Technology in Healthcare

The NYT just reported that patients are uploading entire medical records into chatbots - but the risks are not what most people think. Patients are pasting labs, imaging, clinical notes, and oncology reports directly into LLMs. • 26-year-old told her labs “most likely” indicated a pituitary tumor. MRI: normal • 63-year-old advised to escalate to catheterization. Found ~85% LAD stenosis Because of how the chatbot responds, many assume the AI reasons about their symptoms and medical record the same way a clinician does. But AI systems are capable of both meaningful help and serious error, without any calibration signal visible to the user. Most worry about wrong AI recommendations. But the bigger risk is what the AI does not say. 📊 Harm preprint study A new Stanford-Harvard study (David Wu, MD, PhD, Fateme (Fatima) Nateghi, Adam Rodman, Jonathan H. Chen et al.) evaluated 31 models on 100 real outpatient eConsult cases across 10 specialties: - 4,249 management actions - 12,747 expert ratings Severe harm per 100 cases: - Best models: ~12–15 - Worst models: ~40 ~77% of severe harms were omissions: - Not ordering a critical test - Missing a needed referral - Neglecting follow-up suggestions 🔷 Additional findings: 1) Top models outperformed generalists using conventional resources (though these were difficult eConsult cases that PCPs were posing to specialists) 2) No link between safety and model size, recency, “reasoning modes,” or standard benchmarks 3) Multi-agent + RAG approaches reduced harm; heterogeneous ensembles had ~6× higher odds of top-quartile safety 📌 Implications When a patient asks AI for medical advice, the primary risk is not incorrect recommendations. It's neglecting critical actions a clinician might suggest (notably, humans also make a lot of mistakes). ⚠️ Why this matters 1) 2/3 of US physicians report using LLMs, and millions of patients. Errors will become more subtle as models get better. Both harms of omissions and commission will become harder for clinicians (and especially patients) to detect. 2) Sampling a few outputs is not enough: clinical AI evaluation needs explicit, systematic harm measurement on real cases, not just performance or accuracy on knowledge benchmarks. 3) If we don’t measure omission harms, we will systematically underestimate risk. 🔴 Open Call: State of Clinical AI Report (Jan 2026) The ARISE Network (Stanford + Harvard) is compiling a State of Clinical AI Report for 2026. Audience: health system leaders, clinicians, researchers, tech/pharma, media, investors 2025 peer reviewed and preprint studies within scope: • Clinical AI (doctor- or patient-facing) • Benchmarks, evaluations, real-world deployments, prospective trials • Workflow, outcomes, and implementation studies 📅 Submission deadline: Dec 21, 2025 - Comment with study link + 1–2 sentences on key findings and why it matters - We will follow up with a one-slide reference example for invited submissions

MIT just cleared 50% of Alzheimer's plaques using 40 Hz sound waves. No drugs. No surgery. Just precisely engineered frequencies making immune cells devour toxic proteins. Frequency is becoming medicine's most powerful tool. Think about that. While we've spent decades failing with Alzheimer's drugs, MIT researchers discovered something extraordinary: exposing brains to 40 Hz gamma frequencies activates microglia—the brain's cleanup crew—to clear amyloid plaques naturally. Mice regained memory. Human trials are showing promise. This isn't alternative medicine. It's FDA-approved precision. Traditional Brain Treatment: ↳ Invasive surgery with months of recovery ↳ Drugs that barely slow decline ↳ Blood-brain barrier blocking 98% of medications ↳ Essential tremor requiring skull opening The Frequency Revolution: ↳ 60% tremor reduction in one ultrasound session ↳ Same-day discharge, no incisions ↳ Drug delivery increased 5-fold to brain tumors ↳ 90+ clinical trials transforming neurology But here's what stopped me cold: Focused ultrasound doesn't destroy tissue—it tunes it. Opening the blood-brain barrier for exactly 4 hours to deliver chemotherapy. Synchronizing neurons at 40 Hz to trigger natural healing. Making Parkinson's tremors vanish while patients stay awake, go home that afternoon. We're not attacking