Article

Metrics for Evaluating Biological AI Model Prediction Accuracy at the Data-Substrate Level

2026-06-18

Abstract excerpt

<h4>Summary</h4> Reports in the biological literature disagree on whether a given model can predict a biological outcome from a given data sample — one study finding a model capable, another, on the same kind of data, finding it is not. This is particularly a challenge in relation to LLMs–where the models are large and opaque, with weights and training data inaccessible. Such disagreements cannot be settled by di...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 5e582d77-e463-5f38-9e76-d3364c152ef0
DOI: 10.64898/2026.06.14.732129

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Metrics for Evaluating Biological AI Model Prediction Accuracy at the Data-Substrate LevelDOI 10.64898/2026.06.14.732129Read

Metrics for Distinguishing Biological Versus Interventional Change in AI ModelsSemantic proximity Read

Biomarker fidelity score: A quantitative framework for individual-level validation of explainability methods in 3D Alzheimer’s disease MRI classificationSemantic proximity Read

Feeding the machine: challenges to reproducible predictive modeling in resting-state connectomicsSemantic proximity Read

On stability of Canonical Correlation Analysis and Partial Least Squares with application to brain-behavior associationsSemantic proximity Read

Reference-Class Failure in Mortality Prediction: A Synthetic Study of Irreducible Uncertainty in End-of-Life Prognostic ModelsSemantic proximity Read

A formalization of the IdioNomo Framework for explanation and prediction in psychology: unifying idiographic and nomothetic approachesSemantic proximity Read

A formalization of the IdioNomo Framework for explanation and prediction in psychology: unifying idiographic and nomothetic approachesSemantic proximity Read

RDoC-Informed Explainable AI as a Paradigm for Multilevel Alzheimer’s Disease Diagnosis and Progression Prediction: a Systematic ReviewSemantic proximity Read

Metadata Insufficiency and Reporting Deficits in Medical AI Generalization: A Meta-Research Audit Using Non-Parametric Bootstrap ResamplingSemantic proximity Read

Trustworthy LLM-Embedding Clinical Prediction: Calibrating Confidence and Transparency for Foundation Model-Based Disease Risk ScoresSemantic proximity Read

An Objective Search for Unrecognized Bias in Validated COVID-19 Prediction ModelsSemantic proximity Read

Data-driven distillation and precision prognosis in traumatic brain injury with interpretable machine learning.Semantic proximity Read