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ARXIV:2604.22018 · BIOMEDICAL AI · SUBMITTED 27 APR · 20:16 UTC · FRESHNESS STALE
ARXIV:2604.22018BIOMEDICAL AISUBMITTED 27 APR · 20:16 UTCFRESHNESS STALEDeepank Girish · Yi Hao Chan · Sukrit Gupta · Jing Xia · Jagath C. Rajapakse · arXiv
A framework to evaluate the robustness of AI-identified biomarkers for neurological disorders, enabling more reliable diagnostics.
Opportunity summary
Pain A framework to evaluate the robustness of AI-identified biomarkers for neurological disorders, enabling more reliable diagnostics.
Evidence 0 refs | 4 sources | 67% coverage
Blocker Evidence unverified
A framework to evaluate the robustness of AI-identified biomarkers for neurological disorders, enabling more reliable diagnostics. While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers…
Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified…
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated.…
Biomedical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
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A framework to evaluate the robustness of AI-identified biomarkers for neurological disorders, enabling more reliable diagnostics.
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10.48550/arXiv.2604.22018A framework to evaluate the robustness of AI-identified biomarkers for neurological disorders, enabling more reliable diagnostics.
Abstract
Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated. We propose RE-CONFIRM, a framework for evaluating the robustness of potential biomarker candidates elucidated by deep learning (DL) models including FMs. From experiments on five large datasets of Autism Spectrum Disorder (ASD), Attention-deficit Hyperactivity Disorder (ADHD), and Alzheimer's Disease (AD), we found that although commonly used performance metrics provide an intuitive assessment of model predictions, they are insufficient for evaluating the robustness of biomarkers identified by these models. RE-CONFIRM metrics revealed that simply finetuning FMs leads to models that fail to capture regional hubs effectively, even in disorders where hubs are known to be implicated, such as ASD and ADHD. In view of this, we propose Hub-LoRA (Low-Rank Adaptation) as a fine-tuning technique that enables FMs to not only outperform customised DL models but also produce neurobiologically faithful biomarkers supported by meta-analyses. RE-CONFIRM is generalizable and can be easily applied to ascertain the robustness of DL models trained on functional MRI datasets. Code is available at: https://github.com/SCSE-Biomedical-Computing-Group/RE-CONFIRM.
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unverified0 refs; 4 sources; 67% coverage.
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Dimensions overall score 7.0
PROBLEM
A framework to evaluate the robustness of AI-identified biomarkers for neurological disorders, enabling more reliable diagnostics. While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers ar...
METHOD
Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential b...
RESULT
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated. A public repository is...
WHY NOW
Biomedical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed public claims while anchored extraction refreshes.
A framework to evaluate the robustness of AI-identified biomarkers for neurological disorders, enabling more reliable diagnostics. While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Several brain foundation models (FM) have recently been proposed to predict brain disorders by modelling dynamic functional connectivity (FC). While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
ScienceToStartup currently rates this 7.0/10 on the public viability pass. While they demonstrate remarkable model performance and zero- or few-shot generalization, the salient features identified as potential biomarkers are yet to be thoroughly evaluated. A public repository is linked, so build verification can inspect implementation evidence instead of treating the paper as PDF-only.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
Biomedical AI moved forward this cycle; last verified April 2026. Public score 7.0/10. Implementation evidence is present through a linked repository.
Abstract-backed fallback claim; anchored extraction has not materialized a public claim row yet.
partial
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A framework to evaluate the robustness of AI-identified biomarkers for neurological disorders, enabling more reliable diagnostics.
Segment
Biomedical AI
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7.0/10 public viability
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2/3 checks · 67%
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reason
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proof status
unverified
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confidence low
next verification path
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stale
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0 references, 4 sources, 67% evidence coverage.
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