Clinical AI Stack: A Modular Framework

• Clinical AI Stack is a structured, multi-layered framework that defines essential steps from clinical problem identification to regulatory-ready validation.
• It emphasizes high-quality, representative data and methodological rigor to prevent overfitting while ensuring causality in clinical outcomes.
• Modular workflows and hybrid AI approaches facilitate transparent benchmarking, agile improvements, and scalable integration into healthcare settings.

The clinical AI stack designates a structured, multi-layered approach to developing, validating, and integrating artificial intelligence systems into clinical healthcare. It encompasses a series of principled, interdependent stages that ensure AI solutions are grounded in clinical need, built on high-quality and representative data, developed using methodologically appropriate models, validated against rigorous criteria, and ultimately translated into safe, effective, and reliable healthcare practice (Hartskamp et al., 2019). This structuring is critical to meeting the unique scientific, regulatory, and practical requirements of clinical deployment.

1. Six Foundational Principles (“6R’s”) of the Clinical AI Stack

The “Clinical AI Stack” is anchored by six core principles (editor's term: "6R's"), each representing a stage or requirement that must be satisfied for successful biomedical and clinical AI development and deployment:

1. Relevant and Well-Defined Clinical Question: Every project must start with a sharply articulated clinical problem, identified in collaboration with domain experts to ensure direct translatability into improved patient care. Narrow and specific tasks—such as quantifying left ventricular volume from cardiac MRI—are preferred over broad, heterogeneous objectives.
2. Right Data (Representativeness and Quality): Datasets must represent the true clinical population and contain “ground truth” annotations from experts. Rigorous data cleaning, standardization, and (for imaging) valid augmentation are essential. Patient-level data limitations must be recognized; for example, patient cases cannot be artificially expanded as with generic image sets.
3. Ratio of Samples (N) to Variables (P): The AI methodology must be appropriate for the sample-to-variable ratio, ideally ensuring $N \gg P$ (where $N$ is the number of patients and $P$ is the number of features). In clinical data, $N/P$ is often suboptimal, necessitating dimensionality reduction via domain-driven feature grouping, selection, or regularization to prevent overfitting and spurious inferences.
4. Relationship Between Data and Ground Truth (Causality): Inputs must be causally linked to clinical outcomes. Direct, expert-annotated measures (e.g., imaging features from pathologists) are favored over noisy or indirect inputs. The stack promotes models that encode or approximate underlying causal structures—illustrated with Bayesian inference formulas:

$P(H \mid D) = \frac{P(D \mid H)P(H)}{P(D)}$

(where $H$ denotes the clinical hypothesis/outcome and $D$ the observed data).

1. Regulatory Ready (Validation): Systems must be modular, allowing for “freeze-and-validate” cycles. Each module (e.g., a segmentation model) is independently validated and “frozen” before integration, supporting both robust validation and regulatory approval pathways.
2. Right AI Method: The choice of AI architecture is dictated by the problem. Deep learning (such as CNNs) is optimal for image-based tasks, while Bayesian and knowledge-based models are favored for multimodal data or low-sample scenarios. Hybrid strategies—deep learning for feature extraction, Bayesian models for uncertainty and domain knowledge—provide performance and interpretability.

2. Modular Workflow Stages and Architectural Integration

The clinical AI stack organizes the AI workflow into clearly delineated, modular components, each with dedicated roles and methodological best practices:

• Data Ingestion and Preprocessing:

Data is standardized, cleaned, and—if appropriate—augmented. Dimensionality is reduced according to domain knowledge to preserve the $N \gg P$ relationship.

• AI Modeling Layer:

For imaging, modular CNNs are deployed for specific subtasks (segmentation, contouring), while multimodal tasks are handled with pipelines combining deep learning outputs ($f_1, ..., f_k$) and Bayesian meta-models:

$$P(\text{Outcome} \mid f_1, ..., f_k) = \frac{\prod P(f_i \mid \text{Outcome}) P(\text{Outcome})}{P(f_1,...,f_k)}$$

• Validation and Regulatory Interface:

Offline “freeze-and-validate” workflows ensure each component is robustly tested against independent datasets and clinical benchmarks. Transparency is prioritized to streamline regulatory approval and foster clinical trust.

The stack’s modularity enables phased, rigorous validation and facilitates both iterative improvement and regulatory compliance.

3. Handling Data Quality, Representativeness, and Dimensionality

Quality and representativeness of clinical data underpin all other stack components:

• Data Cleaning and Standardization:

Essential to mitigate missing or noisy annotations common in clinical records.

• Expert Annotation:

Ground truth is provided by domain experts; for patient-level imaging data, augmentation is severely restricted compared to generic computer vision.

• Dimensionality Reduction and Regularization:

When $P$ approaches or exceeds $N$, the risk of overfitting and false discovery rises. Dimensionality is reduced through grouping similar features, applying domain-guided selection, and using regularization.

• Task-Specific Data Preparation:

The stack emphasizes that every data curation step must be tailored to the specific clinical question, ensuring direct mapping between input and intended outcome.

4. Causality, Model Selection, and Hybrid Approaches

Ensuring that input features map causally (not merely correlationally) to clinical outcomes is a central principle:

• Modeling Causality:

Bayesian approaches—and, when appropriate, causal inference frameworks—are advocated to address confounding and clinical interpretability.

• Model Selection:

Imaging-centric problems benefit from supervised deep learning, while datasets with limited sample size or high heterogeneity call for probabilistic, Bayesian, or hybrid models.

• Hybridization:

Combining deep learning’s representational power with Bayesian priors or network structures improves robustness and interpretability, especially when data is sparse.

5. Validation Strategy and Regulatory Pathways

The stack prescribes a rigorous, stepwise validation paradigm tailored to clinical requirements:

• Module Freezing:

Once an AI module (e.g., image segmentation) is validated against independent test sets, it is “frozen” to prevent inadvertent drift during system integration.

• Independent Evaluation:

Each module is benchmarked using sensitivity, specificity, and clinical relevance. Validation is conducted using independent datasets representing the real-world population.

• Transparency and Documentation:

The design discourages opacity (“black box” models), demanding full documentation of modeling decisions, validation procedures, and limitations.

• Regulatory Readiness:

By validating and freezing modules sequentially, the stack simplifies regulatory submissions and allows for incremental system certification.

6. Translation to Practical Deployment

The clinical AI stack inherently supports the translation of AI research into real-world healthcare improvements:

• Direct Engagement with Clinical Experts:

Ensures continual alignment between AI outputs and actionable clinical interventions.

• Iterative, Modular Improvement:

Modular design encourages phased, agile development, supporting rapid prototyping, iterative improvement, and the retiring or replacement of underperforming modules without disrupting the entire system.

• Scalability and Adaptability:

The stack structure enables scaling from laboratory development to broad clinical integration, with each component tested and certified for deployment in increasingly complex environments.

• Framework Applicability Across Modalities:

Though motivated by imaging and structured clinical data, the principles generalize to multimodal and longitudinal datasets as well.

7. Summary and Outlook

The clinical AI stack, as articulated in the foundational viewpoint (Hartskamp et al., 2019), constitutes a rigorous methodology for clinical AI development and deployment. By enforcing a structured sequence—clinical problem definition, representative data acquisition, dimensionality control, emphasis on causality, regulatory-ready validation, and methodologically appropriate model selection—it ensures AI systems are scientifically robust and practically deployable in high-stakes healthcare environments. The modular, agile, and transparent approach of the stack is designed not only to optimize technical performance but also to facilitate regulatory approval, clinical trust, and—most critically—tangible improvements in patient outcomes.