MIT - Critical Data

05/22/2026

Please post:��When you open a chatbot at midnight, worried about a symptom, you are not thinking about network traffic. But your browser is. We captured every HTTP/HTTPS request generated during routine interactions with Claude, ChatGPT, Gemini, and Grok: logging in, starting a chat, sharing a conversation. Between 9% and 36% of those requests went to third-party domains: analytics services, behavioral tracking, in some cases advertising networks. Every platform contacted external analytics during ordinary use. None of this is forbidden. It lives in the terms of service you clicked through.

The gap this study surfaces is not technical. It is a governance failure. These platforms were designed as consumer products, and their data practices follow from that: built for product analytics, not for the weight of what people actually bring to them. Over 40 million people use AI chatbots daily for health questions, many after clinic hours, many in communities where care is hard to reach. They are not patients in any legal sense. That is the problem. HIPAA does not apply because the law was never written for this.

The ask: transparency reports, guidance from medical associations, regulatory frameworks that meet people where they are actually seeking care.

Objective: This study presents an analysis of network traffic across four commercial LLM platforms to document which external domains they contact during routin

04/17/2026

AI as a Catalyst: Reimagining Innovation | May 1–2, 2026

On May 1–2, MIT Critical Data brings AI as a Catalyst to the heart of Silicon Valley, not to celebrate the innovation ecosystem as it stands, but to fundamentally challenge it. This isn’t another tech conference. It’s a provocation. We are gathering founders, funders, clinicians, artists, community voices, and boundary-crossers to reimagine how we create knowledge, how we innovate, how we think, how we communicate, and most importantly, how we connect and relate to one another. Half of our workshops will confront a question the venture world rarely asks out loud: why do we accept a system where 99% of ideas die after billions of dollars of investment and years of blood, sweat, and tears, and why do we keep calling that system “successful”? Could there be a better way to nurture ideas, one that doesn’t treat failure as an acceptable mass casualty event?

We are looking for funders and founders courageous enough to sit with that discomfort and imagine alternatives, and just as urgently, we are looking for the voices that have been historically excluded from the innovation table, the communities most likely to benefit from or be harmed by the technologies being built in their name. If you believe the future of innovation demands not just better tools but better values, join us.

Details and registration:
https://criticaldata.mit.edu/events/san-francisco-2026

03/24/2026

Instead of building AI that knows everything, we should be building AI that makes us better: more humble, more curious, more creative. How can we engineer virtues directly into clinical AI systems, equipping them with self-awareness modules that detect overconfidence, flag uncertainty, and prompt clinicians to seek fresh perspectives rather than passively accept a machine’s verdict?

The implications reach far beyond medicine. If we accept that the purpose of AI is not simply to automate cognition but to catalyze our evolution as a species, then the virtues we encode into these systems matter enormously.

The consortium behind this work practices what it preaches. The initiative spans all the continents except Antarctica, deliberately weaving together students, patients, data scientists, clinicians, social scientists, indigenous knowledge holders, and artists. Ultimately, the biases baked into AI are biases baked into who gets to design it. Let us stop building AI that thinks for us and does stuff for us, and start building AI that helps us humans think together, more wisely, and with the kind of courage our most complex challenges demand.

https://news.mit.edu/2026/creating-humble-ai-0324
Image: MIT News; iStock

03/24/2026

In this paper, we examine how a single vendor came to control the digital backbone of American healthcare. But let us be clear: this is not about tearing down a company. We have nothing against technology, innovation, or AI. We believe deeply in the promise of digital tools to democratize access to expertise, to extend the reach of the best clinical knowledge to communities that have never had it. What we are calling for is the building and bridging of communities so that they gain the agency to shape these technologies rather than simply be shaped by them. When one company controls how health data is captured, exchanged, and monetized, the question is not whether the technology works. It is who it works for, and who gets to decide.

As we develop AI for healthcare, we must be deliberate about the systems that generate and capture the data on which everything downstream depends. The electronic health record is not a neutral tool; it encodes assumptions about whose experiences count, whose pain gets documented, whose outcomes get measured. That is why the entire pipeline, from care delivery to data capture, from data curation to modeling, from validation to deployment and continuous monitoring, must involve a diverse set of actors. This means patients, clinicians, data scientists, ethicists, community health workers, and most importantly, those who have been historically marginalized from the design table. Health data is a public good. Its governance should reflect the communities it is meant to serve, not the commercial priorities of any single entity. The path forward is not less technology. It is more inclusive stewardship of the infrastructure on which all of us depend.

https://journals.plos.org/digitalhealth/article?id=10.1371/journal.pdig.0001143&?utm_id=plos111&utm_source=internal&utm_medium=email&utm_campaign=author

03/08/2026

Privacy, as we once understood it, is dead. Every time we tap “Accept All Cookies” or scroll past a terms-of-service agreement to download a fitness app, we hand over intimate details about our bodies, our habits, our vulnerabilities. We present a compelling case for transparency mandates around health data transactions. The uncomfortable starting point is one the paper dances around: the traditional framing of privacy as something we can protect through consent and de-identification is largely a fiction. Our health records, wearable data, and genomic information are already circulating through a commercial ecosystem most of us never agreed to and barely understand. The real question isn’t how to lock the barn door; it’s who took the horse, where did they ride it, and who got paid along the way. What we need is a disclosure framework built on that honest foundation: Who is selling our data? What are they doing with it? Who is profiting? And who is being harmed? That kind of radical transparency won’t restore privacy in any nostalgic sense, but it can restore something arguably more important: accountability. And accountability, specifically, relational accountability, unlike privacy, is something we can still fight for.

