AI, autonomous machines and human awareness: Towards shared machine-human contexts in medicine

D. Douglas Miller, Elena A. Wood

Research output: Chapter in Book/Report/Conference proceedingChapter


Medical curricula trend to integrate clinical skills training and to create efficiencies in preclinical medical sciences, but the rapid emergence big data-intensive health care has led to initiating collaborations among data scientists, computer engineers, and medical educators that might generate novel educational high-technology platforms and innovative AI practice applications. The preprocessing of big data improves neural network feature recognition, improving the speed and accuracy of AI diagnostics and permitting chronic disease predictions. Applications of generative adversarial networks to create virtual patient phenotypes and image sets exposes medical learners to endless illness presentations, improving system-1 critical thinking for differential diagnosis development. AI offers great potential for education data managers working in support of medical educators and learners. These opportunities to build a shared context, in keeping with these themes of this book, include emerging data-driven AI applications for medical education and provider training include individual aptitude-based career advising, early identification of learners with academic difficulties, highly focused e-tutoring interventions, and natural language processing of standardized exam questions.

Original languageEnglish (US)
Title of host publicationHuman-Machine Shared Contexts
Number of pages16
ISBN (Electronic)9780128205433
StatePublished - Jan 1 2020
Externally publishedYes


  • Applications
  • Artificial intelligence
  • Challenges
  • Health care
  • Medical education
  • Medicine

ASJC Scopus subject areas

  • Computer Science(all)


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