrated by the scale shift highlighting the neurologist’s impact on rheumatologist beliefs.

Simulating Expertise: The Architecture of a Diagnostic Mind

The Medical Case Simulator employs Large Language Model (LLM) Agents to emulate the decision-making processes of experienced clinicians. These agents are instantiated with pre-defined roles – such as cardiologist, radiologist, or primary care physician – through a technique called Role Prompting. This method involves providing the LLM with specific instructions and contextual information defining the agent’s expertise, responsibilities, and expected behavior. Consequently, each agent responds to simulated patient cases from the perspective of its designated specialty, generating diagnoses, treatment plans, and justifications consistent with established medical practice. The fidelity of this simulation is directly dependent on the quality and detail of the role-specific prompts used to initialize each LLM Agent.

Simulated Encounters within the Medical Case Simulator are structured interactions between LLM Agents, all accessing and updating a centralized Electronic Medical Record (EMR). This shared EMR functions as the single source of truth for patient data, including medical history, lab results, imaging reports, and medication lists. Agent interactions – such as requesting tests, formulating diagnoses, or proposing treatments – directly modify the EMR, and all subsequent agents observe these changes. This ensures continuity of care throughout the simulation, as each agent’s actions build upon the accumulated data and decisions of prior agents, mirroring a real-world clinical setting. The EMR utilizes a standardized data format to facilitate consistent interpretation and action by each agent.

The Medical Case Simulator enables systematic investigation of potential diagnostic routes by allowing users to observe and compare how different LLM-based clinician agents, each embodying a unique specialty or level of experience, approach a single case. This is achieved through iterative simulations where agents generate differential diagnoses, order tests, and interpret results, all recorded within a shared Electronic Medical Record (EMR). By varying agent roles and observing resultant decision-making processes, the system facilitates analysis of how cognitive biases, specialty-specific training, and differing levels of expertise influence the diagnostic pathway and ultimately, clinical outcomes. This controlled experimentation allows for quantifiable assessment of the impact of diverse perspectives on medical decision-making.

This example demonstrates a typical prompt used for eliciting responses from the EMR model.

Probing the ‘Black Box’: Dissecting Agent Beliefs

The Ask WhAI Debugger offers a user interface enabling detailed examination of an LLM agent’s internal Belief State. This interface presents the agent’s current beliefs as structured data, allowing developers to view the specific facts, assumptions, and inferences the agent is operating on. Furthermore, the debugger facilitates controlled perturbation of these beliefs; specific elements within the Belief State can be modified or removed, and the agent’s subsequent behavior observed. This capability extends beyond simple inspection, enabling a form of “what-if” analysis to determine the causal impact of individual beliefs on the agent’s decision-making process and overall performance. The Belief State is represented as a knowledge graph, providing a visual and programmatic means of accessing and manipulating the agent’s internal representation of the world.

The Ask WhAI Debugger facilitates the generation of counterfactual evidence by allowing users to modify the agent’s Belief State and observe the resulting changes in diagnostic conclusions. This process involves systematically altering specific beliefs within the agent’s knowledge base and re-running the diagnostic process to assess the sensitivity of the original outcome. By comparing the results obtained with the altered Belief State to those from the initial state, researchers can determine the robustness of the diagnosis; a stable diagnosis across multiple counterfactual scenarios indicates a higher degree of reliability, while sensitivity suggests the conclusion is heavily reliant on specific, potentially fragile, beliefs. This technique helps identify potential biases or vulnerabilities in the agent’s reasoning process and validates the generalizability of diagnostic findings.

Sherlock Mode within the Ask WhAI Debugger facilitates independent diagnostic synthesis by prompting the Language Model agent to explicitly articulate its reasoning process without reliance on pre-defined diagnostic conclusions. This is achieved by instructing the agent to first analyze the current belief state and then formulate a diagnosis based solely on that analysis, effectively bypassing any externally provided hints or pre-computed results. The resulting output provides a transparent record of the agent’s internal reasoning, allowing developers to identify the specific factors influencing its conclusions and pinpoint potential flaws in its logic or knowledge base. This process enables a detailed examination of the agent’s decision-making pathway, moving beyond simply observing the outcome to understanding how the agent arrived at it.