disease anymore. We're conducting it away. What changes everything: ↳ Brain surgery without cutting ↳ Alzheimer's clearing without drugs ↳ Tumors targeted without systemic poison ↳ Healing through harmony, not harm The Multiplication Effect: 1 frequency device = surgery avoided 10 hospitals equipped = tremor wards emptying 100 conditions targeted = non-invasive becomes standard At scale = medicine's violent era ends Stanford uses ultrasound for depression. Johns Hopkins for addiction. Mayo Clinic for brain tumors. Each discovering that precisely tuned frequencies can reprogram biology better than any drug. We spent centuries cutting and poisoning disease. Now we're tuning it out of existence. Because when 40 Hz can clear plaques that billion-dollar drugs couldn't touch, and ultrasound can perform brain surgery without a scalpel, we're not just advancing medicine. We're using medical precision. Follow me, Dr. Martha Boeckenfeld for breakthroughs where physics becomes pharmacy. ♻️ Share if you want other to learn about new possibilities to fight Alzheimer. Resources: Gamma frequency entrainment attenuates amyloid load and modifies microglia" Authors: Li-Huei Tsai et al. NatureDecember 2016 DOI: 10.1038/nature20587. Gamma frequency sensory stimulation in mild probable Alzheimer’s dementia: Phase 2A pilot study" PLOS Biology, November 30, 2022 Evidence that 40Hz gamma stimulation promotes brain health,” Li-Huei Tsai, PLOS Biology, 2025.

5 key developments this month in Wearable Devices supporting Digital Health ranging from current innovations to exciting future breakthroughs. And I made it all the way through without mentioning AI… until now. Oops! >> 🔘Movano Health has received FDA 510(k) clearance for its EvieMED Ring, a wearable that tracks metrics like blood oxygen, heart rate, mood, sleep, and activity. This approval enables the company to expand into remote patient monitoring, clinical trials, and post-trial management, with upcoming collaborations including a pilot study with a major payor and a clinical trial at MIT 🔘ŌURA has launched Symptom Radar, a new feature for its smart rings that analyzes heart rate, temperature, and breathing patterns to detect early signs of respiratory illness before symptoms fully develop. While it doesn’t diagnose specific conditions, it provides an “illness warning light” so users can prioritize rest and potentially recover more quickly 🔘A temporary scalp tattoo made from conductive polymers can measure brain activity without bulky electrodes or gels simplifying EEG recordings and reducing patient discomfort. Printed directly onto the head, it currently works well on bald or buzz-cut scalps, and future modifications, like specialized nozzles or robotic 'fingers', may enable use with longer hair 🔘Researchers have developed a wearable ultrasound patch that continuously and non-invasively monitors blood pressure, showing accuracy comparable to clinical devices in tests. The soft skin patch sensor could offer a simpler, more reliable alternative to traditional cuffs and invasive arterial lines, with future plans for large-scale trials and wireless, battery-powered versions 🔘According to researchers, a new generation of wearable sensors will continuously track biochemical markers such as hydration levels, electrolytes, inflammatory signals, and even viruses, from bodily fluids like sweat, saliva, tears, and breath. By providing minimally invasive data and alerting users to subtle health changes before they become critical, these devices could accelerate diagnosis, improve patient monitoring, and reduce discomfort (see image) 👇Links to related articles in comments #DigitalHealth #Wearables

