Artificial intelligence and machine learning for clinical pharmacology

DK Ryan, RH Maclean, A Balston… - British Journal of …, 2024 - Wiley Online Library
Artificial intelligence (AI) will impact many aspects of clinical pharmacology, including drug
discovery and development, clinical trials, personalized medicine, pharmacogenomics …

AI-based computer vision techniques and expert systems

Y Matsuzaka, R Yashiro - AI, 2023 - mdpi.com
Computer vision is a branch of computer science that studies how computers can 'see'. It is a
field that provides significant value for advancements in academia and artificial intelligence …

A survey on the interpretability of deep learning in medical diagnosis

Q Teng, Z Liu, Y Song, K Han, Y Lu - Multimedia Systems, 2022 - Springer
Deep learning has demonstrated remarkable performance in the medical domain, with
accuracy that rivals or even exceeds that of human experts. However, it has a significant …

Explainable AI applications in the Medical Domain: a systematic review

N Prentzas, A Kakas, CS Pattichis - arXiv preprint arXiv:2308.05411, 2023 - arxiv.org
Artificial Intelligence in Medicine has made significant progress with emerging applications
in medical imaging, patient care, and other areas. While these applications have proven …

Comparison of decision tree with common machine learning models for prediction of biguanide and sulfonylurea poisoning in the United States: an analysis of the …

O Mehrpour, F Saeedi, S Nakhaee… - BMC medical informatics …, 2023 - Springer
Background Biguanides and sulfonylurea are two classes of anti-diabetic medications that
have commonly been prescribed all around the world. Diagnosis of biguanide and …

Artificial intelligence applications in clinical chemistry

DR Bunch, TJS Durant… - Clinics in Laboratory …, 2023 - labmed.theclinics.com
Investigation of artificial intelligence (AI) applications in health care has accelerated rapidly
in recent years. Accordingly, there are also rapid advancements in this area applied to the …

Classification of acute poisoning exposures with machine learning models derived from the National Poison Data System

O Mehrpour, C Hoyte, H Delva‐Clark… - Basic & Clinical …, 2022 - Wiley Online Library
The primary aim of this pilot study was to develop a machine learning algorithm to predict
and distinguish eight poisoning agents based on clinical symptoms. Data were used from …

Deep learning neural network derivation and testing to distinguish acute poisonings

O Mehrpour, C Hoyte, A Al Masud… - Expert Opinion on …, 2023 - Taylor & Francis
Introduction Acute poisoning is a significant global health burden, and the causative agent is
often unclear. The primary aim of this pilot study was to develop a deep learning algorithm …

Utility of artificial intelligence to identify antihyperglycemic agents poisoning in the USA: introducing a practical web application using National Poison Data System …

O Mehrpour, S Nakhaee, F Saeedi, B Valizade… - … Science and Pollution …, 2023 - Springer
Clinical effects of antihyperglycemic agents poisoning may overlap each other. So,
distinguishing exposure to these pharmaceutical drugs may take work. This study examined …

[HTML][HTML] Exploring explainable AI features in the vocal biomarkers of lung disease

Z Chen, N Liang, H Li, H Zhang, H Li, L Yan… - Computers in Biology …, 2024 - Elsevier
This review delves into the burgeoning field of explainable artificial intelligence (XAI) in the
detection and analysis of lung diseases through vocal biomarkers. Lung diseases, often …