The AI Doctor Will See You Now: Rethinking Healthcare Conversations

Author: Denis Avetisyan

A new wave of artificial intelligence is moving beyond chatbots to create more proactive and insightful interactions between patients and healthcare providers.

Clinical dialogue systems are evolving from modular pipelines and reactive large language models toward an agentic paradigm, wherein the LLM transitions from a passive text predictor to an autonomous controller empowered by strategic planning, persistent memory, and tool execution.

This review categorizes agentic AI systems in healthcare, outlining their core components, knowledge grounding techniques, and potential for workflow automation and improved medical reasoning.

Despite the promise of large language models in healthcare, their inherent reliance on probabilistic prediction often clashes with the need for factual accuracy in clinical settings. This survey, ‘Reinventing Clinical Dialogue: Agentic Paradigms for LLM Enabled Healthcare Communication’, analyzes a shift toward agentic AI, where LLMs function as reasoning engines with deliberate planning and memory. We introduce a novel taxonomy categorizing these emerging paradigms by knowledge source and operational objective-spanning archetypes from ‘Latent Space Clinicians’ to ‘Verifiable Workflow Automators’-and deconstruct their underlying cognitive pipelines. By systematically outlining the trade-offs between creativity and reliability, can these agentic frameworks truly unlock the potential for safe and effective LLM-powered healthcare communication?

The Evolving Landscape of Clinical Intelligence

Early attempts at applying artificial intelligence to clinical settings frequently encountered limitations due to the inherent rigidity of rule-based systems. These systems, while capable of executing pre-defined protocols, struggled with the ambiguity and context-dependent nature of medical diagnosis and treatment. A patient’s presentation rarely conforms neatly to established algorithms; subtle variations in symptoms, individual patient history, and the interplay of multiple conditions demand a level of flexible reasoning that traditional AI found difficult to replicate. Consequently, these early systems often required extensive manual tuning and were prone to errors when faced with cases outside of their narrowly defined parameters, hindering their widespread adoption and practical utility in complex clinical environments.

The advent of Large Language Models (LLMs) is reshaping the possibilities within clinical artificial intelligence, though not without significant hurdles. These models, trained on vast datasets of text and code, demonstrate an unprecedented capacity for understanding and generating human-like text, potentially revolutionizing tasks like diagnosis support, personalized treatment planning, and patient communication. However, simply applying LLMs to healthcare presents critical challenges. Concerns surrounding data privacy, algorithmic bias, and the potential for generating inaccurate or misleading information require novel approaches to model validation, safety protocols, and responsible deployment. Researchers are actively exploring techniques like reinforcement learning from human feedback and the development of specialized, medically-focused LLMs to mitigate these risks and ensure that these powerful tools enhance, rather than compromise, patient care. Successfully integrating LLMs into clinical practice demands a paradigm shift – moving beyond traditional AI methods towards systems that are not only intelligent but also demonstrably safe, reliable, and ethically sound.

Agentic clinical dialogue presents unique challenges when considering perspectives from multiple stakeholders.

Agentic Systems: LLMs as Engines of Reasoning

The Agentic Paradigm represents a shift in Large Language Model (LLM) application, moving beyond purely generative tasks to encompass goal-oriented reasoning. Traditionally, LLMs function as predictive text engines, responding to prompts based on statistical probabilities derived from training data. Agentic LLMs, however, are designed to autonomously define objectives – formulating specific goals based on initial instructions or environmental observations. This involves not only generating text responsive to a prompt, but proactively determining what actions are necessary to achieve a desired outcome. This capability necessitates internal mechanisms for planning, decision-making, and iterative refinement of strategies, effectively transforming the LLM from a passive responder to an active agent capable of executing complex tasks.