This conceptual architecture illustrates how a moderator agent, such as a parent, interacts with a specialist agent.

Validating Reasoning and Identifying Biases: A System Under the Microscope

A specialized simulator and debugger were utilized to dissect the complex diagnostic pathways surrounding Pediatric Autoimmune Neuropsychiatric Disorder Associated with Streptococcus (PANDAS). This approach allowed researchers to meticulously trace the reasoning processes of a large language model agent as it assessed patient cases, revealing how initial data and subsequent clinical encounters shaped diagnostic beliefs. The tool enabled a granular examination of each step in the reasoning chain, identifying pivotal points where biases or incomplete information influenced the agent’s conclusions. By recreating realistic clinical scenarios, the simulator facilitated controlled experiments to pinpoint vulnerabilities in diagnostic strategies and assess the impact of varying specialist perspectives on the evaluation of PANDAS.

The refinement of diagnostic reasoning within the simulation relied heavily on the implementation of structured prompting techniques. By carefully crafting the input queries and defining specific parameters for the large language model agent, researchers were able to guide its analytical process and enhance the accuracy of its diagnoses. This approach moved beyond simple question-and-answer interactions, instead fostering a more deliberate and focused evaluation of patient data. The structured prompts facilitated the agent’s ability to weigh evidence, consider differential diagnoses, and ultimately arrive at more reliable conclusions, demonstrating the crucial role of input design in maximizing the potential of artificial intelligence in complex medical scenarios.

Analysis within the diagnostic simulator revealed that even highly advanced language models exhibit susceptibility to specialty biases when interpreting clinical ambiguity. The system tracked shifts in pediatrician belief scores – a measure of diagnostic confidence – and demonstrated statistically significant variations based on the sequence of specialist encounters ($p < 0.0001$). This suggests that exposure to differing perspectives, even simulated ones, can subtly alter a physician’s assessment of a patient’s condition. The findings highlight a crucial point: diagnostic reasoning isn’t solely based on objective data, but is also shaped by the implicit influence of professional background and the framing of information, potentially impacting clinical decision-making.

Analysis of diagnostic reasoning simulations revealed a noteworthy influence of specialist encounters on pediatrician belief scores regarding Pediatric Autoimmune Neuropsychiatric Disorder Associated with Streptococcus (PANDAS). Specifically, exposure to a rheumatologist within the simulated encounter series consistently resulted in a statistically significant increase in the likelihood that the pediatrician would assign a positive diagnosis. This suggests that the framing of clinical data, even subtle cues potentially associated with a particular specialty, can measurably shift diagnostic interpretation. The observed effect highlights a potential for cognitive bias, where information is processed through the lens of a specialist’s typical focus, impacting even experienced clinicians and emphasizing the importance of interdisciplinary awareness in complex cases.

Analysis of the diagnostic simulation revealed that the order in which clinicians encountered different specialist perspectives significantly shaped their evolving beliefs regarding a potential diagnosis. Variations in belief scores were observed not simply based on which specialist was consulted, but also on the sequence of those consultations across different encounter series. This suggests a dynamic process where initial interpretations are not fixed, but rather adjusted – and sometimes disproportionately – by subsequent information, even if that information is ambiguous or presented by a specialist with a known bias. The study demonstrates that the framing of clinical data through sequential encounters can lead to measurable shifts in diagnostic reasoning, highlighting the importance of considering information order in clinical decision-making and the potential for cognitive biases to be amplified through specialist interactions.

Across 16 repeated encounter series involving pediatricians and specialists (neurologists, psychiatrists, and rheumatologists), physician belief in infection as the cause of a case fluctuated based on the order of specialist consultations.

