AI Digital Healthcare

🧠 Unlocking the Black Box: How AI Interpretability is Transforming Digital Healthcare Decision-Making

Dr. Brendan O'Brien02 August 2025

🧠 Unlocking the Black Box: How AI Interpretability is Transforming Digital Healthcare Decision-Making

White Paper No. 1 — August 2025

Executive Summary

As healthcare rapidly adopts AI systems for critical decisions affecting millions of patients, the ability to understand how these systems think becomes a matter of safety, ethics, and regulation. Mechanistic interpretability research now offers concrete tools to map internal features and circuits of modern foundation models.

This paper explains why that matters clinically and operationally, lays out an implementation blueprint (edge + cloud), and proposes FHIR-native patterns so explanations travel with clinical data. We also highlight limits: explanation faithfulness, spurious "insight," and the compute tax of deep interpretability — all solvable with disciplined governance and focused scope.

Recent work shows millions of internal "features" in production-grade LLMs can be surfaced and in controlled settings, steered — this is a turning point for accountable clinical AI.

🚨 The $78 Billion Problem

950+ AI/ML-enabled devices. Millions of patients. Limited visibility into decision logic.

The FDA maintains a public list of AI/ML-enabled medical devices cleared or approved; industry analyses counted ~950+ devices by mid-2025, underscoring scale and momentum — and the urgency of trustworthy explanations. The FDA's list is continuously updated and does not itself guarantee interpretability, only market authorization.

💡 What If We Could See Inside AI's Mind?

Anthropic reported mapping millions of concepts (features) inside a deployed Claude Sonnet model using dictionary learning and sparse autoencoders (SAEs) — then demonstrated feature manipulation that predictably changes behaviour in sandboxed experiments. This is the first detailed look into a production-grade LLM's internals, a leap beyond post-hoc saliency.

OpenAI showed sparse autoencoders (SAEs) scaling to 16M latents on GPT-4 activations, evidencing feasibility at very large scales — but "latent count" ≠ "validated interpretable features."

Understanding the Technical Breakthrough

Think of this as creating a massive "dictionary" with 16 million entries to translate the internal "thoughts" (activations) of GPT-4 into understandable human concepts:

  • Latents: Individual features or concepts the SAE identifies (the "words" in our dictionary).
  • 16,000,000: The dictionary size — researchers created an SAE capable of identifying up to 16 million distinct features.
  • GPT-4 activations: The internal signals being analyzed — like eavesdropping on the model's internal monologue.

This enables researchers to:

  • Pinpoint Concepts: Find specific features representing "the Eiffel Tower" or "a bug in Python code", or to localise the clinical reasoning behind diagnosing Parkinson's disease or monitor changing biosensor trends.
  • Improve Safety: With an ability to monitor or control features corresponding to dangerous or biased concepts, models could be further customised to purpose.
  • Control Behaviour: Allowing researchers/ developers/ engineers to "Steer" the model by amplifying or suppressing certain features.

🔬 The Anthropic Breakthroughs: "Seeing AI Think"

🎯 Key Reality: Millions of Interpretable Features Are Extractable (at Cost)

Technical basis: Train sparse autoencoders on intermediate activations to learn a dictionary of features that align with human concepts more cleanly than single neurons (often polysemantic). Prior "Toward Monosemanticity" work has proved the idea on small transformers; Scaling Monosemanticity extends it to Claude 3 Sonnet.

Why clinicians should care: In controlled settings, amplifying a "feature" causally steers a model's output. That causal leverage — not just correlation — is the crucial difference from many post-hoc methods. It opens the door to realtime safety monitors (e.g., detecting deceptive tendencies) and teaching UIs that seek to reveal reasoning hooks, evidence weights, and counterfactuals.

