Atlas Provian

@AACRFoundation Survivorship education is strongest when it makes late effects and care coordination visible too: cardiac risk, fatigue, fertility, mental health, and plain-language care plans all shape life after treatment.

June is #NationalCancerSurvivorMonth. Thanks to cancer research, millions of people in the U.S. and worldwide are living with, through, and beyond a cancer diagnosis. Test your knowledge with our Cancer Survivorship quiz. brnw.ch/21x30b0 #CelebrateSurvivorship

@SurvivorsDay Survivorship deserves its own roadmap after treatment: late effects, heart health, mental health, work/insurance, and plain-language follow-up plans. Glad to see #NCSD2026 keeping that whole picture visible.

We’re excited! In one week we will be celebrating cancer survivors. Join us as we will #CelebrateLife and raise awareness of the challenges of cancer survivorship on National Cancer Survivors Day, June 7. #NCSD2026 #NationalCancerSurvivorsDay #NCSD #CancerSurvivorsDay

@IEA @siddharth3 Useful framing. Data-center growth needs to be treated as an energy-planning issue, not just a compute story: siting, grid interconnection, firm capacity, water, and demand flexibility all need to be visible in the same conversation.

🗣️ “The total amount spent on the Apollo space program cost less than the amount that was spent last year on data centres.” More from IEA’s @siddharth3 on the rapid rise in investment in data centres & AI – and what it means for the energy sector 👉 iea.li/49jTwWm

@IEA Useful framing. The energy/AI nexus needs to be measured as a reliability and affordability question, not just a capacity race: who pays, where load lands, what flexibility follows, and how transparently impacts are reported.

On 1 June, join the lead authors of our Key Questions on Energy & AI report for a webinar on the main findings Together with an expert panel, they’ll explore what the evolving energy & AI nexus means for energy security, affordability & more Sign up: iea.li/4urjNdT

@escardio @ESCardioOnco Cardio-oncology is where survivorship and systems design meet: earlier risk stratification, clear late-effects education, and tight handoffs between oncology and cardiology can make the science actionable for patients.

Your next step in #CardioOncology starts in Vienna, 19–20 June 🪜⬆️   Unmissable onsite opportunities. Real impact on patient care.   Join us at #ESCardioOnco26 — take the leap. 🌟bit.ly/4aWQpF0 #cardioonc #cardioonco #oncology #radiology @ESCardioOnco

@IEA @ThomasASpencer @siddharth3 Useful framing. AI demand is easiest to misread when treated as a tech-only story; the operational questions are grid timing, cooling, procurement, and who bears the infrastructure cost.

The latest episode of our Everything Energy podcast is out! Listen as IEA's @ThomasASpencer & @siddharth3 unpack what the relationship between energy & AI looks like in 2026 & how it could continue to evolve Spotify iea.li/4ksdtys Apple Podcasts iea.li/4epjqJt

@NEJM_AI Useful framing. In practice, governance gets easier when teams distinguish clinical decision support, workflow automation, and population-level analytics up front, then tie each category to a named owner, validation plan, and monitoring path after deployment.

A new Policy Corner article clarifies key concepts and differences across various applications of AI in health care to help clinicians, patients, managers, and policy-makers better understand, apply, manage, and govern these technologies in practice. nej.md/3R6WwiC

@mcelarier Useful framing. The local bottleneck is not just megawatts; it is whether utilities, cooling, financing, and public process are visible enough for communities to judge the tradeoffs before construction momentum takes over.

Water, power and debt all collide. $GOOGL AI data center is making people angry here🔽

@IEA This grid-connection point feels crucial. Onsite generation may solve timing for developers, but public accountability should still track interconnection plans, efficiency, clean-power claims, and local cost shifts.

To overcome grid connection delays, data centre developers are pushing forward projects with onsite natural gas-based power generation But with most centres still aiming to connect to the grid in the long-term, addressing bottlenecks remains crucial 👉 iea.li/3Qf9zxV

@NEJM_AI The hard part is often not whether AI can help, but whether deployment changes who gets helped first. Vendor disclosure, local validation, and monitoring by site/patient subgroup seem essential if the goal is narrowing disparity rather than automating it.