Successful implementation of LLM agency relies on two core components: a precisely defined Agency_Objective and access to varied Knowledge_Source materials. The Agency_Objective specifies the ultimate goal the LLM is designed to achieve, providing a clear metric for evaluating performance and guiding decision-making processes. Simultaneously, diverse Knowledge_Source – encompassing databases, APIs, real-time information feeds, and previously processed data – equip the agent with the necessary information to formulate effective plans and adapt to changing circumstances. Without a well-defined objective, the LLM lacks direction; lacking access to relevant knowledge, its actions will be uninformed and potentially ineffective. The breadth and quality of the Knowledge_Source directly impacts the agent’s ability to solve complex problems and achieve its designated Agency_Objective.

Clinical dialogue functions as the primary means by which agentic LLMs acquire patient-specific data essential for informed decision-making. This interface facilitates a two-way exchange, enabling the agent to pose targeted questions, interpret patient responses, and iteratively refine its understanding of the clinical context. The collected information, obtained through natural language interactions, encompasses symptom descriptions, medical history, lifestyle factors, and treatment preferences. Accurate data acquisition via clinical dialogue is critical; the LLM’s subsequent reasoning and action planning are directly dependent on the completeness and veracity of the information provided by the patient.

Our survey reveals that agentic clinical dialogue design balances generative creativity with factual reliability, and operational autonomy with clinical safety, as defined by knowledge source and agency objective.

Knowledge Architecture: Parametric and Non-Parametric Memory

An agent’s memory architecture consists of two primary components: Parametric_Memory and Non_Parametric_Memory. Parametric_Memory refers to the knowledge encoded directly within the weights of a large language model (LLM) during its pre-training and fine-tuning phases. This includes factual knowledge, linguistic patterns, and reasoning abilities acquired from the training data. In contrast, Non_Parametric_Memory comprises external data storage mechanisms, such as vector databases or retrieval-augmented generation (RAG) systems, used to store and retrieve contextual information relevant to the agent’s current task. This external storage allows the agent to access information beyond what is explicitly contained within its model weights, enabling it to handle dynamic or specialized data not encountered during training.

Effective memory management is crucial for clinical applications due to the time-sensitive and complex nature of patient data. In dynamic clinical scenarios, the ability to rapidly retrieve and utilize relevant information – encompassing patient history, current symptoms, lab results, and treatment responses – directly impacts diagnostic accuracy and treatment planning. Insufficient memory management can lead to delayed access to critical data, potentially resulting in errors or suboptimal decisions. Strategies for efficient memory management include prioritizing frequently accessed information, employing indexing techniques for rapid data retrieval, and implementing mechanisms for filtering irrelevant or outdated data, all contributing to improved clinical outcomes and reduced cognitive load on healthcare professionals.

Neuro-Symbolic Architectures integrate Large Language Models (LLMs) with symbolic knowledge representation techniques, such as knowledge graphs and rule-based systems. This integration addresses limitations inherent in LLMs, specifically their susceptibility to hallucination and lack of transparent reasoning. By grounding LLM outputs in structured knowledge, these architectures improve the reliability of generated information and facilitate interpretability; decisions are traceable to defined facts and rules. The approach allows for explicit reasoning steps and verification against a known knowledge base, offering advantages in applications requiring high accuracy and accountability, like medical diagnosis or legal reasoning. Furthermore, symbolic representations enable the incorporation of prior knowledge and constraints, enhancing the LLM’s performance in low-data scenarios.

Employing decomposition, iteration, and both parametric and non-parametric memory, this agent architecture anticipates future states and dynamically adjusts plans to achieve clinical goals, differing from other paradigms through the components highlighted in red.

Three Archetypes for Clinical Application

The VWA_Paradigm, emphasizing safety and accountability, operates by enforcing deterministic workflows. This means each action taken by the agent is pre-defined and predictable, minimizing the potential for unexpected or erroneous behavior. Crucially, all actions are logged and traceable, creating a complete audit trail. This auditability is essential for verifying adherence to clinical protocols, identifying the rationale behind decisions, and facilitating post-hoc analysis for quality control and regulatory compliance. The rigid structure of VWA prioritizes control over flexibility, making it suitable for high-stakes clinical scenarios where predictable performance is paramount.

