# Benchmarking and Evaluation of AI Models in Biology: Outcomes and Recommendations from the CZI Virtual Cells Workshop

arXiv: https://arxiv.org/abs/2507.10502 · HTML: https://mlsanity.com/paper/2507.10502/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 41 references that are in the map.
- First posted on arXiv 14 Jul 2025.

## Builds on (most cited first, 5 of 5)

- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [Methods and open-source toolkit for analyzing and visualizing challenge results](https://mlsanity.com/paper/1910.05121/) (2019)
- [Understanding metric-related pitfalls in image analysis validation](https://mlsanity.com/paper/2302.01790/) (2023)
- [The Problem with Metrics is a Fundamental Problem for AI](https://mlsanity.com/paper/2002.08512/) (2020)
- [Toward AI-Driven Digital Organism: Multiscale Foundation Models for Predicting, Simulating and Programming Biology at All Levels](https://mlsanity.com/paper/2412.06993/) (2024)

## Built on by (most cited first, 1 of 1)

- [Parameter-free representations outperform single-cell foundation models on downstream benchmarks](https://mlsanity.com/paper/2602.16696/) (2026)

## Abstract (arXiv)

Artificial intelligence holds immense promise for transforming biology, yet a lack of standardized, cross domain, benchmarks undermines our ability to build robust, trustworthy models. Here, we present insights from a recent workshop that convened machine learning and computational biology experts across imaging, transcriptomics, proteomics, and genomics to tackle this gap. We identify major technical and systemic bottlenecks such as data heterogeneity and noise, reproducibility challenges, biases, and the fragmented ecosystem of publicly available resources and propose a set of recommendations for building benchmarking frameworks that can efficiently compare ML models of biological systems across tasks and data modalities. By promoting high quality data curation, standardized tooling, comprehensive evaluation metrics, and open, collaborative platforms, we aim to accelerate the development of robust benchmarks for AI driven Virtual Cells. These benchmarks are crucial for ensuring rigor, reproducibility, and biological relevance, and will ultimately advance the field toward integrated models that drive new discoveries, therapeutic insights, and a deeper understanding of cellular systems.