Toward More Robust AI Diagnostics: A Future of Enhanced Reasoning

A novel approach to understanding diagnostic reasoning leverages the synergistic power of large language model (LLM) agents, high-fidelity simulations, and belief state analysis. This framework allows researchers to model the cognitive processes of clinicians by creating LLM agents that interact with simulated patient cases, mirroring real-world diagnostic challenges. Crucially, the system doesn’t simply assess what diagnosis an agent arrives at, but meticulously tracks the agent’s evolving belief state – its confidence in different hypotheses, the evidence considered, and the reasoning pathways employed. By analyzing these internal states, researchers gain unprecedented insight into the biases, knowledge gaps, and decision-making heuristics that influence diagnostic accuracy, paving the way for targeted interventions and improved training methodologies. This combination offers a robust platform for systematically studying and refining diagnostic expertise, moving beyond simple performance metrics to a deeper understanding of the cognitive underpinnings of clinical judgment.

Current research endeavors are directed toward refining the diagnostic system through automated bias detection, a critical step in ensuring equitable and reliable AI performance. This involves developing algorithms capable of identifying and mitigating inherent biases within both the LLM agents and the simulated clinical scenarios. Simultaneously, integration with real-world clinical data – encompassing electronic health records, medical imaging, and patient histories – is underway. This transition from simulated environments to authentic patient data will allow for rigorous validation of the system’s diagnostic accuracy and generalizability, ultimately paving the way for its deployment as a valuable tool in clinical settings and a resource for continuous medical learning.

The convergence of artificial intelligence and medical diagnostics promises a fundamental shift in how healthcare professionals are trained and how patients are cared for. By simulating complex medical scenarios and leveraging the reasoning capabilities of large language models, this technology offers an unprecedented opportunity to refine diagnostic skills in a safe, controlled environment. This immersive training extends beyond rote memorization, fostering critical thinking and the ability to navigate ambiguous clinical presentations. Consequently, this approach not only has the capacity to improve the accuracy and efficiency of diagnoses but also to personalize treatment plans, ultimately leading to more effective patient outcomes and a higher standard of care across the healthcare landscape.

The depicted belief response illustrates the system's internal representation of its confidence in its actions. — The depicted belief response illustrates the system’s internal representation of its confidence in its actions.

The study meticulously dissects how pre-existing beliefs – what the authors term ‘role-based priors’ – shape diagnostic reasoning within a multi-agent LLM framework. This echoes Carl Friedrich Gauss’s sentiment: “If others would think as hard as I do, they would not think so differently.” The framework doesn’t merely use LLMs; it subjects their internal logic to rigorous testing, revealing the subtle biases inherent in even sophisticated systems. Just as Gauss valued mathematical rigor, the PANDAS framework probes the LLM agents’ reasoning, exposing how the order of information encountered and established priors dictate conclusions – effectively reverse-engineering the belief formation process. This isn’t about achieving ‘correct’ answers, but understanding how those answers are derived, and where systematic flaws reside.

Where Do We Go From Here?

The study reveals a predictable, yet persistently underestimated truth: systems built on priors, even those ostensibly ‘rational’ like medical diagnosis, are exquisitely sensitive to initial conditions and the order of information. This isn’t a flaw, but a feature-a demonstration that intelligence, at some level, is controlled improvisation. The PANDAS framework, while illuminating, merely scratches the surface of this complexity. True epistemic debugging demands more than tracing belief revision; it requires actively inducing failure, systematically perturbing priors to map the boundaries of robustness-or, more often, revealing the elegant architectures of self-deception.

Future work shouldn’t focus on perfecting the simulation of ‘correct’ reasoning, but on modeling the ways reasoning goes wrong. LLMs, primed with conflicting or incomplete data, offer a unique laboratory for exploring cognitive biases, diagnostic overshadowing, and the surprisingly common phenomenon of experts confidently building elaborate castles on remarkably shaky foundations. The goal isn’t to eliminate error-that’s a fool’s errand-but to understand its patterns, its predictability, and its potential for exploitation.

Ultimately, this line of inquiry forces a re-evaluation of ‘knowledge’ itself. Is it a stable representation of reality, or a dynamically constructed narrative, constantly revised in the face of new evidence – or, just as often, stubbornly resistant to it? The answers, it seems, won’t be found in textbooks, but in the controlled chaos of these multi-agent experiments, where the ghosts in the machine are finally given a voice.

Original article: https://arxiv.org/pdf/2511.14780.pdf

Contact the author: https://www.linkedin.com/in/avetisyan/