AI in Healthcare
Clinical Machine Learning, Medical Imaging, Safety and Governance
About this guide
A concise exam companion for AI in healthcare, written for medical and health students, not just computer scientists. It covers clinical machine learning from scratch, health data, medical imaging, clinical prediction and language, evaluation and validation in a clinical setting, bias and equity, safety and human factors, regulation, deployment, and genomics and drug discovery, closing on ethics and trust. Worked examples are marked the way an examiner marks them, so you can see why an answer earns the grade.
Inside the guide
- Machine learning from scratch
- Health data
- Medical imaging
- Clinical prediction and language
- Evaluation and validation in a clinical setting
- Bias and equity
- Safety and human factors
- Regulation
- Deployment
- Genomics and drug discovery
- Closing on ethics and trust
Questions students ask
What does AI in Healthcare cover?
It covers clinical machine learning from scratch, health data, medical imaging, clinical prediction and language, evaluation and validation in a clinical setting, bias and equity, safety and human factors, regulation, deployment, and genomics and drug discovery, closing on ethics and trust.
Who is AI in Healthcare for?
It is written for medical and health students meeting AI, and for lecturers and librarians choosing a clear, exam-focused reading-list text.
What format does it come in?
Kindle ebook and paperback on Amazon.
Can I get a free copy?
Yes. Full Marks Press offers free review copies to students and educators at fullmarkspress.com/free.