📊 The Clinical Imperative: Why Interpretability Matters Now

🏥 Patient Safety & Accountability

Challenge Current Reality With Interpretability
Diagnostic errors Opaque model behavior; hard to audit Traceable evidence contribution and path-to-conclusion
Legal liability "The AI told me to" ≠ defense Versioned explanation of artifacts + provenance
Training & CQI Black-box answers de-skill Pedagogic traces + counterfactuals for learning loops

Regulatory Momentum is Building

  • FDA (U.S.): Ongoing guidance for AI-enabled device software; transparency and post-market learning are policy priorities (SaMD/AI action plan & 2025 draft guidance * FDA — AI/ML‑Enabled Device List & AI SaMD updates — market scale and policy signals. ([U.S. Food and Drug Administration]))
  • EU: AI Act + MDR: healthcare AI is high-risk; documentation, risk management, and transparency obligations are significant
  • NIST AI RMF 1.0: Distinguishes transparency, explainability, interpretability; provides a practical risk framework widely adopted by industry
  • Australia (TGA): 2025 outcomes report foresees targeted reforms for AI-enabled SaMD and increased compliance activities — expect rising transparency expectations.

🛠 Technical Deep-Dive: How Interpretability Actually Works

🔬 Mechanistic Interpretability 101

Example: Chest Pain Diagnostic Pathway coding in specific features for observability, which are then documentable

{
  "diagnostic_features": {
    "mi_chest_pain_concept": {"feature_id": "X", "effect": "raises MI prior"},
    "ecg_st_elevation": {"feature_id": "Y", "effect": "strong positive evidence"},
    "troponin_elevated": {"feature_id": "Z", "effect": "supportive lab evidence"},
    "pleuritic_descriptor": {"feature_id": "Q", "effect": "negative evidence for MI"},
    "risk_factor_cluster": {"feature_id": "R", "effect": "prior adjustment"}
  }
}

---

Three Key Pillars of Mechanistic Interpretability

  1. Monosemanticity: Strive for one-concept-per-feature; SAEs are a practical step, but perfect monosemanticity remains aspirational at scale. Aim to represent one clear idea- e.g., ST-elevation or Airspace opacity. Monosemanticity = one feature = one concept.
  2. Circuit Analysis: Map interactions among features/heads to chart computation paths ("symptom cluster" → "rule-out branch" → "final hypothesis"). This Research remains at an early stage in large models. This is like tracing a *clinical reasoning chain** This can show how a model thinks step by step Not just the final output. Circuit Analysis = "How features link up"
  3. Feature Attribution (and Limits): Use feature-level attribution rather than raw input saliency; note that classical saliency can fail sanity checks and be unfaithful. Prefer methods with faithfulness tests and counterfactuals. Feature Attribution = which features drove the answer.

Complementary Methods

  • TCAV/CAV for concept-level sensitivity (clinician-defined concepts, e.g., "airspace opacity")
  • Counterfactual explanations for actionable "what would need to change," useful in shared decision-making and recourse — but not a substitute for mechanism

TCAV/CAV slots in as a complementary interpretability method — one that’s particularly friendly to clinicians, because it connects latent features to clinician-defined concepts. Let me walk it through in your setting.

But you say- 🔎 What is TCAV / CAV? CAV = Concept Activation Vector •A direction in the model’s internal activation space that corresponds to a human-defined concept. •You train a simple linear classifier (often logistic regression) to separate examples of the concept vs. random counterexamples in the activation space of a hidden layer. •The separating hyperplane’s normal vector = the Concept Activation Vector.

**TCAV = Testing with CAVs * •Measures how sensitive a model’s prediction is to movement along a CAV. •Intuition: “If I push the activations slightly in the ‘airspace opacity’ direction, does the probability of pneumonia increase?” •Yields a TCAV score = fraction of examples where the gradient of the logit wrt. activations aligns positively with the CAV.

🎯 Real-World Applications: Where This Makes a Difference

🏥 Case Study Emergency Department Triage

The Scenario: A 65-year-old patient presents with chest discomfort, fatigue, and shortness of breath.

Traditional AI System Says: "High priority - possible cardiac event (87% confidence)"

Interpretable AI System Shows:

✅ Age factor activated (Feature #1,247): Male over 60 = +15% risk ✅ Symptom cluster identified (Feature #8,933): Classic angina triad = +40% risk ✅ Risk factor assessment (Feature #2,156): History of hypertension = +12% risk ❌ Reassuring signs noted (Feature #5,441): Normal skin color, no diaphoresis = -5% risk

Interpretable AI output example:

  • Age feature (+15%)
  • Symptom cluster (+40%)
  • HTN history (+12%)
  • Reassuring signs (-5%)
  • ECG latent feature (+45%)
  • Lab latent (troponin) (+25%)

Rationale graph and weights shown with uncertainty bars and "click-through" to supporting tokens/images.

