Artificial Intelligence

Jinxiang Hu, Ph.D. Jinxiang Hu, Ph.D., is an Associate Professor in the department of Biostatistics & Data Science. Her work in Artificial Intelligence is to analyze and compare the imaging workflow, radiation dose, and image quality for COVID-19 patients examined using either the conventional manual positioning (MP) method or an AI-based automatic positioning (AP) method. A comparison between manual and artificial intelligence-based automatic positioning in CT imaging for COVID-19 patients | European Radiology (oclc.org)

Dr. Zhiguo Zhou, Ph.D., is an Assistant Professor in the Department of Biostatistics and Data Science at the University of Kansas Medical Center. In a recent article involving Artificial Intelligence titled, An introduction to deep learning in medical physics: advantages, potential, and challenges, Dr. Zhou and team will provide, "an overview of the method to medical physics researchers interested in DL to help them start the endeavor and will give in-depth discussions on the DL technology to make researchers aware of its potential challenges and possible solutions."

Dong Pei, Ph.D., is a Research Assistant Professor in the Department of Biostatistics and Data Science at the University of Kansas Medical Center. In an article titled, Artificial intelligence enhances whole-slide interpretation of PD-L1 CPS in triple-negative breast cancer: A multi-institutional ring study Dr. Pei and his team conclude, "With the help of the AI-assisted diagnostic method, different levels of pathologists achieved excellent consistency and repeatability in the interpretation of PD-L1 (Dako 22C3) CPS. Our AI-assisted diagnostic approach was proved to strengthen the consistency and repeatability in clinical practice..

Yanming Li, Ph.D., is an Assistant Professor in the department of Biostatistics and Data Science at the University of Kansas Medical Center (KUMC). As described in his article, An artificial intelligence-generated model predicts 90-day survival in alcohol-associated hepatitis: A global cohort study, Dr. Li and his team concluded, "Harnessing artificial intelligence within a global consortium, we pioneered a scoring system excelling over traditional models for 30 and 90-day AH mortality predictions. Beneficial for clinical trials, steroid therapy, and transplant indications."

ADr. Amit Noheria, MD, is an Associate Professor of Medicine at the University of Kansas School of Medicine. Dr. Noheria leads the Program for AI and Research in Cardiovascular Medicine. In this article titled, Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review | Current Cardiology Reports (oclc.org), Dr. Noheria and team describe how Artificial intelligence (AI) is transforming electrocardiography (ECG) interpretation. AI diagnostics can reach beyond human capabilities, facilitate automated access to nuanced ECG interpretation, and expand the scope of cardiovascular screening in the population.

Dr. Andres M. Bur, MD, FACS is the Director of Robotics and Minimally Invasive Head and Neck Surgery and an Associate Professor of Otolaryngology - Head and Neck Surgery at the University of Kansas. In this article titled, Exploring the Role of Artificial Intelligence Chatbots in Preoperative Counseling for Head and Neck Cancer Surgery, Dr. Bur and his team concluded, "Head and neck surgeons rated ChatGPT-generated and readily available online educational materials similarly and further refinement in AI technology may soon open more avenues for patient counseling."

Dr. Anil Chauhan, MD, is a Professor of Radiology and the Department Vice-Chair of Artificial Intelligence, Informatics, and Innovation. In his article, Dr. Chauhan describes, "Evolving techniques such as multiparametric modeling, radiofrequency envelope analysis, and artificial intelligence-based tools are also on the horizon. The authors discuss the societal impact of fatty liver disease, summarize the current state of liver fat quantification with CT and MRI, and describe past, currently available, and potential future US-based techniques for evaluating liver fat. US Quantification of Liver Fat: Past, Present, and Future | RadioGraphics (oclc.org).

Dr. Diego Robles Mazzotti, Ph.D. is an Assistant Professor and the Interim Division Chief in the Division of Medical Informatics, Department of Internal Medicine at the University of Kansas Medical Center. In an article titled, Diagnostic Performance of Machine Learning-Derived OSA Prediction Tools in Large Clinical and Community-Based Samples , Dr. Mazzotti and his team describe, "The machine learning-derived symptomless OSA prediction tool using ANN both outperformed the LOG approach and had a similar AUC to the STOP-BANG questionnaire, which requires patient-reported symptoms.

Hannes Devos, Ph.D., PT, DRS, FACRM, is an associate professor in the Department of Physical Therapy, Rehabilitation Science, and Athletic Training KU Medical Center. In this article titled, Classification of Parkinson's disease and essential tremor based on balance and gait characteristics from wearable motion sensors via machine learning techniques: a data-driven approach, Dr. Devos and his team concluded, "This study demonstrated the utility of machine learning models to classify different movement disorders based on balance and gait characteristics collected from wearable sensors.

Dr. Daniel Parente, MD, is certified by the American Board of Family Medicine. Dr. Parente currently works in Family Medicine at The University of Kansas Health System. Dr. Parente's research is centered around improving precision medicine in primary care. His research focuses primarily on how advanced technologies - such as machine learning or genomic technologies - can be used to improve patient outcomes. In his article Dr. Parente discusses, "the principal limitations to the use of generative artificial intelligence in medical education-hallucination, bias, cost, and security-and suggest some approaches to confronting these problems." Generative Artificial Intelligence and Large Language Models in Primary Care Medical Education (stfm.org)

Dr. Denton K Shanks, DO, MPH is certified by the American Osteopathic Board of General Practice. Dr. Shanks currently works in Family Medicine at The University of Kansas Health System. Dr. Shanks has investigated Artificial Intelligence as a tool for, Adaptation and External Validation of Pathogenic Urine Culture Prediction in Primary Care Using Machine Learning - PMC (nih.gov). Utilizing a machine learning urine culture predictor devised for Emergency Department (ED) patients requiring the use of urine microscopy ("NeedMicro" predictor). Read more here. Machine Learning Prediction of Urine Cultures in Primary Care - PMC (nih.gov)

Sarah F. Kessler, PhD, MPH, is a Professor, Family Medicine and Community Health at the University of Kansas Medical Center. Dr. Kessler completed her doctoral training in International Public Health at the Johns Hopkins Bloomberg School of Public Health. In her work on, Evaluating the efficacy of the HITSystem 2.1 to improve PMTCT retention and maternal viral suppression in Kenya: Study protocol of a cluster-randomized trial, Dr. Kessler and her team investigate, "The HITSystem 2.1 is an eHealth intervention that aims to improve retention in PMTCT services and viral load monitoring, using electronic alerts to providers and SMS to patients.