This $2M machine saves many of the 15M+ lives affected by stroke every year. You lose 2M neurons/min during a stroke and have ~4.5hrs to live. New Computed tomography (CT) perfusion tech extends that window to 24hrs. Yet we know so little about these life saving devices.. The hardware. A rotating X-ray tube spun at 10,000 RPM shoots high-energy beams through your brain while iodine drip flows in your vessels. 128-640 rows of scintillator detectors capture X-rays every few microseconds. The scan takes 30-60s. Each scan generates >100GB raw data. Custom ASICs & GPU clusters process this in real-time, handling 10^9 data points. The image reconstruction pipeline in C++/CUDA uses deconvolution algos to convert X-ray attenuation data into high-def blood flow maps. Takes < 2min. Companies like Rapid AI & Viz ai revolutionized interpretation. Their deep learning systems analyze perfusion maps in minutes, automatically alerting stroke teams. What took experts hours can now happen fast enough to save critical brain tissue. Takes 2-3mins. The entire process, from door to completed scan is done in 15-20mins. Four giants dominate the space — Siemens' SOMATOM Force claims best speed — GE Revolution claims best AI — Canon Aquilion claims widest coverage — Philips claims unique spectral imaging Two trials changed everything in 2018. DAWN showed 49% good outcomes vs 13% control up to 24hrs after stroke. DEFUSE 3 proved similar results up to 16hrs. Both used CT Perfusion to find salvageable tissue, revolutionizing the "time is brain" paradigm. Before, doctors just used time (4.5hr) after which treatment risk outweighed benefits. Now, we can see exactly which brain tissue is dead (red) vs salvageable (green). Some people's backup blood vessels keep tissue alive for 24hrs - we can spot and save them. CT Perfusion isn't just for strokes: — helps catch aggressive cancers — guides biopsies — finds blocked heart arteries — spots internal bleeding — checks if treatments work By tracking blood flow anywhere in the body, it saves lives in many ways. The tech industry rarely talks about breakthroughs in healthcare and medical imaging. CT Perfusion is just one such technology that combines hardware and software innovation to beat the clock in stroke care.

Half a million genomes. 1.5 billion variants. One breakthrough: we are all truly unique. Twenty years ago, the Human Genome Project took 13 years and $2.7B to sequence a single genome. Today? We can sequence a genome in less than 24 hours for under $1,000. Last week, UK Biobank released 490,640 whole genomes — the largest genetic dataset ever (Nature, 2025). What did we learn? • Each person carries 4–5 million variants • 76% appear in fewer than 10 people — your genome is almost entirely yours • 1 in 10 carries clinically actionable mutations where doctors can intervene today (e.g., BRCA1/2 for cancer, LDLR for heart disease) Why it matters: • Previous genetic tests captured ~6% of human variation. This dataset reveals 40× more • In non-coding regions — the biological switches controlling genes — researchers found 63 new disease associations • Adding 31,785 non-European genomes uncovered 82 disease links invisible in Eurocentric studies From genetics to health impact This transforms medicine today: • Prevention - Polygenic risk scores flag disease decades before symptoms • Diagnosis - Rare disease patients waiting years for answers finally find them • Treatment - Pharmacogenomics matches the right drug, right dose, to your genome The next frontier: genetics + everything else Genetics is the hardware. Health is the software running in real time. Your DNA is fixed, but biology is dynamic, shaped by: • Epigenetics: how environment and lifestyle switch genes on/off • Proteomics & metabolomics: molecular signals revealing your current health state • Digital biomarkers: continuous data from stress, sleep, glucose, heart rate • Stress biology & neuroendocrine signaling: how cortisol and brain-body responses reshape your health trajectory Layer these dynamic signals onto genetic foundations, power them with AI, and you create living health models, not just predicting disease, but understanding when, why, and how it manifests in YOU. The critical question? We've spent decades treating the "average patient" — who doesn't exist. Now we can better see each person as they truly are: biologically unique, dynamically changing, infinitely complex. The healthcare winners of the next decade won't just collect data: they'll integrate genetics, epigenetics, molecular and phenotypic tests, lifestyle, stress biology, and digital signals to deliver truly personalized, preventive care at scale. There is no "normal" genome, only 8 billion unique experiments in being human. And we just decoded the first half million. 👉 Which excites you more: knowing your genetic blueprint, or understanding how your daily choices rewrite it?