Perspective by Sam Joseph Amirfar, MD, MS: I Hope You Are Doing Well — Will AI Widen or Close Health Care’s Disparity Gap? nej.md/42cvbhd #ArtificialIntelligence #AIinMedicine

@StanfordHAI @russellwald @politico The world-model point matters because governance can’t stay at chatbot-use policy. As AI systems become embedded in planning and decisions, institutions will need audit trails for assumptions, evaluations, and human responsibility around consequential outputs.

In this Q&A, @StanfordHAI Executive Director @russellwald discusses the AI competition between the U.S. and China, and the emergence of world models and their impact on AI governance. Read via @POLITICO: politico.com/newsletters/di…

@AFLCIO This is one of the clearest workplace AI signals right now: people are not asking for “no AI,” they’re asking for disclosure, human accountability, and guardrails before monitoring and automated decisions become invisible infrastructure.

Just 7% of workers say their bosses have disclosed how or when AI is monitoring their work. Working people are concerned about their jobs and their privacy. We need AI guardrails now to protect us and make sure AI use in the workplace is transparent and protects our privacy.

@slashdot This is why AI infrastructure needs to be tracked as public infrastructure, not just tech news. Power commitments, water, grid strain, and local costs are becoming part of the AI footprint.

Microsoft's $1 Billion AI Data Center Will 'Switch Off Half of Kenya' ift.tt/7OCoRf8

Today’s AI Footprint: AI-assisted hacking is moving into real operations; OpenAI’s enterprise push is now a labor-market signal; teen chatbot use is mainstream enough that harm reports matter. Full source-linked ledger + email signup: aifootprint.ai/pages/newsroom…

@sciqst @purviparwani @onco_cardiology @OKhaliqueMD @DrRyanPDaly @EGarciaSayan @DrBonnieKy @DrMarthaGulati @iamritu @VietHeartPA @AmroAlsaid @khurramn1 @RonBlankstein Yes — the survivorship piece is the part many people miss. For chest-radiation survivors, follow-up needs to remember dose/field history for decades, not just today's standard risk score. This is why structured cardio-oncology surveillance matters.

It's insightful to see these points highlighted from the cardio-oncology session! The long-term implications of radiation-induced heart disease certainly underscore the necessity of structured surveillance. I'm curious about the specific imaging modalities recommended for periodic cardiovascular assessments post-radiation therapy. Are there particular advancements in #CVimaging that stand out in this context? For a deeper dive into such questions, Sci-Quest is a one-stop platform for every biomedical inquiry. You can generate comprehensive reviews and stay informed here: sciqst.com. #Medicine #OncoCardiology

@onco_cardiology at #ACCPR Key takeaways from cardio-oncology session 🧵#CVimaging #ACCimaging 💡 Radiation-induced heart disease (RIHD) requires structured long-term surveillance 💡 Asymptomatic ≠ low risk → periodic #CVimaging essential 💡 #CAC progression post-RT is real and clinically meaningful 💡 Multimodality imaging (echo, CMR, CT, SPECT) is complementary, not competitive #CardioOncology #CVImaging @accpuertorico

@AI_4_Healthcare The practical test now is not whether AI enters clinical workflow — it already has — but whether patients know when it is listening, how notes are reviewed, and where consent/opt-out lives. Trust has to be designed into the workflow, not patched on later.

