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[2602.19502] Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark
Summary
This article explores how human-guided agentic AI can enhance multimodal clinical prediction, detailing its performance in the AgentDS Healthcare Benchmark and offering insights on feature engineering and model selection.
Why It Matters
As healthcare increasingly relies on AI for clinical predictions, understanding the interplay between human expertise and automated systems is crucial. This study highlights how human guidance can improve AI outcomes, ensuring better patient care and resource management.
Key Takeaways
- Human guidance in AI workflows significantly improves clinical prediction accuracy.
- Domain-specific feature engineering yields better results than automated searches.
- Multimodal data integration requires tailored human judgment for effective analysis.
Computer Science > Artificial Intelligence arXiv:2602.19502 (cs) [Submitted on 23 Feb 2026] Title:Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark Authors:Lalitha Pranathi Pulavarthy, Raajitha Muthyala, Aravind V Kuruvikkattil, Zhenan Yin, Rashmita Kudamala, Saptarshi Purkayastha View a PDF of the paper titled Human-Guided Agentic AI for Multimodal Clinical Prediction: Lessons from the AgentDS Healthcare Benchmark, by Lalitha Pranathi Pulavarthy and 5 other authors View PDF HTML (experimental) Abstract:Agentic AI systems are increasingly capable of autonomous data science workflows, yet clinical prediction tasks demand domain expertise that purely automated approaches struggle to provide. We investigate how human guidance of agentic AI can improve multimodal clinical prediction, presenting our approach to all three AgentDS Healthcare benchmark challenges: 30-day hospital readmission prediction (Macro-F1 = 0.8986), emergency department cost forecasting (MAE = $465.13), and discharge readiness assessment (Macro-F1 = 0.7939). Across these tasks, human analysts directed the agentic workflow at key decision points, multimodal feature engineering from clinical notes, scanned PDF billing receipts, and time-series vital signs; task-appropriate model selection; and clinically informed validation strategies. Our approach ranked 5th overall in the healthcare domain, with a 3rd-place finish on the discharge readiness task. Ablat...