Category Archives: Technology and Informatics

Posts related to technology, gadgets, cloud, informatics, or just about anything that is/can be/plugs into a computer. Relationship to radiology optional.

The FDA has an Idea, or 10, on Good Machine Learning Practice

Good machine learning practices goes beyond the chip

The U.S. FDA, Health Canada, and the UK’s MHRA have unveiled 10 guiding principles for Good Machine Learning Practice (GMLP) in developing AI/ML medical devices. These principles aim to ensure safety, efficacy, and quality in healthcare innovation. Key focuses include leveraging multi-disciplinary expertise, implementing good software and security practices, ensuring representative clinical study participants and data sets, maintaining independence between training and test data sets, and emphasizing the performance of the human-AI team. These guidelines also highlight the importance of clear user information, robust testing, and ongoing monitoring of deployed models to manage re-training risks and maintain performance.

Read the full GMLP draft on the FDA website.

1-Minute Summary

Here are the ten principles of GMLP.

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Artificial Intelligence: Are you a Centaur or a Cyborg?

In the rapidly evolving field of radiology, artificial intelligence (AI) is not just a tool but a collaborator, reshaping the dynamics of diagnosis and patient care. On the first order, the answer seemed clear: knowledge workers using AI outperforms those that don’t.

But the literature offers little detail on what happens after you embrace AI. Just with every tool ever existed, it really matters how you use it. As it turns out, it also matters how AI becomes part of your work.

To better understand this partnership, a group of Harvard Business School scholars published a study on business consultants who have, and who have not, opted to adopt GPT-4 in their daily work in spring 2023. There are several interesting conclusions – one of them delve into the analogy of centaurs versus cyborgs, concepts borrowed from mythology and science fiction that provide a vivid framework for the interaction between human intelligence and AI in radiology.

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AI Regulation in Healthcare: CMS and Congressional Scrutiny

AI regulation in healthcare is coming. This article from FierceHealthcare summarizes the growing use of artificial intelligence (AI) in healthcare innovations and the increasing scrutiny from Senate lawmakers regarding AI regulation and healthcare payment. It highlights the concerns of bias in AI systems and the legislative scrutiny to ensure these technologies benefit patient care without discrimination. The discussion also covers lawsuits against major Medicare Advantage insurers for allegedly using AI to deny care, and the Centers for Medicare & Medicaid Services’ (CMS) guidance on AI use in healthcare decisions. Additionally, the need for transparency, accountability, and meaningful human review in AI applications in healthcare is emphasized, alongside calls for federal support to navigate AI’s integration into healthcare practices responsibly.

I urge you to read the full article which includes links to the first-hand sources and supplement with a short summary below for the busy professional.

1-Minute Summary

  • Federal lawmakers are actively discussing the impact of artificial intelligence (AI) in healthcare, emphasizing the need to protect patients from bias inherent in some big data systems without stifling innovation. These biases can discriminate against patients based on race, gender, sexual orientation, and disability.
  • To ensure the beneficial outcomes of AI while safeguarding patient rights, the Algorithmic Accountability Act was introduced. This act mandates healthcare systems to regularly verify that AI tools are being used as intended and are not perpetuating harmful biases, especially in federal programs like Medicare and Medicaid.
  • Major Medicare Advantage insurers, including Humana and UnitedHealthcare, are under legal scrutiny for allegedly using AI algorithms to deny care, highlighting the challenges of implementing AI in patient care decisions without exacerbating discrimination or introducing new biases.
  • The Centers for Medicare & Medicaid Services (CMS) issued guidelines prohibiting the use of AI or algorithms for making coverage decisions or denying care based solely on algorithmic predictions, emphasizing the necessity for decisions to be based on individual patient circumstances and reviewed by medical professionals.
  • Testimonies during the legislative hearings called for additional clarity on the proper use of AI in healthcare, suggesting the establishment of AI assurance labs for developing standards, and advocating for federal support to help healthcare organizations navigate the use of AI tools through investments in technical assistance, infrastructure, and training.

American College of Radiology

The emphasis on AI transparency as an answer to bias is not new. The American College of Radiology (ACR) recently kicked off its Transparent-AI initiative to advocate for openness and trust in AI. The program invites all manufacturers with FDA-cleared AI tools to participate. By offering detailed insights into an algorithm’s training, performance, and intended use, Transparent-AI not only boosts product credibility but also aids in integrating these innovations into diverse healthcare environments. Behind the scenes, the ACR has also advocated for transparency in AI with various federal agencies and lawmakers.

Disclosure: I am not involved with Transparent-AI but do sit on the ACR Commission on Informatics and chair the ACR Informatics Advisory Council and annual ACR DSI Summit. Register for the 2024 DSI Summit here!

AI+Human Better than Human in Neurodegenerative Imaging

Recent research underscores a leap in neuroimaging accuracy for Alzheimer’s disease diagnosis, emphasizing the superior performance of AI-assisted radiologists over either AI or humans alone. This collaborative approach marries the meticulous precision of AI with the nuanced understanding of human experts, potentially setting a new standard in the detection of amyloid-related imaging abnormalities. Specifically, it demonstrated superior performance in detecting amyloid-related imaging abnormalities (ARIA), crucial for amyloid-β–directed antibody therapy. This synergy enhances diagnostic precision and underscores the potential of AI-enhanced radiological diagnostics to improve patient care significantly.

