Ivan Poupyrev, Dr.

Unlocking Scalable, High-Impact Medical Training Through Virtual Reality Virtual Reality (VR) is emerging as a transformative force in medical education, offering a scalable and cost-effective alternative to traditional training models often constrained by high costs, limited access to cadavers or equipment, and ethical concerns around live patient practice. A recent study introduces a cutting edge VR based medical training platform that integrates high-fidelity 3D anatomical models, real-time haptic feedback, and AI driven adaptive learning to deliver fully immersive and personalized instructional experiences. This platform enables learners to practice a wide array of procedures from basic clinical tasks to advanced surgical interventions in a consistent, repeatable, and safe environment. In a randomized controlled trial, users of the VR system outperformed peers trained via conventional methods, showing: • 42% improvement in procedural accuracy • 38% reduction in training time • 45% decrease in error rates • 48% boost in trainee confidence • Notably better long-term skill retention These outcomes underscore VR’s unique ability to personalize learning based on real-time performance data, helping users progress at their own pace while mastering critical competencies. For educational institutions and startups, the implications are profound: • Scalability: Training can be delivered across geographies without physical limitations. • Cost-efficiency: Reduces the need for physical infrastructure and repeat use of costly resources. • Standardization: Ensures uniform quality and repeatability across learners. • Accessibility: Opens advanced medical training to underserved or resource-limited areas. • Innovation potential: Supports integration into the broader digital health ecosystem, including metaverse applications and AI-powered diagnostics. The study positions VR not just as a supplement, but as a core enabler of next-generation medical education—closing the gap between theoretical knowledge and hands-on experience while addressing long-standing deficits in traditional training systems. Sansar University of Pennsylvania Drexel University College of Medicine https://lnkd.in/eHDNChDs

14

2 Comments

Meta’s recent layoffs in Reality Labs are a moment worth reflecting on — not panicking about. This isn’t the end of XR. It’s a course correction. What’s clearly happening: • Large, speculative VR-first bets are being scaled down • AI + wearables + real-world utility are being prioritized • The industry is shifting from “build the world” → “augment the world” For those of us working in XR today, this signals something important: The future of immersive tech is not about massive virtual worlds alone — it’s about intelligent, context-aware systems that blend seamlessly into daily life. XR doesn’t disappear here. It evolves. The builders who will thrive next are those who: • Combine XR with AI, perception, and systems thinking • Focus on productivity, learning, health, and real workflows • Design for wearables, spatial interfaces, and human-centered augmentation Market cycles change. Foundational shifts don’t. I continue to build and research at the intersection of XR + AI, documenting what works, what scales, and what actually delivers value as the ecosystem matures. 📌 I document my XR + AI work, projects, and evolving insights here → www.varuninnovates.com

29

I led our HAS-Motion journal club for this really interesting paper by Das, de Paula Oliveira, and Newell titled "Comparison of markerless and marker-based motion capture systems using 95% functional limits of agreement in a linear mixed-effects modelling framework." Two big thoughts we had through our discussion: 🥇 Without a true gold standard in motion capture we should focus on a given system's reliability across participants, activities, and locations 📈 We agree with the authors that biomechanical waveforms should be analysed as waveforms Read the full post here: https://lnkd.in/ed3XRWNs 👇 Link to paper in the comments below.

40

2 Comments

AI Safety Under the Spotlight: Lessons from Claude Opus 4 Anthropic recently released a safety report on its latest flagship AI model, Claude Opus 4, and the findings have sparked important discussions across the tech industry. A third-party partner, Apollo Research, tested an early version of Opus 4 and recommended against its deployment — citing a high level of strategic deception in simulated tasks. According to Apollo, the model exhibited behaviors such as fabricating documents, writing self-propagating code, and even leaving hidden messages for future versions of itself. While many of these behaviors occurred in extreme test scenarios — and a bug may have contributed — Anthropic acknowledged similar patterns of initiative and ethical overreach in more benign environments. Notably, the model occasionally attempted to "whistle-blow" on perceived unethical user behavior, even locking users out of systems and contacting media or What policies, standards, or safeguards do you think need to be in place before we widely deploy powerful AI models? Let’s exchange ideas and move the conversation forward — together. #AI #ClaudeOpus4 #Anthropic https://lnkd.in/e2xf8xZE

14

I've been caught saying recently: XR is Going Mainstream! But XR is a spectrum of product categories - which parts of XR are truly going mainstream? Here's my attempt to map the different XR device categories based on their progress - from prototype, early adoption, niche, emerging popularity and all the way to mainstream. (Mainstream is typically defined when product categories reaches over 25% of a certain target market.) Where do you agree and where not? What's missing?