Clinician value: Rapid verification of reasoning, differential diagnosis branches are made explicit, and clear counterfactuals ("no ST elevation → priority drops to moderate").

🧬 Multi-Modal Radiology

Text (patient has a cough), image (CXR shows an opacity), labs (elevated WBC↑) fuse into a pneumonia pathway with exposed feature activations and their relative contributions, traceable to an evidence bundle stored as a FHIR Composition.

*Under the hood, this relies on multimodal features discovered by SAEs (e.g., "Golden Gate Bridge" fired across languages and images — illustrating cross-modal feature binding in Anthropic's experiments).*

⚠️ Limits & Pitfalls You should anticipate

Overcoming the critical Challenges

  1. Faithfulness vs. Plausibility: Some explanations look convincing but don't track the true computation. Use sanity checks and adversarial tests; instrument A/B policies that fail closed if faithfulness falls below thresholds.
  2. Spurious correlations & dataset shift: Expose feature neighborhoods so clinicians can spot non-causal cues (e.g., EHR artifacts). Require prospective validation across sites.
  3. Compute tax: Full-model interpretability can exceed training cost; scope explanations to high-stakes flows, batch heavy analysis, and cache reusable artifacts.

The Solution:

  • Selective Analysis: Focus interpretability on high-stakes decisions *
  • Progressive Disclosure: Show basic reasoning first, detailed analysis on demand
  • Edge Computing: Deploy interpretability models locally for real-time analysis
  • Gaming risk: If features can be steered, guard access. Treat interpretability endpoints as regulated capabilities with audit-backed controls

🧪 Clinical Validation: Make Explanations Clinically Real

The Challenge: How do we know if an AI "feature" actually corresponds to real medical knowledge?

The Approach:

  • Review: Board/College -certified specialists validate feature interpretations
  • Outcome Correlation: Track whether interpretable features predict actual patient outcomes
  • Cross-Validation: Test features across different patient populations and healthcare systems

Use reporting standards that regulators, journals, and clinicians respect: These are companies involved in this space:

🛣 Implementation Roadmap example to improve Observability/ Interpretability

Phase 0 (>= 2025): Readiness & Guardrails

  • Pick 2–3 high-stakes flows (ED chest pain, oncology staging, med-interaction checks)
  • Define trust metrics: explanation coverage, clinician agreement, time-to-decision, post-hoc error catch rate
  • Set governance using NIST AI RMF 1.0 (interpretability ≠ explainability ≠ transparency)

Phase 1 (2025–2026+): Targeted Interpretability for High-Risk Decisions

Technical Implementation:

  • Deploy SAE pipelines for selected high-stakes workflows
  • Implement feature attribution systems with faithfulness monitoring
  • Establish FHIR-based explanation storage and retrieval

Clinical Integration:

  • Pilot with emergency medicine and radiology workflows
  • Establish clinician feedback loops for explanation quality
  • Validate against clinical outcomes and decision quality metrics

Phase 2 (2026–2028): EHR-wide Integration & Real-Time Explanation

  • Explanations persist as FHIR Composition + Provenance, with AuditEvent for every recommendation
  • Integration with major EHR systems (Epic, Cerner, MEDITECH)
  • Real-time explanation generation for clinical decision support

Phase 3 (2028–2030): Personalized, Interpretable Medicine

  • Patient-specific explanation adaptation based on health literacy
  • Cross-institutional explanation portability
  • Advanced circuit analysis for complex multi-step reasoning

🧱 Clinician UX Principles

Design Guidelines

  1. Progressive disclosure: Headline rationale first, details on request
  2. Counterfactual first: "If troponin were normal, recommendation would drop to medium priority"
  3. Faithfulness cues: Visually mark low-confidence attribution; allow "why isn't X considered?" probing via concept tests (TCAV)
  4. Avoid alarm fatigue: Integrate with CDS wisely; leverage DetectedIssue prioritization and role-sensitive displays