How will this synergy between AI and human intelligence redefine the future of medical diagnostics? Can this model be the blueprint for addressing other complex diseases? This breakthrough prompts us to envision a healthcare landscape where technology and human expertise converge to offer unparalleled patient care.

Detailed study can be found in JAMA Network Open.

Different flavors of ESP8266

ESP8266 is a wifi enabled microcontroller. One of the most helpful ones because of it’s wifi ability and very low cost. This makes the ESP8266 popular in even commercial products that need wifi connectivity.

For development purposes, there are also a lot of variants for this chip. After some preliminary research, there appears to be two most helpful breakout boards for it.


NodeMCU / ESP8266

NodeMCU is technically the name of the Lua-compatible firmware for ESP8266, which later added support for ESP32 (the more powerful, dual-core sibling of ESP8266). NodeMCU was created in 2014 when user Hong committed the first file of nodemcu-firmware to GitHub. but people sometimes use this term to refer to breakout boards using ESP8266 following this particular schema. It comes with additional chips that enable USB-to-serial and other “quality of life” enhancements that make development easier. The breakout board is also compatible with solderless breadboards, making prototyping much easier.

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Your Radiology AI Briefing – May 12, 2018

In this briefing:

  • Research: Machine learning algorithm predicts wait time for outpatient imaging.
  • Commercial brain imaging AI receives FDA clearance
  • Paul Chang shares insight on the future of AI
  • Dreyer and Allen publish their views on the radiology AI ecosystem
  • CB Insights publishes market research on Google’s increasing involvement in healthcare AI.

Radiology AI Briefing logo graphic

Stay up to speed in 2 minutes. Radiology AI Briefing is a semi-regular series of blog posts featuring hand-picked news stories and summaries on machine learning and data science.

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Your Radiology AI Briefing – May 3, 2018

In this briefing:

  • RSNA launches a new AI journal.
  • ACR makes several moves to advance the use of artificial intelligence in the future of medical imaging.
  • Researchers publish in high impact journal the successful use of AI to detect breast density.
  • ACR and MICCAI sign agreement to advance AI in medical imaging.
  • Geisinger declares successful implementation of algorithm to improve time-to-diagnosis of intracranial hemorrhages 20-fold.

Radiology AI Briefing logo graphic

Stay up to speed in 2 minutes. Radiology AI Briefing is a semi-regular series of blog posts featuring hand-picked news stories and summaries on machine learning and data science.

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A Simple Tool to Brainstorm AI for Your Radiology Practice

Use the AI Canvas.

Source: A Simple Tool to Start Making Decisions with the Help of AI

In a recent Harvard Business Review article, Ajay Agrawal and coauthors shared a simple tool to think about how an AI tool may be deployed.  Although the tool is no more complex than 6 boxes of free text, it does follow a number of best practices when thinking about general data and machine learning:

  1. Always define an end-goal – what’s the desired outcome?
  2. You should always make a hypothesis of what may drive this desired outcome.
  3. You should determine how to present the ML prediction in a way that drives action, not just the data itself.
  4. Your data acquisition strategy should include a feedback mechanism.

For example, this is how one might fill out the AI Canvas tool in a radiology use case:

  • Prediction: Predict whether a brian MRI for a cancer patient contains increasing or new hydrocephalus
  • Action: Label the examination as critical, and denote that AI has determined a critical finding.  For example, create an “AI-STAT” category on worklist priority.
  • Judgment: Compare the cost of interpreting this brain MRI at its usual turnaround time, versus immediately.
  • Outcome: Observe whether the action taken in response to a study labeled AI-STAT is correct.
  • Input: New MRIs of the brain MRI performed, and their prior studies.
  • Training: Historical brain MRIs
  • Feedback: Identify false positives – perhaps the prior study was from 20 years ago, or there’s been surgical resection, so that ex vacuo dilation of ventricles is not hydrocephalus.  Perhaps there has been recent surgery Identify false negatives – subtle enlargement of the temporal horns missed by AI.  Use this information to improve the AI.

How might you use this worksheet to brainstorm AI for your radiology practice?


Your Origin Story for Data Science

There’s an origin story for every superhero; even those without superpowers (like Batman – that’s right) got started somewhere. What we sometimes forget is that there is also an origin story for every regular person, every profession, every hobby.

Source: Wikimedia Commons


If you’re a radiologist looking to learn a few things in radiology data science, a simple web search will reveal a seemingly overwhelming amount of material you might have to know.

Fortunately, only a very small subset is necessary to start being productive.  Here are a few resources I used to get started.

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William Chen’s answer to What are the top 5 skills needed to become a data scientist? – Quora

A Quora answer/article about data science.

Incidentally, the same 5 skills are also highly relevant to be a physician-informatician, particularly in radiology.  Give it a read.

Source: William Chen’s answer to What are the top 5 skills needed to become a data scientist? – Quora