Healthcare has never lacked data. What’s been missing is a scalable way to put that data to work.

Today, deploying AI models in clinical settings is still slow, expensive, and fraught with deployment risk. Data scientists spend up to 80% of their time on cleaning, preprocessing, and matching models to datasets. Hospitals often dedicate $850K to $1.5M annually for interdisciplinary teams just to operationalize pipelines. The result is that many promising AI solutions never make it to real-world deployment.

We set out to change that.

The Solution: Autonomous Agents

Our framework automates the entire journey from messy uploads to model-ready data and interpretable predictions. At its core are specialized data agents, each handling a crucial step:

• Ingestion Identifier Agent: Detects file types (CSV, XLSX, DICOM, ZIP) and routes them to the correct workflow.

• Data Anonymizer Agent: Scrubs protected health information (PHI) with ≥97% recall and ~99% precision, meeting HIPAA/GDPR standards.

• Feature Extraction Agent: Extracts headers for tabular data or uses our VLM model to infer modality and disease type in images.

• Model–Data Matcher Agent: Aligns datasets with pretrained models using SapBERT embeddings and cosine similarity ≥0.60.

• Preprocessing Recommender Agent: Suggests the right preprocessing steps, switching to auto-mode for datasets >50 MB.

• Preprocessing Implementor Agent: Executes the chosen preprocessing recipe; for images, applies relevant transformations tuned to the model.

• Model Inference Agent: Runs the appropriate model and delivers explainable predictions using SHAP, LIME, or attention maps.

All of this runs seamlessly via Google’s Agent Development Kit (ADK), meaning no manual scripts, no engineering bottlenecks, and no post-hoc cleanup.

Proven Across Clinical Domains

To validate the framework, we ran it across three very different medical domains:

• Geriatric fall prediction: multimodal gait sensor data (229 patients)

• Palliative care anxiety prediction: structured clinical and demographic data (537 patients)

• Colonoscopy polyp detection/classification: 37,896 annotated endoscopy images

In each case, the pipeline:

• Detected file type and anonymized PHI automatically

• Selected the correct model with schema alignment

• Preprocessed data without human intervention

• Produced interpretable outputs clinicians can trust

Why it Matters

By embedding automation, compliance, and explainability into every stage, the framework fundamentally changes what’s possible in clinical AI:

• Efficiency gains: Replaces manual pipelines with autonomous agents, reducing data science effort by up to 80%.

• Cost savings: Cuts annual deployment costs by $850K–$1.5M.

• Privacy by default: Meets HIPAA/GDPR standards with no human review needed.

• Hands-off scalability: Large datasets (>50 MB) trigger fully automated processing.

• Transparency built-in: Clinicians get explainable results with SHAP, LIME, or DETR attention maps, no extra tooling required.

Looking Ahead

Our Agentic AI framework is more than just a research prototype – it’s a blueprint for scalable, trustworthy AI in healthcare. By making automation, compliance, and interpretability the defaults, we lower the barrier for clinical adoption and enable smaller teams to harness AI without costly infrastructure.

The future of clinical AI isn’t about adding more people to pipelines, it’s about building agentic systems that reason, act, and explain on their own.

Read the Full Paper

This post only scratches the surface. Our full paper dives deeper into the architecture, validation benchmarks, limitations, and ethical considerations.

If you’re curious about the future of agent-oriented healthcare AI, we invite you to read the full paper here →