A Comparative Framework for Evaluating Foundation Models on Tabular Data: A Case Study in Healthcare

arXiv:2609.22154v1 Announce Type: cross Abstract: Tabular data is the most common format in clinical practice, encompassing laboratory results, medication records, diagnostic codes, and patient demographics. As foundation models for tabular data have grown in number and variety, a practical question has become harder to answer: which model should a clinician or data scientist actually choose for a given task, and why? Existing surveys catalogue what these models can do, but they stop short of providing a structured way to compare them against the specific demands of a real application. We introduce \system{}, a comparative evaluation framework that scores and ranks tabular foundation models (TFMs) across six clinically meaningful dimensions: how well a model generalizes to new datasets, how effectively it protects patient privacy, how much data it needs to perform well, how it scales with growing datasets and feature spaces, how interpretable its predictions are to clinicians, and how fairly it performs across patient subgroups. Each dimension is broken down into measurable sub-components, and groups of sub-components can optionally be combined into supplementary compound scores, called super-metrics, that provide a diagnostic view of how a model performs across several dimensions simultaneously. To show how the framework works in practice, we apply it to two healthcare use cases, screening for iron deficiency and predicting heart failure, demonstrating how the same set of metrics leads to different model rankings depending on what matters most in each clinical context. We

The framework evaluates models for better healthcare outcomes.

The framework supports better clinical ML model selection by evaluating multiple relevant factors including generalization, privacy, data efficiency, scalability, interpretability, and fairness. This can enhance healthcare decision-making but does not directly change broader societal dynamics or guarantee improved outcomes. Impact is mostly technical and incremental.

This is a methodological contribution offering an evaluation framework rather than a claim about societal outcomes. It could modestly improve responsible model selection in healthcare but makes no direct claim about deployment impact or human flourishing.

This paper proposes a framework for evaluating foundation models on tabular healthcare data across multiple dimensions, including privacy, interpretability, and fairness. While neutral in its direct impact, the framework's intent is to guide responsible AI deployment in healthcare, indicating a slightly positive overall implication. It provides tools for better selection rather than making claims about AI's direct societal effects.

The framework aims to improve the selection and evaluation of AI models in healthcare. It addresses key concerns such as generalization, privacy, data efficiency, scalability, interpretability, and fairness. The application to real healthcare use cases demonstrates practical utility.

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
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DOCUMENT   a47a48b1-584f-4d84-8a31-f3d855360b87
           https://arxiv.org/abs/2609.22154
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EVIDENCE   3 extracted excerpts
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MODEL RUN  5 runs, methodology 1.0.1
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SCORE      -4  (Mixed or uncertain)
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CONFIDENCE 79%