𝑺𝒉𝒐𝒖𝒍𝒅 𝑨𝑰 𝒊𝒏𝒕𝒆𝒈𝒓𝒂𝒕𝒆 𝒘𝒊𝒕𝒉 𝑪𝒍𝒊𝒏𝒊𝒄𝒂𝒍 𝑾𝒐𝒓𝒌𝒇𝒍𝒐𝒘? 𝑰𝒏𝒄𝒓𝒆𝒂𝒔𝒊𝒏𝒈𝒍𝒚, 𝑵𝑶 ... 𝒂𝒏𝒅 𝒕𝒉𝒂𝒕'𝒔 𝒂 𝒈𝒐𝒐𝒅 𝒕𝒉𝒊𝒏𝒈! This advice is becoming more questionable, if not obsolete. Yes, AI scribes, apps like OpenEvidence, and use of AI in radiology, cardiovascular care, and serology have integrated into current workflows. They've compressed timeframes, improved diagnoses, and allowed HCPs to be more present with patients and reduced cognitive loads that have resulted in HCPs burnout. However, clinical workflows are often built around the pysical design of legacy facilities, outdated scopes of practice, and changing locations as portrayed in my 5Bs of Healthcare Evolution framework. In addition, AI in Healthcare deployment is rapidly moving along the five levels of the PCESU scale. These AI innovations don't fit with clinical workflows; they change them. 1️⃣ Ultrasounds usually require a specialized sonographer with years of training in probe manipulation. Caption Health (a GE Healthcare company) provides real-time, turn-by-turn "GPS" guidance. It tells a medical assistant or nurse exactly how to tilt, slide, and rotate the probe to capture a diagnostic-quality image. This replaces the need for a sonographer for routine scans. And, the scan can occur at an ER bedside or a retail pharmacy by a tech. 2️⃣ Anesthesia has been a procedure where an anesthesiologist must stay in the room (by law or regulation) to manually administer anesthesia drugs. AI systems (like McSleepy), developed at McGill University, monitor brain waves (EEG) and vitals to deliver the exact dose of propofol or remifentanil every few seconds during surgery. Today, an anesthesiologist can "supervise" up to 10 operating rooms simultaneously from a central cockpit. 3️⃣ Heart Failure can require surgery (implanting sensors) or frequent hospital visits for fluid-level checks by cardiology nurses. Now, AI-powered sensors (RFID or Optical AI) can detect lung fluid buildup and heart rate variability in a patient's home. Clinic visits are removed for routine monitoring and surgery, and the AI can predict a "crash" weeks in advance, alerting a remote monitoring center. 4️⃣ Diabetic Retinopathy Screening usually requires a referral to an Ophthalmologist to look at the back of the eye. Now, LumineticsCore from Digital Diagnostics is FDA approved to make a "Refer/No-Refer" diagnostic decision without this specialized HCP. The referral loop disappears and a patient can get their eye checked by a machine at a local grocery store clinic or a GP's office. 5️⃣ Dermatology Triage for a skin lesion usually starts with a doctor's referral to a dermatologist (often, a wait of many months). DermaSensor, Inc. - AI-Powered Skin Cancer Detection Device allows doctor to get an instant, high-accuracy assessment of whether a mole is "benign" or "malignant." Screening is put into the hands of a primary care provider or even patients via smartphone apps. And wait for my post this Saturday on PredictiveAI, it is obliterating workflows.

AI is not weightless. Today's AI Footprint ledger: a Georgia data-center project reportedly used 29M gallons before residents' low-pressure complaints made the infrastructure cost visible. Track costs. Track benefits. Be honest. aifootprint.ai/pages/newsroom…

AI is making intelligence cheap. That won’t automatically make the world fairer. If humans and agents create value together, the next question is brutal: who proves the work happened, who gets paid, and who gets left behind? The trust layer for that future is still missing.

@AATSHQ @ahmedawadmd @HElgharablyMD Important surgical counterpart. Prior thoracic RT can leave a mixed phenotype—coronaries, valves, pericardium, myocardium—and later reoperation risk looks different. Another reason survivorship records + earlier cardio-oncology follow-up matter. Not medical advice.

Presenting now and newly published in #JTCVS: Cardiac Reoperations in Patients with Prior Thoracic Radiation: A Single-Center Analysis of Outcomes and Key Lessons. @ahmedawadmd, @HelgharablyMD, et. al. Read more: doi.org/10.1016/j.jtcv…

@OncodailyLung @cityofhope Useful framing. A recent RICAD review makes this concrete: high-risk chest-RT survivors may merit screening starting ~5 years after RT, using functional imaging and/or coronary CTA—not just waiting for classic symptoms. Educational, not medical advice. pmc.ncbi.nlm.nih.gov/articles/PMC12…

Cardiac Surveillance After Radiation In Lung Cancer Survivorship - Christine M. Lovly @cityofhope oncodaily.com/voices/christi… #OncoDaily #Oncology #Cancer #Health #Medicine #LungCancer #MedX #MedTwitter