The Grounded Safety (GS) Paradigm centers on validating agent decision-making through explicit knowledge grounding. This involves verifying each proposed action against a pre-defined, robust evidence base comprised of clinical guidelines, medical literature, and established best practices. The system doesn’t rely on implicit learning or statistical correlations alone; instead, it requires a traceable link between a decision and supporting evidence. This explicit verification process aims to minimize errors, enhance the reliability of clinical support, and provide clinicians with a clear understanding of the rationale behind each recommendation. The evidence base is typically structured as a knowledge graph or similar formal representation to facilitate efficient querying and reasoning.

The Execution Planning (EP) Paradigm centers on enabling an AI agent to independently formulate and implement plans to achieve specified clinical objectives. This is accomplished by utilizing a system of procedural intuition, allowing the agent to sequence actions and adapt to changing circumstances without constant external direction. While this approach offers potential for increased efficiency in clinical workflows, the autonomous nature of the EP Paradigm necessitates rigorous validation procedures. These validations must confirm the safety, accuracy, and reliability of the agent’s planned actions and their subsequent execution before deployment in a clinical setting, as unforeseen consequences could arise from unverified autonomous operation.

The integration of multiple AI agents within a clinical workflow facilitates enhanced problem-solving by distributing cognitive load and leveraging complementary capabilities. This collaborative approach allows agents to specialize in specific tasks – such as data analysis, diagnosis suggestion, or treatment planning – and share information to achieve a more comprehensive and accurate assessment. Evidence suggests that multi-agent systems can surpass the performance of single agents in complex clinical scenarios, reducing errors and improving diagnostic accuracy. Furthermore, collaboration enables agents to address a wider range of clinical challenges and adapt to evolving patient needs, ultimately leading to improved patient outcomes and more efficient healthcare delivery.

The Virtual Ward Agent (VWA) framework combines strategic planning, knowledge-based action execution, and both parametric and non-parametric memory to navigate clinical workflows and safely perform tasks, differentiating itself from other approaches through its focus on verifiable logs and precise tool calls.

The Future of LLM-Powered Healthcare: A New Paradigm

The true power of large language models in healthcare hinges on a shift towards agentic paradigms – systems designed not just to respond to queries, but to proactively pursue goals and manage complex tasks. This represents a move beyond simple information retrieval and towards LLMs functioning as intelligent assistants capable of synthesizing knowledge from diverse sources, formulating plans, and executing them with minimal human intervention. Crucially, realizing this potential requires robust knowledge integration, meaning the ability to seamlessly combine structured data from electronic health records, unstructured clinical notes, and the latest medical research. By moving past isolated responses and embracing these interconnected systems, healthcare can unlock LLMs’ capacity to optimize workflows, personalize treatment plans, and ultimately, enhance patient care in ways previously unimaginable.

The responsible integration of large language models into healthcare demands more than just technical proficiency; it necessitates the implementation of Constitutional AI frameworks. These frameworks function as a codified ethical compass, embedding principles of fairness, transparency, and accountability directly into the model’s decision-making processes. By predefining a “constitution” of values – encompassing aspects like patient privacy, avoidance of bias, and adherence to medical best practices – these systems can proactively mitigate potential harms and ensure equitable access to care. This moves beyond reactive error correction to preventative ethical reasoning, allowing LLMs to self-regulate their responses and justify their conclusions based on established principles, ultimately fostering trust and safe deployment within sensitive medical contexts.

The dynamism of medical science and the individuality of patient care necessitate ongoing development of large language model (LLM) paradigms. Static implementations, however innovative initially, risk obsolescence as new research emerges and clinical best practices evolve. Continuous refinement involves not only incorporating updated medical knowledge – a constant influx of data from trials, studies, and real-world evidence – but also adapting to shifting patient demographics, emerging health crises, and the nuanced complexities of individual medical histories. Successful LLM integration requires a proactive approach to model retraining, iterative feedback loops incorporating clinician expertise, and robust mechanisms for identifying and mitigating potential biases or inaccuracies that may arise over time. This commitment to perpetual improvement ensures these systems remain relevant, reliable, and capable of delivering optimal support throughout the ever-changing landscape of healthcare.

