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Reading List and Resources

Overview

This reading list supports the AI in Healthcare unit. Readings are organised by week and priority level.

Priority Levels: - Required: Essential reading for the week - Recommended: Strongly encouraged for deeper understanding - Extension: For students wanting additional depth

Foundational Texts

These texts provide broad foundations and are valuable references throughout the unit:

Topol, E. (2019). Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again. Basic Books. - Accessible introduction to healthcare AI - Clinical perspective on AI potential and challenges

Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. New England Journal of Medicine, 380(14), 1347-1358. - Comprehensive overview of ML in healthcare - Essential reference for terminology and concepts

Section 1: Foundations (Weeks 1-3)

Week 1: Introduction to AI in Healthcare

Required: - Topol (2019), Chapter 1: Introduction

Recommended: - Australian Digital Health Agency. (2023). National Digital Health Strategy. [Link to be added] - Matheny, M. E., et al. (2020). Artificial Intelligence in Health Care: A Report From the National Academy of Medicine. JAMA, 323(6), 509-510.

Extension: - Jiang, F., et al. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4), 230-243.

Week 2: AI Architecture and Algorithms

Required: - Rajkomar, A., Dean, J., & Kohane, I. (2019). Machine Learning in Medicine. NEJM, 380(14), 1347-1358.

Recommended: - Google Machine Learning Crash Course: Supervised Learning modules. ml.crash.course.google.com - Anthropic. Claude Model Card and Documentation. docs.anthropic.com

Extension: - Esteva, A., et al. (2019). A guide to deep learning in healthcare. Nature Medicine, 25(1), 24-29. - Vaswani, A., et al. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30.

Week 3: Data Foundations

Required: - Chen, I. Y., et al. (2021). Ethical Machine Learning in Healthcare. Annual Review of Biomedical Data Science, 4, 123-144.

Recommended: - Australian Institute of Health and Welfare. Data governance resources. aihw.gov.au - Mayi Kuwayu Study. Indigenous Data Sovereignty resources. mkstudy.com.au

Extension: - Gianfrancesco, M. A., et al. (2018). Potential Biases in Machine Learning Algorithms Using Electronic Health Record Data. JAMA Internal Medicine, 178(11), 1544-1547.

Section 2: Evaluation (Weeks 4-6)

Week 4: AI Safety and Failure Modes

Required: - Obermeyer, Z., et al. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science, 366(6464), 447-453.

Recommended: - Cabitza, F., Rasoini, R., & Gensini, G. F. (2017). Unintended Consequences of Machine Learning in Medicine. JAMA, 318(6), 517-518. - FDA. Artificial Intelligence and Machine Learning in Software as a Medical Device. fda.gov

Extension: - Bates, D. W., et al. (2020). Reporting and Implementing Interventions Involving Machine Learning and Artificial Intelligence. Annals of Internal Medicine, 172(11_Supplement), S137-S144.

Week 5: Ethics and Regulatory Landscape

Required: - Australian Government. Australia's AI Ethics Framework. industry.gov.au - Therapeutic Goods Administration. Software as a Medical Device (SaMD). tga.gov.au

Recommended: - AHPRA/Medical Board of Australia. Technology and telehealth guidelines. - Mello, M. M., & Guha, N. (2024). Understanding Liability Risk from Using Health Care AI Tools. NEJM.

Extension: - World Health Organization. (2021). Ethics and governance of artificial intelligence for health. - Char, D. S., Shah, N. H., & Magnus, D. (2018). Implementing Machine Learning in Health Care — Addressing Ethical Challenges. NEJM, 378(11), 981-983.

Week 6: AI in High-Stakes Environments

Required: - Selected literature review on AI in emergency medical services (provided via LMS)

Recommended: - International aeromedical service reports and conference proceedings (selection provided) - Human factors in healthcare AI: selected readings (provided via LMS)

Extension: - Literature from aviation human factors and AI - Autonomous systems safety frameworks

Section 3: Application (Weeks 7-9)

Week 7: Implementation Frameworks

Required: - Sendak, M. P., et al. (2020). A Path for Translation of Machine Learning Products into Healthcare Delivery. EMJ Innovations.

Recommended: - Australian Commission on Safety and Quality in Health Care. National Model Clinical Governance Framework. - NHS AI Lab. Implementation guidance. transform.england.nhs.uk/ai-lab

Extension: - Greenhalgh, T., et al. (2017). Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies. Journal of Medical Internet Research, 19(11), e367.

Week 8: Human-AI Teaming

Required: - Sittig, D. F., & Singh, H. (2010). A new sociotechnical model for studying health information technology in complex adaptive healthcare systems. Quality and Safety in Health Care, 19(Suppl 3), i68-i74.

Recommended: - Selected human factors in healthcare AI papers (provided via LMS) - Clinical education resources on technology-enhanced practice

Extension: - Parasuraman, R., & Riley, V. (1997). Humans and Automation: Use, Misuse, Disuse, Abuse. Human Factors, 39(2), 230-253.

Week 9: Monitoring, Maintenance, and Incident Response

Required: - FDA. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning-Based Software as a Medical Device. fda.gov

Recommended: - ECRI Institute. Health technology safety resources. - Business continuity planning frameworks

Extension: - Literature on post-market surveillance of AI medical devices

Section 4: Synthesis (Weeks 10-12)

Week 10: Current AI Landscape

Required: - Selected peer-reviewed AI implementation studies (provided via LMS)

Recommended: - Australian Digital Health Agency. Current reports and publications. - Lancet Digital Health: Current issue review

Extension: - Systematic reviews of healthcare AI effectiveness

Week 11: Emerging Technologies

Required: - Selected recent publications from Nature Medicine, Lancet Digital Health (provided via LMS)

Recommended: - Technology company research publications (Google Health, Microsoft Research Health Futures) - Health workforce futures literature

Extension: - Moor, M., et al. (2023). Foundation models for generalist medical artificial intelligence. Nature, 616(7956), 259-265.

Week 12: Integration and Future Positioning

Required: - Review of all course materials

Recommended: - Professional body position statements on AI - Peer capstone projects

Key Journals

For ongoing engagement with healthcare AI literature:

  • Lancet Digital Health - Clinical digital health focus
  • npj Digital Medicine - Broad digital medicine
  • JAMIA - Health informatics
  • Nature Medicine - High-impact medical AI
  • NEJM AI - New journal focused on AI

Newsletters and Digests

For staying current without being overwhelmed:

  • Import AI - Weekly AI developments
  • The Batch - DeepLearning.AI newsletter
  • Healthcare AI Digest - Healthcare-specific roundup
  • ADHA Newsletter - Australian digital health updates

Australian Resources

Interactive Resources

Note on Currency

Healthcare AI is a rapidly evolving field. While this reading list is current as of unit development, additional readings may be provided throughout the semester to address new developments. Check the LMS for updates.