User Interface Patterns

Demonstrate clear metrics to the User to build verification and validation. typescript interface ExplanationComponent { headline: string; confidence: number; features: Feature[]; counterfactuals: Counterfactual[]; evidencetrail: EvidenceNode[]; uncertaintyindicators: UncertaintyMarker[]; }

interface Feature { id: string; label: string; weight: number; confidence: number; evidence_source: string; }

⚡ Overcoming Implementation Challenges

🖥 Computational Complexity

Solutions:

  • Selective analysis with progressive disclosure
  • Edge-first design philosophy
  • Batch SAE refresh overnight and warm caches of frequent pathways
  • Anthropic notes full coverage would be cost-prohibitive with today's methods

🧪 Clinical Validation Framework

Study Design:

  • Expert review with blinded vignettes; capture discordances
  • Outcome correlation (A/B with explanations on/off)
  • Cross-site validation to stress test covariate shift
  • Align study reporting with DECIDE-AI and CONSORT-AI/SPIRIT-AI

Metrics:

  • Clinical decision accuracy improvement
  • Time-to-decision changes
  • Clinician confidence and trust scores
  • Patient safety outcomes

🧩 FHIR-Native Patterns for Interpretability

Use Core FHIR Resources for AI Explanations (technical note-code available on request)

Goals: Make every AI recommendation auditable, portable, and longitudinal.

  • Composition — the explanation "bundle" (sections for inputs, context, features, attributions, counterfactuals)
  • Provenance — who/what generated the explanation (model version, weights hash, dataset snapshot)
  • AuditEvent — access and action log (by user, system, and agent), aligned with IHE BALP
  • DetectedIssue — surfaced risks (e.g., drug–drug interactions) with links back to evidence

FHIR Implementation pattern is utilised for AI Interpretability which = an explainability document. We treat interpretability as a first-class clinical document.

Make Composition your “explainability cover page” with these sections (slices)

  • A. Summary & Recommendation – human-readable narrative + the primary AI Observation or DiagnosticReport.
  • B. Inputs & Context – references to DocumentReference/Media (e.g., transcript, imaging), structured Observation inputs, Parameters (prompt, settings) and Consent if applicable.
  • C. Model & Runtime – Device/DeviceDefinition representing the AI model (name/version/weights build), Organization of the vendor, Provenance agent(s).
  • D. Attribution & Evidence – feature attribution artifacts (e.g., SHAP CSV, saliency heatmaps as Media), ArtifactAssessment/Evidence (if using R5; in R4 use DetectedIssue, Observation, or DocumentReference with codes).
  • E. Confidence, Uncertainty, Limits – quantitative Observation(s) for confidence intervals, calibration error, abstention reasons; qualitative Annotation for caveats.
  • F. Safety & Governance – Provenance (who/what/when), AuditEvent (optional), policy tags in meta.security, DetectedIssue for known risks.
  • G. Trace & Reproducibility – Provenance.signature, checksums, seed IDs, dataset snapshot references (DocumentReference), Bundle.identifier for deduplication.

Use standard FHIR resources instead of stuffing everything into extensions:

  • Observation for model outputs and metrics (e.g., risk score, class label, uncertainty).
  • DocumentReference for big artifacts (PDF model card, CSV attributions, .npz tensors).
  • Media for images (saliency overlays, attention maps).
  • Parameters for runtime knobs (temperature, top-p) and input hashes/prompts.
  • Device / DeviceDefinition to model “the AI system” (model family, version, quantization, runtime).
  • Provenance to assert derivation chains and sign the Composition and key outputs.

Only extend when you must Prefer codes (SNOMED CT, LOINC) and Observation.code/component.code. If you need custom attributes (e.g., “explanation faithfulness”), add a narrowly scoped extension with a canonical URL.

Package & sign as a Bundle (document) with Composition first, then all referenced entries. This becomes your single exchangeable explainability artifact.

