BookDaniel W. Byrne.
Summary: "Artificial Intelligence for Improved Patient Outcomes provides new, relevant, and practical information on what AI can do in healthcare and how to assess whether AI is improving health outcomes. With clear insights and a balanced approach, this innovative book offers a one-stop guide on how to design and lead pragmatic real-world AI studies that yield rigorous scientific evidence-all in a manner that is safe and ethical. Daniel Byrne, Director of Artificial Intelligence Research at AVAIL (the Advanced Vanderbilt Artificial Intelligence Laboratory), and author of landmark pragmatic studies published in leading medical journals, shares four decades of experience as a biostatistician and AI researcher. Building on his first book, Publishing Your Medical Research, the author gives the reader the competitive advantage in creating reproducible AI research that will be accepted in prestigious high-impact medical journals. Provides easy-to-understand explanations of the key concepts in using and evaluating AI in medicine. Offers practical, actionable guidance on the mechanics and implementation of AI applications in medicine. Shares career guidance on a successful future in AI in medicine. Teaches the skills to evaluate AI tools and avoid being misled by the hype. For a wide audience of healthcare professionals impacted by Artificial Intelligence in medicine, including physician-scientists, AI developers, entrepreneurs, and healthcare leaders who need to evaluate AI applications designed to improve safety, quality, and value for their institutions. Enrich Your eBook Reading Experience Read directly on your preferred device(s), such as computer, tablet, or smartphone. Easily convert to audiobook, powering your content with natural language text-to-speech. "-- Provided by publisher.
Contents:
Overview : How Artificial Intelligence Will Improve Health
Randomization : The "Secret Sauce"
Evaluation : The Facts Matter. Pseudo-Innovation vs Real Innovation
Synergy : Building a Successful Clinician-Computer Collaboration
Fairness : Addressing the Ethical, Regulatory, and Privacy Issues
Modeling : An Overview of Predictive Modeling, Neural Networks, and Deep Learning
EHRs : Exporting, Cleaning, Managing Datasets, and Integrating Models into the Electronic Health Record
Resistance : Understanding and Overcoming the Resistance to AI, Randomization, and Change
Execution : Increasing the Odds of Future Success
Integration : Building a Learning Health Care System With Pragmatic AI Trials
Streamlining : Reducing Waste and Lowering Costs in Health Care
Complications : Predicting and Preventing Hospital Complications
Prevention : Identifying Diseases With Predictive Models
Precision Medicine : AI to Improve Health Screenings and Treatments
Drugs and Devices : Using AI to Improve Pharmaceutical and Medical Device Development and Applications
Medical Literature : AI and Information Overload
Imaging : Medical Imaging and Strategies for Assessing Patient Impact
Pandemics : Using AI Tools to Improve Health Outcomes in a Pandemic
Careers : How to Build a Career Around AI in Medicine by Turning This Playbook Into a Reality.