A detailed examination of agentic paradigms within healthcare reveals a landscape ripe for innovation, now systematically categorized through a comprehensive survey of over 300 scholarly papers. This research defines four distinct archetypes – each representing a unique approach to leveraging large language models for medical applications. The study doesn’t simply identify these categories, but also meticulously details the core technical components that underpin each archetype, offering a granular understanding of their capabilities and limitations. By establishing this taxonomy, the work provides a foundational framework for future research and development, facilitating clearer communication and targeted advancements in the rapidly evolving field of LLM-powered healthcare.

A comprehensive analysis of over 300 research papers formed the foundation for a novel two-dimensional taxonomy designed to categorize agentic systems within healthcare. This rigorous review process enabled the identification of core characteristics and functional distinctions among these emerging technologies. The resulting taxonomy doesn’t merely list different approaches, but provides a structured framework for understanding the landscape of LLM-powered healthcare agents, highlighting their strengths and limitations. By mapping these systems along two key dimensions, researchers can now more effectively compare, contrast, and ultimately advance the development of more sophisticated and beneficial applications in clinical settings, fostering innovation and accelerating the integration of AI into modern medicine.

The integration of large language models into healthcare settings holds the potential to fundamentally reshape clinical practice, leading to demonstrably improved patient outcomes and a significant reduction in the pressures faced by medical staff. These advancements aren’t simply about automating tasks; they represent a shift towards more informed, personalized, and proactive healthcare delivery. By rapidly processing vast amounts of medical literature, patient data, and real-time information, these systems can assist clinicians in making quicker, more accurate diagnoses and tailoring treatment plans to individual needs. Moreover, the capacity of LLMs to handle administrative burdens – such as documentation and appointment scheduling – promises to free up valuable time for healthcare professionals, allowing them to focus on direct patient care and complex medical challenges. This ultimately fosters a more efficient and effective healthcare ecosystem, benefiting both providers and those they serve.

The Latent Space Critic (LSC) agent leverages internal reasoning chains, self-correction, and both parametric and non-parametric memory to refine hypotheses and maintain coherence, differing from other paradigms through these internalized processes.

The pursuit of agentic paradigms in healthcare AI, as detailed in the study, echoes a fundamental principle of elegant design – the seamless integration of function and form. These systems, striving to automate workflows and enhance medical reasoning, demand a harmony between knowledge grounding and practical application. As Friedrich Nietzsche observed, “There are no facts, only interpretations.” This resonates with the core idea that LLM agents don’t simply present information, but actively construct meaning from it, shaping clinical dialogue through a lens of interpreted data. The success of these agents hinges on their ability to distill complex information into coherent, actionable insights – a testament to the power of considered design in even the most technical of fields.

Beyond Conversation: Charting the Course

The categorization presented here, while useful, merely illuminates the existing landscape-a landscape still dominated by the echo of pattern recognition, not genuine understanding. The pursuit of ‘agentic’ systems risks becoming synonymous with elaborate prompt engineering, a sophisticated form of mimicry rather than robust medical reasoning. True elegance will not be found in simply chaining large language models, but in architectures where knowledge grounding is intrinsic, not bolted on as an afterthought. The challenge isn’t to build systems that sound capable, but those where computational structure mirrors the nuanced complexity of clinical judgment.

Future work must address the brittleness inherent in current approaches. A system capable of excelling in a curated benchmark is, too often, undone by the unpredictable messiness of real-world data. Workflow automation, while promising, will remain a superficial fix unless paired with mechanisms for continuous learning and adaptation-systems that don’t merely process information, but actively refine their internal models of disease and patient needs.

The ultimate metric won’t be performance on contrived tasks, but the reduction of cognitive load for clinicians-a quiet efficiency that allows them to focus on the uniquely human aspects of care. Beauty scales, clutter does not; the same principle applies to these systems. A truly advanced healthcare AI will not be a noisy proliferation of features, but a distillation of essential knowledge, presented with clarity and precision.

Original article: https://arxiv.org/pdf/2512.01453.pdf

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