🏗 Reference Architecture: iOS + Python/Node + Cloud

Edge Computing (iOS/macOS)

Foundation Models Framework Integration:

  • Run primary inference on-device via Apple's Foundation Models framework (WWDC25)
  • Use for local reasoning, token-level evidence pointers, and privacy-preserving pre-screens
  • Encrypt bundled Core ML models at rest
  • When heavy interpretability (SAE probing, circuit tracing) is needed, defer to backend with consent

Privacy Implementation:

  • Ship masked activations or minimal sufficient statistics
  • Leverage Apple's differential privacy and on-device processing guidelines
  • Implement secure enclave for sensitive model components

🚀 The Future of Transparent Healthcare AI

2030 Vision

By 2030, expect AI that teaches while diagnosing, adapts explanations to patient literacy, and negotiates uncertainty openly with clinicians — with FHIR-portable interpretability so insights survive handoffs and audits.

Emerging Capabilities

  • Patient-facing explanations adapted to health literacy levels
  • Cross-institutional explanation transfer via FHIR networks
  • Real-time bias detection and mitigation
  • Collaborative human-AI reasoning interfaces
  • Continuous learning from explanation feedback

📋 Implementation Checklists for Health Organisations

🏥 For Health Systems

  • Audit live AI for explanation coverage and storage as FHIR Composition/Provenance/AuditEvent
  • Add NIST AI RMF governance and a Model/Data Card library
  • Define fail-closed policies when explanation faithfulness degrades (use saliency sanity checks as guard tests)
  • Establish explanation quality metrics and monitoring dashboards
  • Train clinical staff on interpretable AI interfaces and decision workflows
  • Implement patient explanation capabilities for shared decision-making

🏛 For Regulators

  • Interpretability assessment rubric tied to intended use and risk class (MDR/AI Act mapping in EU; TGA reforms in AU; FDA SaMD AI guidance in US)
  • Standards for explanation quality and faithfulness validation
  • Post-market surveillance requirements for interpretable AI systems
  • Auditing frameworks for AI decision transparency

🔬 For AI Developers

  • Integrate SAE pipelines early; log feature activations (de-identified) with OpenTelemetry Integrate interpretability from day one of development.
  • Implement TCAV suites for clinician-defined concepts (= testing with concept activation vectors) Collaborate with clinical experts for feature validation.
  • Ship a Model Card with explicit interpretability limits and monitoring plan
  • Build faithfulness testing into CI/CD pipelines
  • Design explanation APIs with FHIR compatibility from day one, Build interpretability into user interfaces.

💡 Key Takeaways

Strategic Imperatives should be drafted , debated and executions started.

  • Act now: Interpretability will soon be baked into regulatory expectations and procurement
  • Start focused: High-stakes flows; measure decision quality deltas
  • Build it into the data layer: utilise FHIR Composition/Provenance/AuditEvent — with explanations that travel
  • Invest in validation: Use Faithfulness testing and clinical outcome correlation
  • Plan for scale: At Regenemm Healthcare we are using Edge computing and selective explanation to manage computational costs - you should too.

Technical Priorities- make these stick

  • Feature extraction infrastructure using sparse autoencoders
  • Faithfulness validation create well thought out pipelines and continuous monitoring
  • FHIR-native explanation storage and retrieval systems
  • Multi-modal interpretability for complex clinical data, build these pipelines, analyse clinical workflows, build sequence maps that are relatable and informative.
  • Real-time explanation generation with acceptable latency.

🔗 References and Further Reading

Core Research Papers

Regulatory Guidance

Clinical Standards

Technical Implementation

🌟 Conclusion

Healthcare AI is evolving from "can you trust me?" to "let me show you."

Organizations that embed interpretability — technically, clinically, and in data standards — will lead the transformation. The question isn't whether interpretable AI becomes standard; it's whether your systems are ready to explain themselves.

The convergence of mechanistic interpretability research, regulatory momentum, and clinical need creates an unprecedented opportunity to build AI systems that are not just powerful, but trustworthy, auditable, and pedagogically valuable. The technical foundations exist today; the challenge is implementation at scale with appropriate governance and validation.

Regenemm Healthcare 2025 — White Paper No. 1. This document reflects our research and regulatory context as of August 2025 and should be validated against the latest guidance and product documentation.

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