No more knee replacement surgery? We can now re-grow knee cartilage with a single injection. When knee cartilage wears away with age, every step becomes painful. This affects 1 in 5 American adults and costs $65 billion a year in medical bills. Dr. Nidhi Bhutani and her team at Stanford Medicine might have solved this problem forever. Here's how 👇 ▶ They found a molecule that blocks 15-PGDH This protein builds up in your knees and wears away cartilage. The team identified a compound that stops this process. They injected it into old mice with worn-out knees. The cartilage grew back - thick and strong. ▶ It stopped arthritis before it started Half of all people who tear their ACL develop arthritis. When tested in mice with torn ligaments, the injection prevented arthritis from developing. ▶ It worked on human tissue They tested it on actual knee tissue from patients undergoing knee replacements. After one week, the cartilage started re-growing.  Not just healing. Re-growing. ▶ A pill version is already in clinical trials An oral version of this treatment is now being tested on humans. If it works, it could eliminate the need for knee replacements entirely - a surgery performed over 1 million times a year in the US alone. I believe this is the kind of breakthrough healthcare needs. Not just managing decline, but reversing it. Because the alternative isn't just surgery. It's millions of people losing mobility, independence, and quality of life because their bodies aged. Do you think this will eventually replace joint replacement surgeries? #entrepreneurship #healthtech #innovation

A new study confirms what we already knew -- physicians spend a ton of time using EHRs! I’ll summarize the findings and share my contrarian interpretation. Researchers examined Epic Signal metadata from 200,000+ physicians across nearly 400 organizations to determine EHR time by specialty and across different functions (documentation, chart review, orders, inbox) during and outside clinical hours. [doi:10.1007/s11606-024-08988-0] They found: 1️⃣ We spend an average of 5.8 hours working in the EHR for every 8 scheduled patient care hours. 2️⃣ 42% of this occurs outside clinic hours (after hours and on non-clinical days). 3️⃣ We spend the most time on documentation > chart review > orders > inbox > other activities. 4️⃣ PCPs and cognitive-based specialists spend far more time in the EHR (ID > endocrinologists > nephrologists > PCPs) than procedural-based specialists (anesthesiologists < orthopedics). (see figure) This isn’t new news, but it’s useful data. And it's easy to imagine how this will fuel the anti-EHR bonfire. My take: 1️⃣ Everyone loves a boogeyman. Because EHRs are so central to clinical work, they make an easy scapegoat. While EHRs cause headaches, it’s silly to blame them for all (or most of) our problems. We must consider various other factors – especially clinical and process complexity. 2️⃣ EHRs bring many underappreciated benefits. They actually make many tasks easier. EHRs have eliminated regular trips to the medical records department, waiting for faxes, and struggling to decipher illegible notes. They make it easy to communicate with other teammates and with patients. 3️⃣ As is often the case, physicians who perform procedures have it somewhat easier than those who do not. PCPs, endocrinologists, nephrologists, and ID physicians (the top 4 in time spent) do mostly cognitive work and little/no procedures. I do both and spend far more time in the EHR on days I’m in the clinic than on days I perform endoscopic procedures. 4️⃣ Clinical work starts well before and continues long after seeing the patient. We do a lot more than an RVU can capture. 5️⃣ Not all time in the EHR is wasted! We too easily forget that we using the EHR is far more than simply typing, toggling, pointing and clicking. Notwithstanding the waste, junk, and nonsense, when using EHRs, we are synthesizing information. We are communicating. We are thinking. We are practicing medicine. Computers are central to all modern knowledge work. We don’t ask how much time lawyers, bankers, accountants, or writers spend online during and after working hours. None of this is to say EHRs are not a source of problems. We need better designs, new tools (hello AI), better configurations, better workflows, and right-sized teams. But let's resist overly simplistic views and stop reflexively equating all time spent on EHRs as intrinsically wasteful or soul-crushing. It isn’t.

