# Benchmarking PNW Model for MedMNIST to 100% Accuracy

arXiv: https://arxiv.org/abs/2604.18916 · HTML: https://mlsanity.com/paper/2604.18916/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 20 references that are in the map.
- First posted on arXiv 20 Apr 2026.

## Builds on (most cited first, 8 of 8)

- [Outrageously Large Neural Networks: The Sparsely-Gated Mixture-of-Experts Layer](https://mlsanity.com/paper/1701.06538/) (2017)
- [MedMNIST v2 -- A large-scale lightweight benchmark for 2D and 3D biomedical image classification](https://mlsanity.com/paper/2110.14795/) (2021)
- [Rethinking model prototyping through the MedMNIST+ dataset collection](https://mlsanity.com/paper/2404.15786/) (2024)
- [Benchmarking MedMNIST dataset on real quantum hardware](https://mlsanity.com/paper/2502.13056/) (2025)
- [Rethinking Foundation Models for Medical Image Classification through a Benchmark Study on MedMNIST](https://mlsanity.com/paper/2501.14685/) (2025)
- [Error-free Training for Artificial Neural Network](https://mlsanity.com/paper/2312.16060/) (2023)
- [Toward Aristotelian Medical Representations: Backpropagation-Free Layer-wise Analysis for Interpretable Generalized Metric Learning on MedMNIST](https://mlsanity.com/paper/2604.06017/) (2026)
- [Toward Errorless Training ImageNet-1k](https://mlsanity.com/paper/2508.04941/) (2025)

## Abstract (arXiv)

In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be trained error-free, thus acquiring the capability of not making repeated mistakes. The method is applied to 18 MedMNIST biomedical datasets. Except for three datasets, which suffer from the double-labeling problem, all are trained to perfection.