# Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence

arXiv: https://arxiv.org/abs/2102.01116 · HTML: https://mlsanity.com/paper/2102.01116/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 26 references that are in the map.
- First posted on arXiv 1 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning](https://mlsanity.com/paper/1711.05225/) (2017)
- [Calibrating Healthcare AI: Towards Reliable and Interpretable Deep Predictive Models](https://mlsanity.com/paper/2004.14480/) (2020)
- [Learning and inference in knowledge-based probabilistic model for medical diagnosis](https://mlsanity.com/paper/1703.09368/) (2017)

## Built on by (most cited first, 2 of 2)

- [Explainable AI applications in the Medical Domain: a systematic review](https://mlsanity.com/paper/2308.05411/) (2023)
- [Learning Diagnostic Reasoning for Decision Support in Toxicology](https://mlsanity.com/paper/2603.29608/) (2026)

## Abstract (arXiv)

Medical toxicology is the clinical specialty that treats the toxic effects of substances, be it an overdose, a medication error, or a scorpion sting. The volume of toxicological knowledge and research has, as with other medical specialties, outstripped the ability of the individual clinician to entirely master and stay current with it. The application of machine learning techniques to medical toxicology is challenging because initial treatment decisions are often based on a few pieces of textual data and rely heavily on prior knowledge. ML techniques often do not represent knowledge in a way that is transparent for the physician, raising barriers to usability. Rule-based systems and decision tree learning are more transparent approaches, but often generalize poorly and require expert curation to implement and maintain. Here, we construct a probabilistic logic network to represent a portion of the knowledge base of a medical toxicologist. Our approach transparently mimics the knowledge representation and clinical decision-making of practicing clinicians. The software, dubbed Tak, performs comparably to humans on straightforward cases and intermediate difficulty cases, but is outperformed by humans on challenging clinical cases. Tak outperforms a decision tree classifier at all levels of difficulty. Probabilistic logic provides one form of explainable artificial intelligence that may be more acceptable for use in healthcare, if it can achieve acceptable levels of performance.