Evidence map›Paper›PMID 42466075›Full record

ArticleMachine learning. Health2026

Autonomous radiotherapy planning via agentic orchestration using a multimodal TPS-integrated compound AI platform.

Austen Matthew Maniscalco, Yang Kyun Park, Sean J Domal, Mu-Han Lin, Sanda Harabagiu, Steve B Jiang, Dan Nguyen

Abstract read
In one paragraph

Article in Machine learning. Health, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Austen Matthew ManiscalcoMedical Artificial Intelligence and Automation (MAIA) Lab, UT Southwestern Medical Center, Dallas, TX, United States of America.ORCID https://orcid.org/0000-0001-7156-3239
Yang Kyun ParkDepartment of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States of America.ORCID https://orcid.org/0000-0002-3784-2653
Sean J DomalDepartment of Radiation Oncology, UT Southwestern Medical Center, Dallas, TX, United States of America.ORCID https://orcid.org/0000-0002-5988-5240
Mu-Han LinMedical Artificial Intelligence and Automation (MAIA) Lab, UT Southwestern Medical Center, Dallas, TX, United States of America.ORCID https://orcid.org/0000-0002-7992-5465
Sanda HarabagiuUniversity of Texas at Dallas, Richardson, TX, United States of America.ORCID https://orcid.org/0000-0002-8186-1501
Steve B JiangMedical Artificial Intelligence and Automation (MAIA) Lab, UT Southwestern Medical Center, Dallas, TX, United States of America.ORCID https://orcid.org/0000-0002-3083-6752
Dan NguyenMedical Artificial Intelligence and Automation (MAIA) Lab, UT Southwestern Medical Center, Dallas, TX, United States of America.ORCID https://orcid.org/0000-0002-9590-0655

Funding

Human-like automated radiotherapy treatment planning via imitation learningR01CA254377 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI JIA, XUN, JIANG, STEVE BIN · 2021 to 2025
$2.9M
Artificial Intelligence-Based Quality Assurance for Online Adaptive RadiotherapyR01CA258987 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI Steve Bin Jiang, Dan Nguyen · 2022 to 2026
$2.8M
Intelligent treatment planning for cancer radiotherapyR01CA237269 · NCI · UT SOUTHWESTERN MEDICAL CENTER · PI JIA, XUN, JIANG, STEVE BIN · 2019 to 2023
$2.5M
NCI NIH HHS R01 CA237269NCI NIH HHS R01 CA254377NCI NIH HHS R01 CA258987
6 · The paper itself

Abstract

PubMed holds no abstract for this paper.

Indexed as

agentic RAGautonomous treatment planningcompound AI systemsdose predictionLLM agentsmulti-agent orchestrationradiation therapy

Identifiers

PMID42466075
PMCPMC13374601

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.