INSIGHTeyehub

Michael D Abramoff Verified account @MichaelAbramoff · Oct 21

Is a medical image like a social security number?

Retinal autonomous AI has been at the forefront of medical AI: regulation, reimbursement, ethics, and adoption. Now, retinal images are again at the forefront of concerns, about privacy and confidentiality of medical data. Multiple US States have enacted laws specifically calling out restrictions on retinal images. And the considerations used for considering retinal images, as personally identifiable information, apply as well to all medical images. This may well be a harbinger of more to come, which may make it difficult to impossible to do medical research or build AI.

Whether right or wrong, it is important to understand where these concerns are coming from.

We studied how retinal images have been depicted in art historically. Our paper just came out, and the results show that almost all (>98%) descriptions of retinal imaging are negative, and describe surveillance and dread. Very few are positive or medical.

sciencedirect.com/science/articl…

@pearsekeane

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Nan Liu, Duke-NUS Verified account @nliulab · Oct 7

🌍𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝘀𝗵𝗼𝘂𝗹𝗱 𝗯𝗲 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗲𝗱 𝗶𝗻 𝘁𝗵𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀, 𝗰𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝗶𝗲𝘀 𝗮𝗻𝗱 𝗵𝗲𝗮𝗹𝘁𝗵 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝘄𝗵𝗲𝗿𝗲 𝗶𝘁 𝘄𝗶𝗹𝗹 𝗮𝗰𝘁𝘂𝗮𝗹𝗹𝘆 𝗯𝗲 𝘂𝘀𝗲𝗱.

Delighted to announce the launch of #MAGIC (𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝗚𝗹𝗼𝗯𝗮𝗹 𝗜𝗻𝗶𝘁𝗶𝗮𝘁𝗶𝘃𝗲 𝗮𝗻𝗱 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻) introduced in our new @NaturePortfolio @NatureMedicine correspondence.
📜MAGIC paper: nature.com/articles/s4159…
🔗MAGIC website: magic-ai.health

MAGIC brings together clinicians, AI researchers, policymakers, industry stakeholders and local experts to address a critical question: does strong benchmark performance translate into safe, useful support across diverse healthcare settings?

Our initial priorities are:
✅ A global health LLM evaluation benchmark addressing multilingual coverage, regional disease burden and sociocultural adaptability.
✅ A global deployment reporting standard clarifying intended use, target users, healthcare settings, and performance and safety across populations.

Thank you to our colleagues #worldwide for helping launch the network. 𝗢𝘂𝗿 𝗴𝗼𝗮𝗹 𝗶𝘀 𝘁𝗼 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝘄𝗵𝗲𝗿𝗲 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝘄𝗼𝗿𝗸𝘀, 𝗳𝗼𝗿 𝘄𝗵𝗼𝗺, 𝗮𝗻𝗱 𝘂𝗻𝗱𝗲𝗿 𝘄𝗵𝗮𝘁 𝗰𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀.

#MAGIC #MedicalAI #AI #LLM #GlobalHealth #ResponsibleAI #HealthEquity #DukeNUS @dukenus

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