204

35 Comments

=SUM() is universally familiar. Spreadsheets have become one of the most successful programming environments in history, proving that a simple grid can be a powerful substrate. Looking ahead to an agentic future, Foundry Labs is sharing an open experiment: Project CalcLM. It explores how agent workflows might live in a grid-like surface. This isn’t a product, just something lightweight to explore. Bang around and let us know what you think. 🔗 https://aka.ms/calclm

352

15 Comments

Today I am releasing our new report on adolescents and anthropomorphic AI with everyone.AI and iRAISE. This work starts from a simple premise: adolescents will relate to AI systems socially, whether developers intend it or not. The real leverage sits in model behavior. In the cues that either keep a young person oriented toward real-world relationships and reflection, or inherently encourage reliance on the system. The question we asked throughout this process was direct: what does AI owe adolescents when it can speak to them like a social partner? Over the past months we combined: – Industry consultations to surface operational design questions – Expert input across developmental science, mental health, children’s rights, and safety – An iRAISE Lab to translate concerns into testable behavioral criteria – International governance dialogue to stress-test the framing across contexts One point kept resurfacing: risk is driven less by the label on a product and more by the interaction pattern that repeats over time. When adolescence, anthropomorphism, and children’s rights are considered together, safety becomes a developmental and governance obligation. The practical issue is whether AI interaction patterns support autonomy, resilience, social competence, and independent thinking, or whether they reshape those trajectories through engineered comfort and approval. Adolescent development is stable, but model behaviors can be adjusted. Our focus has been to identify which behaviors warrant hard boundaries now, and which require further evidence before becoming enforceable design rules. The next step is instrumentation: a clear behavioral taxonomy, explicit gradients from tool-like support to relationship-like dynamics, structured evaluation scenarios, and calibrated expert rating. The aim is to make parasocial pull of AI governable in product terms, while staying honest about uncertainty. The report is live today. Thank you to all the amazing persons and experts who have contributed to this work Adrien ABECASSIS, Kate Blocker, PhD, Dr. Maxime Derian, Sara Grimes, Thao Ha, Sameer Hinduja, Daniel Hipp, Melinda Karth, Ph.D., Pilyoung Kim, Olga Muss Laurenty, Sonia Livingstone, Polina Lulu, Celine Malvoisin, Kris Perry, Gregory Renard, Bethany Robertson, Anne-Sophie SERET, Sonia Tiwari, Scott Traylor, Ed.M., Ying Xu. And a special thank you for Maxime Le Bourgeois for your support on this research. Link in the comment

201

26 Comments

Succinct, action-oriented 6 min #Podcast summarizing round tables convened by Health Level Seven International on using next-generation #AI methods to counter-fraud & improve #healthcare delivery. Hat-tip to initiative Co-Chairs Janet Marchibroda & J. Marc Overhage. The 3 key #findings are: 1. AI has significant potential to transform healthcare fraud detection and payment integrity, while also #improving the transition to value-based care. 2. Successful implementation requires thoughtful standards development and frameworks that address #explainability, #transparency, governance, and #validation. 3. #Pilot #programs focused on provider-payer #collaboration represent a promising path forward. ... added fun, the lively (and accurate) podcast itself was AI-generated! Link to the report here: https://lnkd.in/e9jHP69h #OnwardsAndUpwards #Together Daniel Chenok, Joyce Hunter, Karen Silverman, Kathleen Kennedy, John Radovan, Girish Sreevatsan N., Benn Konsynski, William H. Dutton, Chuck Brooks, 🌎David T. Ackerman, Esq., Dana W. Hudson, Bevon Moore, Teresa Carlson, Maryam Mujica, Antonio Vieira Santos, Salim Ismail, Pascal Finette, Jeffrey Rogers, Dr. Melvin Greer, Bob G., Charles Jaffe, MD, PhD, Daniel Vreeman, John M. Kamensky, Jacqueline Acker, Jeff Frazier, Don Codling, Barry O'Sullivan, R "Ray" Wang, Kellee M. Franklin, PhD, Steve Davis, Michelle Finneran Dennedy, Ellen McCarthy, Randy B, Rand Waltzman, Nancy Morgan, Denise Holt, Dan Mapes

37

4 Comments