One of the most exciting shifts happening in healthcare today isn't just about detecting chronic diseases earlier—it's about empowering primary care physicians to act on that information in real time. This week, our Counterpart Health subsidiary released new results showing that having a relationship with a primary care physician that uses Counterpart Assistant is associated with meaningfully better outcomes for patients with congestive heart failure (CHF) enrolled in Clover Health’s Medicare Advantage plans:  - 18% lower all-cause hospitalizations - 25% lower 30-day readmissions At first glance, these stats are impressive on their own. CHF is a leading cause of hospitalizations among seniors, and interventions that move the needle even slightly are rare. But the bigger story here is what these results say about the future of healthcare: technology that works with physicians—rather than burdening them—is how we bend both the quality and cost curves. Most healthcare technology has asked physicians to do more: more documentation, more box-checking, more clicks. We've taken a different approach. Counterpart Assistant delivers clinical-grade insights directly into the physician workflow—designed not to add burden, but to augment their decision-making and make high-quality care easier to deliver, not harder. This impact compounds over time. It isn’t just about identifying one disease a little earlier or generating one better HEDIS score. It’s about embedding intelligence into the day-to-day fabric of primary care, so that complex, high-burden diseases like heart failure can be managed more proactively, more thoughtfully, and ultimately with fewer hospitalizations and better patient lives. We believe this kind of physician enablement isn’t optional if value-based care is going to succeed at scale—it’s foundational. And as our latest report continues to show, when you align technology, physician experience, and patient outcomes the right way, you don’t have to choose between better quality and lower cost. You can achieve both. #CloverHealth #HealthcareInnovation #ClinicalAI #PrimaryCare #MedicareAdvantage #ValueBasedCare #CongestiveHeartFailure

Our research center in Princeton has become a magnet for healthcare AI expertise. Every time I catch up with Dorin Comaniciu and the team there, conversations quickly move from what’s possible to what really matters in healthcare delivery. Take for instance, our work on what we call the Operational Twin, an advisory service. It starts with creating a virtual representation of a clinical department, reflecting how patients, staff, and equipment interact in everyday operations so that different scenarios can be explored more safely and at scale. By simulating billions of scenarios representing dynamic conditions, AI agents learn how operational decisions shape outcomes. They can begin to anticipate bottlenecks and understand the long-term impact of short-term choices. The goal is more efficient planning of patient schedules, staffing, and equipment use, aligning daily decisions with broader clinical and organizational priorities. This becomes even more relevant as clinical innovations accelerate workflows. Faster scanning technologies such as Deep Resolve can shorten patient timeslots and an Operational Twin can help organizations adapt by optimizing schedules and resources to fully realize gains in speed and throughput. At its core, this work is about creating clarity in complex systems so that action becomes more precise and more purposeful. We see a similar principle in clinical innovation. With photon counting CT, we can visualize the heart in extraordinary detail, including structures inside the left ventricle that were previously difficult to see clearly. That deeper insight is captured by a Foundation Model that could help physicians guide ablation therapies with greater precision and confidence, especially when combined with live ultrasound to support real-time decision making in the procedure room. In both cases, whether in clinical imaging or in operations, the ambition is the same: better insight leading to better decisions at the moments that matter most for patients. 𝘋𝘪𝘴𝘤𝘭𝘢𝘪𝘮𝘦𝘳: 𝘛𝘩𝘦 𝘱𝘳𝘰𝘥𝘶𝘤𝘵𝘴/𝘧𝘦𝘢𝘵𝘶𝘳𝘦𝘴 𝘢𝘯𝘥/𝘰𝘳 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘰𝘧𝘧𝘦𝘳𝘪𝘯𝘨𝘴 𝘮𝘦𝘯𝘵𝘪𝘰𝘯𝘦𝘥 𝘩𝘦𝘳𝘦 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘺𝘦𝘵 𝘢𝘷𝘢𝘪𝘭𝘢𝘣𝘭𝘦 𝘪𝘯 𝘢𝘭𝘭 𝘤𝘰𝘶𝘯𝘵𝘳𝘪𝘦𝘴. 𝘐𝘧 𝘵𝘩𝘦𝘴𝘦 𝘴𝘦𝘳𝘷𝘪𝘤𝘦𝘴 𝘢𝘳𝘦 𝘯𝘰𝘵 𝘮𝘢𝘳𝘬𝘦𝘵𝘦𝘥 𝘪𝘯 𝘤𝘦𝘳𝘵𝘢𝘪𝘯 𝘤𝘰𝘶𝘯𝘵𝘳𝘪𝘦𝘴 𝘧𝘰𝘳 𝘭𝘦𝘨𝘢𝘭 𝘰𝘳 𝘰𝘵𝘩𝘦𝘳 𝘳𝘦𝘢𝘴𝘰𝘯𝘴, 𝘵𝘩𝘦 𝘴𝘦𝘳𝘷𝘪𝘤𝘦 𝘰𝘧𝘧𝘦𝘳𝘪𝘯𝘨𝘴 𝘤𝘢𝘯𝘯𝘰𝘵 𝘣𝘦 𝘨𝘶𝘢𝘳𝘢𝘯𝘵𝘦𝘦𝘥. 𝘍𝘰𝘳 𝘮𝘰𝘳𝘦 𝘪𝘯𝘧𝘰𝘳𝘮𝘢𝘵𝘪𝘰𝘯, 𝘱𝘭𝘦𝘢𝘴𝘦 𝘤𝘰𝘯𝘵𝘢𝘤𝘵 𝘺𝘰𝘶𝘳 𝘭𝘰𝘤𝘢𝘭 𝘚𝘪𝘦𝘮𝘦𝘯𝘴 𝘏𝘦𝘢𝘭𝘵𝘩𝘪𝘯𝘦𝘦𝘳𝘴 𝘳𝘦𝘱𝘳𝘦𝘴𝘦𝘯𝘵𝘢𝘵𝘪𝘷𝘦.