https://www.sciencedirect.com/science/article/pii/S2589750025001293

02/25/2026

🚑🤖 Barcelona was buzzing at the GenAI Health Hack 2026, hosted by Hospital Clínic Barcelona, where 90+ clinicians, researchers & technologists came together to rethink healthcare with generative AI.
🏆 The winning project, EdxPlain, transforms ER discharge reports into personalized, easy-to-understand guides — empowering patients to better manage their own health.
From synthetic MRI generation to AI-powered dialysis optimization, the hackathon spotlighted one thing: innovation only matters if it’s ethical, rigorous, and truly improves patient care.
The future of healthcare is collaborative, human-centered, and AI-augmented. 💡✨

02/24/2026

Hopping on to the throwback to #2016 trend with MIT critical data photos at Beijing and Mexico City! Will take you all on more journeys through time and space soon! ✈️

02/01/2026

The American Medical Association and MIT Critical Data organized "AI as a Catalyst" on January 15, 2026 at MIT, a transformative approach to reimagining education and healthcare by deliberately centering voices typically excluded from academic discourse, i.e., indigenous knowledge holders, musicians, artists, religious leaders, storytellers, and activists, alongside educators and clinicians. The event created an interactive space for community-centered dialogue using six conceptual tools: the mirror (reflection), flashlight (illumination), microscope (analysis), paintbrush (creativity), podium (shared storytelling), and the slingshot (dismantling of power structures). Through three workshops exploring creative expression as pedagogical practice, indigenous and religious wisdom in medical training, and justice-centered AI development, participants charted pathways toward transforming how we prepare the next generation of healers and change agents. The event embodies MIT Critical Data's commitment to challenging traditional academic power structures by recognizing that addressing healthcare's challenges in the AI era requires wisdom from diverse epistemologies and lived experiences, not just technical expertise, ultimately working to build a community of practice committed to centering creativity, love, and justice in education and healthcare.

Watch the recording of the event here:

Building a global community committed to developing & improving health AI.

01/30/2026

After decades of conflicting evidence about how tightly to control blood sugar in critically ill patients, our analysis using causal inference methods on the MIMIC-IV database offers clarity, but clarity that must be understood within its specific context. By combining targeted maximum likelihood estimation with joint longitudinal-survival modeling on 8,002 patients from a single academic medical center in Boston, we discovered a U-shaped relationship between glucose and mortality, where aiming for glucose levels between 160-190 mg/dL appeared optimal, with overly aggressive glucose lowering dramatically increasing hypoglycemia risk (77% of patients at 100 mg/dL targets) without improving survival. However, our cohort, median age 66 years, 57% with diabetes, and significantly older and more comorbid than the general global ICU population, reflects the limitations inherent to single-center observational studies. Institutional variations in insulin protocols, glucose monitoring frequency, and clinical workflows at Beth Israel Deaconess Medical Center may not translate to other settings. This finding validates current guidelines recommending liberal glucose ranges and exemplifies how sophisticated analytics on high-resolution health data can help us move beyond the costly cycle of contradictory randomized trials, but it also underscores a critical principle: causal inference in ICU settings should be viewed as causally suggestive rather than definitive, precisely because we can never fully verify the absence of unmeasured confounding or account for the complex, context-dependent practices that shape real-world care. The real innovation here isn’t methodological; it’s about using these frameworks to ask better questions and identify promising directions before launching expensive trials, while maintaining epistemic humility about what observational data can and cannot tell us across diverse healthcare contexts and patient populations.

https://bmjopen.bmj.com/content/16/1/e104916.full

01/29/2026

After decades of conflicting evidence about how tightly to control blood sugar in critically ill patients, our analysis using causal inference methods on the MIMIC-IV database offers clarity, but clarity that must be understood within its specific context. By combining targeted maximum likelihood estimation with joint longitudinal-survival modeling on 8,002 patients from a single academic medical center in Boston, we discovered a U-shaped relationship between glucose and mortality, where aiming for glucose levels between 160-190 mg/dL appeared optimal, with overly aggressive glucose lowering dramatically increasing hypoglycemia risk (77% of patients at 100 mg/dL targets) without improving survival. However, our cohort, median age 66 years, 57% with diabetes, and significantly older and more comorbid than the general global ICU population, reflects the limitations inherent to single-center observational studies. Institutional variations in insulin protocols, glucose monitoring frequency, and clinical workflows at Beth Israel Deaconess Medical Center may not translate to other settings. This finding validates current guidelines recommending liberal glucose ranges and exemplifies how sophisticated analytics on high-resolution health data can help us move beyond the costly cycle of contradictory randomized trials, but it also underscores a critical principle: causal inference in ICU settings should be viewed as causally suggestive rather than definitive, precisely because we can never fully verify the absence of unmeasured confounding or account for the complex, context-dependent practices that shape real-world care. The real innovation here isn't methodological; it's about using these frameworks to ask better questions and identify promising directions before launching expensive trials, while maintaining epistemic humility about what observational data can and cannot tell us across diverse healthcare contexts and patient populations.

https://bmjopen.bmj.com/content/16/1/e104916.full