The Role and Promise of Artificial Intelligence in Medical Toxicology

Abstract

Artificial intelligence (AI) refers to machines or software that process information and interact with the world as understanding beings. Examples of AI in medicine include the automated reading of chest X-rays and the detection of heart dysrhythmias from wearables. A key promise of AI is its potential to apply logical reasoning at the scale of data too vast for the human mind to comprehend. This scaling up of logical reasoning may allow clinicians to bring the entire breadth of current medical knowledge to bear on each patient in real time. It may also unearth otherwise unreachable knowledge in the attempt to integrate knowledge and research across disciplines. In this review, we discuss two complementary aspects of artificial intelligence: deep learning and knowledge representation. Deep learning recognizes and predicts patterns. Knowledge representation structures and interprets those patterns or predictions. We frame this review around how deep learning and knowledge representation might expand the reach of Poison Control Centers and enhance syndromic surveillance from social media.

Keywords: Artificial intelligence; Big data; Knowledge representation; Machine learning.

Fig. 1

Schematic of a deep neural network. Green neurons receive external input. Blue intermediary neurons combine the input with their own state according to an activation function. The states of some of the intermediary neurons contribute to the red output neuron.