AI in healthcare is not simply another technology upgrade. It is a matter of trust, safety, and ultimately, human life. In many sectors, an AI error might lead to inconvenience or financial loss. In healthcare, an AI error can mean a missed diagnosis, an inappropriate treatment pathway, or avoidable harm. That is why AI adoption in healthcare must be held to a higher standard than in almost any other industry. It requires deeper validation, stricter governance, and human guardrails at every stage. A framework I find particularly helpful is 𝐀𝐈 + 𝐑𝐀𝐂𝐓⁣, strengthened through a Human-Centred AI lens. 𝐑 = 𝐑𝐞𝐚𝐝𝐢𝐧𝐞𝐬𝐬⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣The risk begins long before deployment. If clinical data is incomplete, biased, or unrepresentative, AI systems can fail quietly, often affecting the most vulnerable populations first. Readiness must include: →Data integrity and provenance →Regulatory compliance →Clear clinical problem definition →Ethical and patient safety accountability 𝐀 = 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣In healthcare, adoption is not about installing a tool, it is about integrating it into clinical judgment. The risk is over-reliance, alert fatigue, or the introduction of friction into already pressured workflows. Human-centred adoption means: →Clinicians remain firmly in the loop →AI outputs are explainable and challengeable →Training supports human-AI collaboration, not replacement 𝐂 = 𝐂𝐚𝐩𝐚𝐛𝐢𝐥𝐢𝐭𝐲⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣Healthcare AI is not static. Models drift, populations change, and clinical practice evolves. The risk is that a system that appears safe today may not remain safe tomorrow. Capability requires: →Continuous monitoring and evaluation →Governance structures spanning clinicians, data, ethics and risk →Ongoing validation, not one-off approval 𝐓 = 𝐓𝐫𝐚𝐧𝐬𝐟𝐨𝐫𝐦𝐚𝐭𝐢𝐨𝐧⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣True transformation is not automation for its own sake. The risk of scaling without safeguards is amplified inequity, diminished patient trust, and decision-making that feels outsourced. Transformation must prioritise: →Better patient outcomes and experience →Equity across communities →Shared decision-making, supported, not replaced, by AI The central truth is this: 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐀𝐈 𝐢𝐬 𝐧𝐨𝐭 𝐜𝐨𝐧𝐬𝐮𝐦𝐞𝐫 𝐭𝐞𝐜𝐡𝐧𝐨𝐥𝐨𝐠𝐲. 𝐈𝐭 𝐢𝐬 𝐬𝐚𝐟𝐞𝐭𝐲-𝐜𝐫𝐢𝐭𝐢𝐜𝐚𝐥.⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣ ⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣⁣Progress must be ambitious, but responsibility must be uncompromising. The question is not whether AI will shape the future of care. It is whether we shape it with the rigour, humility, and human focus that patients deserve. What is the single most important gate check you insist on before scaling AI in clinical environments? ♻️ Share if this resonates ➕ Follow (Jyothish Nair) for reflections on AI, change, and human-centred AI #ResponsibleAI #AI #DigitalTransformation #HumanCentredAI