HFEPX · Eval paper review
Shakeel Sheikh, Patrick Marmaroli, MD Sahidullah, Slim Ouni +3 more
Published
May 1, 2026
Citations
0
Trust level
Moderate
Usefulness score
40/100 (Low)
Extraction confidence
50% (Moderate)
Derived from extracted protocol signals and abstract evidence.
Rater population
Domain Experts
Signals refreshed
Oct 17, 2026
This paper is adjacent to HFEPX scope and is best used for background context, not as a primary protocol reference.
Use this for comparison and orientation, not as your only source.
Use this page for context, then validate protocol choices against stronger HFEPX references before implementation decisions.
Best use
Background context only
Use if you need
A secondary eval reference to pair with stronger protocol papers.
What to verify
Read the full paper before copying any benchmark, metric, or protocol choices.
Main weakness
The abstract does not clearly name benchmarks or metrics.
Treat as adjacent context, not a core eval-method reference.
If you are doing eval pipeline work, start here
This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and individualized therapy planning through artificial intelligence (AI) driven workflows. VST integrates deep learning (DL) based stuttering detection fSD with large language model (LLM) based agentic system to assist clinicians in developing evidence informed therapy plans. The VST system begins with the acquisition and feature extraction of given speech samples, followed by the detection of stuttering types. Building on these speech driven information S, VST initiates an agentic reasoning process in which specialized LLM agents autonomously generate, critique, and iteratively refine individualized therapy plans P. A dedicated CriticAgent πca acts as a structured self-refinement mechanism, reviewing the generated therapy plans P and identifying areas for potential improvement in consistency, methodological soundness, and alignment with available evidence and professional guidelines. The resulting therapy plan P is presented then as a draft for clinician review. Clinician feedback can then be incorporated to produce a revised plan P, thereby maintaining a clinician-in-the-loop (CITL) paradigm. An initial evaluation, involving expert speech language pathologist (SLP) examines the clinical interpretability and feasibility of the generated therapy plans. Taken together, the results show the potential of VST to augment clinician-led therapy planning and provide a promising basis for future studies evaluating its integration into real world clinical settings. An interactive user interface for the proposed prototype is available online at: https://vocametrix.com/ai/stuttering-therapy-planning-agent, facilitating interactive stuttering assessment and stuttering therapy planning.
These are the protocol signals we could actually recover from the available paper metadata. Use them to decide whether this paper is worth deeper reading.
Expert Verification, Critique Edit
Directly usable for protocol triage.
"This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and individualized therapy planning through artificial intelligence (AI) driven workflows."
None explicit
Validate eval design from full paper text.
"This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and individualized therapy planning through artificial intelligence (AI) driven workflows."
Not reported
No explicit QC controls found.
"This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and individualized therapy planning through artificial intelligence (AI) driven workflows."
Not extracted
No benchmark anchors detected.
"This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and individualized therapy planning through artificial intelligence (AI) driven workflows."
Not extracted
No metric anchors detected.
"This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and individualized therapy planning through artificial intelligence (AI) driven workflows."
Domain Experts
Helpful for staffing comparability.
"An initial evaluation, involving expert speech language pathologist (SLP) examines the clinical interpretability and feasibility of the generated therapy plans."
No benchmark or dataset names were extracted from the available abstract.
No metric terms were extracted from the available abstract.
This paper presents a proof of concept of the Virtual Speech Therapist (VST), an intelligent multi-agent based system designed to support stuttering assessment and individualized therapy planning through artificial intelligence (AI) driven workflows.
Based on abstract + metadata only. Check the source paper before making high-confidence protocol decisions.
Human feedback protocol is explicit
Detected: Expert Verification, Critique Edit
Evaluation mode is explicit
No clear evaluation mode extracted.
Quality control reporting appears
No calibration/adjudication/IAA control explicitly detected.
Benchmark or dataset anchors are present
No benchmark/dataset anchor extracted from abstract.
Metric reporting is present
No metric terms extracted.
