Clinician-Driven Taxonomy

• Clinician-driven taxonomy is a classification system that structures medical knowledge and tasks based on real-world clinical expertise and data patterns.
• It integrates real-world clinical actions and patient states from large datasets into computational models, ensuring AI operates within empirically validated clinical boundaries.
• This approach enhances the safety, interpretability, and personalization of AI systems by aligning recommendations with observed clinical practice.

A clinician-driven taxonomy is a structured classification system for organizing medical concepts, tasks, or risks based on principles, patterns, and input grounded in clinical expertise and real-world practice. Such a taxonomy bridges medical knowledge, clinical decision-making behaviors, and advanced computational methods. In modern research, clinician-driven taxonomies are leveraged both to guide AI and machine learning systems and to structure the evaluation and safe deployment of those systems in complex healthcare environments.

1. Foundations: Clinician-Driven Taxonomy in AI Systems

Clinician-driven taxonomies have emerged as a response to the limitations of rigid guideline-based or purely expert-coded systems in healthcare. Unlike static, top-down taxonomies, the clinician-driven paradigm integrates observational data from large patient cohorts, restricts model actions or outputs to those well-represented in real-world medical practice, and actively incorporates feedback or constraints derived from clinicians.

For example, the AI Clinician system for sepsis management adopts a clinically-constrained action space—recommended intravenous fluid and vasopressor interventions are filtered to include only those observed frequently among clinicians in historical datasets, thereby embedding a taxonomy directly shaped by actual medical behavior and ensuring the AI operates within accepted boundaries (1903.02345).

2. Methodological Implementation: Integrating Clinical Practice into Taxonomies

Implementation of clinician-driven taxonomies in computational systems typically involves several pillars:

• Action and State Space Construction: Actions (e.g., treatments, search terms) and patient states are derived and discretized based on distributions of clinician choices from massive retrospective datasets. Rarely or never chosen interventions are excluded from the actionable set, ensuring models operate within the empirical boundaries of clinical practice.
• Policy Constraints and Interpretability: Reinforcement learning agents, such as in the AI Clinician system, restrict their exploration and recommendations to the empirically validated taxonomy of clinical actions. Furthermore, models may incorporate feature importance analyses (e.g., random forests, SHAP values) to reveal which parameters most strongly influence recommendations or predictions, increasing transparency and aligning model rationale with clinical reasoning.
• Trajectory Inspection and Iterative Refinement: Systematic clinician engagement is emphasized; methods such as trajectory inspection involve clinicians in reviewing cases where model recommendations diverge substantially from plausible or standard care. Through iterative review of recommendations and simulated patient trajectories, clinicians flag implausible decisions, leading to cycle-by-cycle refinement of both the taxonomy and the computational model (2010.04279).

3. Clinical Applications and Impact

Clinician-driven taxonomies support a wide array of clinical applications, including but not limited to:

• Personalized Treatment Strategies: By organizing and evaluating the space of interventions according to real-world data, these taxonomies make it possible to identify optimal, patient-specific treatment regimens. In the management of sepsis, the AI Clinician’s taxonomy guides reinforcement learning to suggest combinations and dosages of therapies that historically maximize patient outcomes, rather than those that optimize surrogate, short-term endpoints.
• Decision Support and Workflow Integration: AI systems that employ clinician-driven taxonomies act as decision support tools, augmenting physician expertise in complex settings. Their design favors incremental, interpretable recommendations over autonomous, potentially unsafe departures from established norms.
• Standardization and Rationalization: Embedding clinical behavior as the backbone of the taxonomy leads to a rationalization of care delivery. Previously unwarranted practice variation is reduced, and systematic analysis may reveal previously unrecognized clusters or phenotypes for different patient subgroups, further informing guideline refinement and health policy.

4. Technical Frameworks and Evaluation

Clinician-driven taxonomies in AI systems are formalized within frameworks such as Markov Decision Processes (MDP). State and action spaces $S$, $A$ represent the discretized clinical states and allowed interventions, filtered for frequency or safety. Policies $\pi(a|s)$ are learned to maximize cumulative long-term rewards $R(s,a)$—such as 90-day survival—in the context of model-based reinforcement learning:

$$\pi^* = \arg\max_\pi \mathbb{E}_\pi \left[ \sum_{t=0}^T \gamma^t R(s_t, a_t) \right]$$

To ensure the taxonomy remains grounded in clinical reality, the set of allowable actions $\mathcal{A}_\text{common} \subset A$ may be defined as:

$$\forall a \in \pi(A|S),\quad a \in \mathcal{A}_\text{common}$$

where only actions observed above a minimum threshold frequency (e.g., 5% of cases) for each state are included. Off-policy estimation methods such as Weighted Importance Sampling (WIS) and bootstrapping are used for performance evaluation in the absence of prospective deployment.

Comparative tables in the literature summarize the methodological distinctions between AI Clinician systems and traditional biomedical approaches, highlighting differences in policy learning, action space construction, and integration with clinical practice.

Aspect	AI Clinician System	Traditional Approaches
Core Method	RL, maximizes long-term outcomes from real-world data	Rule-based, short-term or heuristic targets
Action Space	Clinician-informed, frequency-filtered	Technically driven, possibly abstracted
Interpretability	Feature importance, AI-human policy matching	Often limited to technical surrogate signals
Integration	Decision support within clinical workflow	Typically closed-loop, detached from workflow
Taxonomy Structure	Mirrors clinician treatment patterns	Categorical, static or absent

5. Advantages, Challenges, and Broader Implications

Advantages

• Safety and Acceptability: Limiting recommendations to the known action space increases safety and promotes trust.
• Interpretability: Clear mapping from model decisions to clinical practice fosters oversight and adoption.
• Personalization: Enables stratification and customization of treatment based on patterns discovered from large, diverse populations.

Challenges

• Data Quality and Bias: Retrospective data may be incomplete or biased; policy evaluation techniques are needed to mitigate estimation errors.
• Generalizability: Population, institutional, and measurement differences can affect the universality of the taxonomy.
• Clinical Trust and Governance: Ensuring transparency, safety, and compliance with clinical standards remains a recurring necessity.

A plausible implication is that the success of clinician-driven taxonomies depends heavily on ongoing co-evolution between AI methods and clinical practice, including routine review, validation, and recalibration of both the taxonomy and associated AI systems.

6. Future Developments and Evolution

Future research and deployment of clinician-driven taxonomies anticipate several directions:

• Dynamic Co-evolution: Taxonomies will evolve with accruing clinical evidence and feedback, subject to iterative refinement by both algorithms and human experts.
• Broad Generalization: Frameworks developed in critical care can be extended to a multitude of medical domains—acute, chronic, and preventive.
• Explainable and Auditable AI: Ongoing development of explainable AI techniques will further empower clinicians to verify, audit, and correct AI-augmented taxonomy structures.
• Integration with Health Systems: Embedding clinician-driven taxonomies within electronic health records and national reporting systems may serve as a foundation for standardization and research.

7. Summary

Clinician-driven taxonomy represents the systematic, data-grounded organization and restriction of AI decision-making to the space of actions, interpretations, and outcomes that are meaningful and safe within clinical practice. By aligning AI recommendations with observed clinician behavior, supporting iterative human review, and promoting personalized, explainable care pathways, this paradigm offers a rigorous path toward trustworthy and impactful deployment of AI in medicine. The underlying framework not only addresses technical optimizations but fosters a new model for collaborative medical knowledge production and dissemination.