# Demo: Healthcare Agent Orchestrator (HAO) for Patient Summarization in Molecular Tumor Boards

arXiv: https://arxiv.org/abs/2509.06602 · HTML: https://mlsanity.com/paper/2509.06602/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Placed in Medical and scientific domain agents, one of 16 directions in LLM agents and tool use.
- Builds on 4 of its 8 references that are in the map.
- First posted on arXiv 8 Sep 2025.

## Placed in

- LLM agents and tool use / [Medical and scientific domain agents](https://mlsanity.com/agents/medical-and-scientific-domain-agents/)

## Builds on (most cited first, 4 of 4)

- [Dense X Retrieval: What Retrieval Granularity Should We Use?](https://mlsanity.com/paper/2312.06648/) (2023)
- [AFaCTA: Assisting the Annotation of Factual Claim Detection with Reliable LLM Annotators](https://mlsanity.com/paper/2402.11073/) (2024)
- [Towards Effective Extraction and Evaluation of Factual Claims](https://mlsanity.com/paper/2502.10855/) (2025)
- [Universal Abstraction: Harnessing Frontier Models to Structure Real-World Data at Scale](https://mlsanity.com/paper/2502.00943/) (2025)

## Built on by (most cited first, 5 of 5)

- [BTReport: A Framework for Brain Tumor Radiology Report Generation with Clinically Relevant Features](https://mlsanity.com/paper/2602.16006/) (2026)
- [Sales Research Agent and Sales Research Bench](https://mlsanity.com/paper/2602.17017/) (2025)
- [FastOMOP: A Foundational Architecture for Reliable Agentic Real-World Evidence Generation on OMOP CDM data](https://mlsanity.com/paper/2604.24572/) (2026)
- [VISTA Architect: A graph database-oriented health AI system demonstrated in multidisciplinary tumor boards](https://mlsanity.com/paper/2606.22692/) (2026)
- [OpenTumorBoard: A Real-World Benchmark of Multidisciplinary Tumor Board Discussion Trajectories](https://mlsanity.com/paper/2609.32810/) (2026)

## Abstract (arXiv)

Molecular Tumor Boards (MTBs) are multidisciplinary forums where oncology specialists collaboratively assess complex patient cases to determine optimal treatment strategies. A central element of this process is the patient summary, typically compiled by a medical oncologist, radiation oncologist, or surgeon, or their trained medical assistant, who distills heterogeneous medical records into a concise narrative to facilitate discussion. This manual approach is often labor-intensive, subjective, and prone to omissions of critical information. To address these limitations, we introduce the Healthcare Agent Orchestrator (HAO), a Large Language Model (LLM)-driven AI agent that coordinates a multi-agent clinical workflow to generate accurate and comprehensive patient summaries for MTBs. Evaluating predicted patient summaries against ground truth presents additional challenges due to stylistic variation, ordering, synonym usage, and phrasing differences, which complicate the measurement of both succinctness and completeness. To overcome these evaluation hurdles, we propose TBFact, a ``model-as-a-judge'' framework designed to assess the comprehensiveness and succinctness of generated summaries. Using a benchmark dataset derived from de-identified tumor board discussions, we applied TBFact to evaluate our Patient History agent. Results show that the agent captured 94% of high-importance information (including partial entailments) and achieved a TBFact recall of 0.84 under strict entailment criteria. We further demonstrate that TBFact enables a data-free evaluation framework that institutions can deploy locally without sharing sensitive clinical data. Together, HAO and TBFact establish a robust foundation for delivering reliable and scalable support to MTBs.