Digital Health Frontier
By John Halamka and Paul Cerrato — In part 2 of our series on the basics of digital technology, we explore the deep learning tools that can improve medical image analysis and much more.
By John Halamka and Paul Cerrato — In this new series, we pull back the curtain on the Wizard of Oz to provide plain English explanations about machine learning, artificial neural networks, natural language processing, large language models, and related technology.
By John Halamka and Paul Cerrato — Given their tendency to invent “facts,” several researchers have begun comparing their strengths and weaknesses.
By John Halamka and Paul Cerrato—A well-reasoned, coherent thesis is not enough to convince editorial gatekeepers to accept your article. Consider these additional suggestions.
By John Halamka and Paul Cerrato — Mayo Clinic’s Tapestry Study has demonstrated that next generation genetic analysis can have a significant impact on patient care.
By John Halamka and Paul Cerrato — Imagine if you could create a digital clone of yourself that can be used to test various treatment options to determine which one is best for your real self.
By John Halamka and Paul Cerrato — Data scientists use a variety of coding languages to create AI-driven models, but the real “secret sauce” that helps them identify the best algorithms are the weights the coding generates.
By John Halamka and Paul Cerrato — Several of these digital tools are supported by strong evidence and are worth considering, not to replace your clinical judgement, but to augment it.
By John Halamka and Paul Cerrato — All the good things in the world worth believing, and among those good things are the therapeutic power of kindness and the healing effects of music.
By John Halamka, Paul Cerrato, and Sonya Makhni — How do you construct a safe, effective algorithm? It’s not an easy question to answer, but with a well thought out roadmap, it’s doable.
By John Halamka and Paul Cerrato — Even the most accurate algorithm is useless if it can’t be seamlessly implemented into a hospital’s workflow. Here’s a way to make that happen.
By John Halamka, Paul Cerrato, and Teresa Atkinson — Many clinicians are well aware of the shortcomings of LLMs, but studies suggest that retrieval-augmented generation could help address these problems.