Evidence map›Paper›PMID 41670585›Full record

ArticleJACC. Advances2026

Design and Architecture of a Generative-AI-Supported, Nonphysician-Delivered Model for GDMT Optimization in HFrEF: The ASSIST-HF Trial.

Eliano P Navarese, Joshua H Leader, Rafaella I L Markides, Sogol Koolaji, Dean J Kereiakes, Jacek Kubica, Mehriban Isgender, Thomas F Lüscher, Diana A Gorog

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in JACC. Advances, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

  1. Review
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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

9 authors.

Eliano P NavareseDepartment of Life and Health Sciences, Link Campus University, Rome, Italy; SIRIO MEDICINE Research Network, Bydgoszcz, Poland; Department of Cardiology and Internal Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland. Electronic address: elianonavarese@gmail.com.
Joshua H LeaderDepartment of Cardiology, East and North Hertfordshire NHS Trust, Stevenage, Hertfordshire, United Kingdom; Faculty of Medicine, National Heart and Lung Institute, Imperial College, London, United Kingdom.
Rafaella I L MarkidesSchool of Clinical Medicine, University of Cambridge, Cambridge, United Kingdom.
Sogol KoolajiDepartment of Cardiology, East and North Hertfordshire NHS Trust, Stevenage, Hertfordshire, United Kingdom; School of Life and Medical Sciences, Postgraduate Medical School, University of Hertfordshire, United Kingdom.
Dean J KereiakesThe Christ Hospital and Lindner Research Center, Cincinnati, Ohio, USA.
Jacek KubicaDepartment of Cardiology and Internal Medicine, Nicolaus Copernicus University, Bydgoszcz, Poland.
Mehriban IsgenderRepublican Clinical Hospital, Department of Cardiology, Azerbaijan Medical University, Department of Family Medicine, Baku, Azerbaijan.
Thomas F LüscherFaculty of Medicine, National Heart and Lung Institute, Imperial College, London, United Kingdom; School of Cardiovascular and Metabolic Medicine & Sciences, Kings College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Gy, s and St. Thomas' NHS Foundation Trust, London, United Kingdom.
Diana A GorogDepartment of Cardiology, East and North Hertfordshire NHS Trust, Stevenage, Hertfordshire, United Kingdom; Faculty of Medicine, National Heart and Lung Institute, Imperial College, London, United Kingdom; School of Life and Medical Sciences, Postgraduate Medical School, University of Hertfordshire, United Kingdom; School of Cardiovascular and Metabolic Medicine & Sciences, Kings College London, London, United Kingdom; Royal Brompton and Harefield Hospitals, Gy, s and St. Thomas' NHS Foundation Trust, London, United Kingdom. Electronic address: d.gorog@imperial.ac.uk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Patients with heart failure with reduced ejection fraction require rapid initiation and uptitration of guideline-directed medical therapy (GDMT), which is resource-intensive. In a prospective, open-label pilot trial, we assessed the feasibility, acceptability, and safety of a generative artificial intelligence-powered virtual assistant (VA), with retrieval-augmented generation and expert prompt engineering, to optimize GDMT. Patients with new heart failure with reduced ejection fraction (n = 60) were randomized to VA-guided care, delivered by nonmedical staff at 2-weekly intervals or standard-of-care treatment delivered by doctors or nurses. At 12 weeks, patients in the VA arm had superior GDMT optimization across all medication classes, lower N-terminal pro-B-type natriuretic peptide, and fewer hospitalizations. Patient-reported acceptability, appropriateness and feasibility scores were high, with no safety disagreements between VA and clinician recommendations. Treatment by an artificial intelligence-powered VA, run by nonmedical staff, with minimal remote medical supervision, is acceptable to patients, and can safely and effectively optimize GDMT, representing a scalable strategy to optimize treatment and health care resource utilization.

Indexed as

artificial intelligenceGDMTheart failure

Identifiers

PMID41670585
PMCPMC13100738

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.