𝟱/𝗻 𝗞𝗲𝘆 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗺𝗲𝗻𝘁𝘀 𝗼𝘃𝗲𝗿 𝗣𝗿𝗲𝘃𝗶𝗼𝘂𝘀 𝗦𝘆𝘀𝘁𝗲𝗺𝘀 • 𝗠𝘂𝗹𝘁𝗶𝗺𝗼𝗱𝗮𝗹 & 𝗖𝗼𝗻𝘁𝗶𝗴𝘂𝗼𝘂𝘀: Unlike previous unimodal models, TOPO-OMNI uses a single contiguous in-silico sheet for visual, auditory, and language/cognitive processing, integrating information across modalities. • 𝗣𝗿𝗲𝘁𝗿𝗮𝗶𝗻𝗲𝗱 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲: Built by fine-tuning a pretrained foundation model, it incorporates state-of-the-art capabilities, overcoming limitations of models trained from scratch. • 𝗖𝗮𝘂𝘀𝗮𝗹 𝗜𝗻𝘁𝗲𝗿𝗽𝗿𝗲𝘁𝗮𝗯𝗶𝗹𝗶𝘁𝘆: Spatially localized functional specialization enables interpretable causal interventions, mimicking TMS or intracranial stimulation studies for in-silico screening. #AIinHealthcare #ModelMonitoring #Innovation [5/9]

5/9 Key Improvements over Previous Systems • 𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗖𝗼𝗻𝘀𝘂𝗹𝘁𝗮𝘁𝗶𝗼𝗻 & 𝗟𝗼𝗻𝗴-𝗖𝗼𝗻𝘁𝗲𝘅𝘁 𝗠𝗲𝗺𝗼𝗿𝘆: Baichuan-M4 achieves 86.9 in long-context clinical memory, a significant 21.1-point improvement over Baichuan-M3, surpassing models like GPT-5.5 (81.7) and DeepSeek-V4-Pro (81.2). • 𝗥𝗲𝗱𝘂𝗰𝗲𝗱 𝗛𝗮𝗹𝗹𝘂𝗰𝗶𝗻𝗮𝘁𝗶𝗼𝗻 𝗥𝗮𝘁𝗲: M4 reduces the hallucination rate to 3.3%, improving safety over both open-source and general-purpose closed-source models. • 𝗛𝗶𝗴𝗵-𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻 𝗘𝘃𝗶𝗱𝗲𝗻𝗰𝗲 𝗦𝘆𝗻𝘁𝗵𝗲𝘀𝗶𝘀: It achieves a Citation Precision score of 90.0 on Baichuan-EBM, indicating superior ability to extract precise, non-redundant evidence when using external tools. 💡 #AIinHealthcare #ModelMonitoring [5/9]

🚨 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗹𝗲𝗿𝘁! 🚨 𝗔𝗿𝗲 𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝗺𝗼𝗱𝗲𝗹𝘀 𝘁𝗿𝘂𝗹𝘆 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, 𝗼𝗿 𝗷𝘂𝘀𝘁 𝗴𝘂𝗲𝘀𝘀𝗶𝗻𝗴 𝗰𝗼𝗿𝗿𝗲𝗰𝘁𝗹𝘆 𝗯𝘆 𝗮𝗰𝗰𝗶𝗱𝗲𝗻𝘁? @FudanUniv presents 𝗠𝗲𝗱𝗥𝗖𝘂𝗯𝗲: 𝗔 𝗺𝘂𝗹𝘁𝗶𝗱𝗶𝗺𝗲𝗻𝘀𝗶𝗼𝗻𝗮𝗹 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗳𝗶𝗻𝗲-𝗴𝗿𝗮𝗶𝗻𝗲𝗱 𝗮𝗻𝗱 𝗶𝗻-𝗱𝗲𝗽𝘁𝗵 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗼𝗳 𝗠𝗟𝗟𝗠𝘀 𝗶𝗻 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗶𝗺𝗮𝗴𝗶𝗻𝗴. By Zhijie Bao, Fangke Chen, Licheng Bao Chenhui Zhang, Wei Chen, Jiajie Peng and Zhongyu Wei Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels! • YouTube Deep Dive: youtu.be/-nsVZjP4t-Q • YouTube Shorts: youtube.com/shorts/cbOH5fdqg… • Spotify: open.spotify.com/show/4edRuS… Here's why it's exciting: 👇🧵 1/9 #MedicalAI #Healthcare #AIinImaging #ModelMonitoring [1/9]

🚨 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗹𝗲𝗿𝘁! 🚨 Can we empower medical AI to dynamically zoom in and inspect images like real clinicians do, instead of just guessing from text? @Tsinghua_Uni presents 𝗠𝗲𝗱𝗩𝗥: 𝗔𝗻 𝗮𝗻𝗻𝗼𝘁𝗮𝘁𝗶𝗼𝗻-𝗳𝗿𝗲𝗲 𝗿𝗲𝗶𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗹𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗳𝗼𝗿 𝗮𝗰𝘁𝗶𝘃𝗲 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝘃𝗶𝘀𝘂𝗮𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴. By Zheng Jiang, Heng Guo, Chengyu Fang, Changchen Xiao, Xinyang Hu, Lifeng Sun and Minfeng Xu Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels! • YouTube Deep Dive: youtu.be/wEG9FGGyxQc • YouTube Shorts: youtube.com/shorts/GXdfkdRK2… • Spotify: open.spotify.com/show/4edRuS… Here's why it's exciting: 👇🧵 1/9 #MedicalAI #Healthcare #AIinImaging #ModelMonitoring ##MedicalAI ##Healthcare ##AIinImaging ##ModelMonitoring [1/9]

🚨 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗹𝗲𝗿𝘁! 🚨 Struggling with poor medical image quality impacting clinical AI? How can we train models to assess image quality like human experts without breaking the bank on annotations? @FudanUniv presents 𝗠𝗲𝗱𝗤-𝗘𝗻𝗴𝗶𝗻𝗲: 𝗔 𝗰𝗹𝗼𝘀𝗲𝗱-𝗹𝗼𝗼𝗽 𝗱𝗮𝘁𝗮 𝗲𝗻𝗴𝗶𝗻𝗲 𝗳𝗼𝗿 𝗲𝘃𝗼𝗹𝘃𝗶𝗻𝗴 𝗠𝗟𝗟𝗠𝘀 𝗶𝗻 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗜𝗺𝗮𝗴𝗲 𝗤𝘂𝗮𝗹𝗶𝘁𝘆 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁. By Jiyao Liu, Junzhi Ning, Wanying Qu, and team from Fudan University Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels! YouTube: youtube.com/@OpenlifesciAI YouTube Deep Dive: youtu.be/Lp9sH27Y8m8 YouTube Shorts: youtube.com/shorts/5nzg_7i-8… Spotify: open.spotify.com/show/4edRuS… Here's why it's exciting: 👇🧵 1/9 #MedicalAI #Healthcare #AIinImaging #ModelMonitoring ##MedicalAI ##Healthcare ##AIinImaging ##ModelMonitoring [1/9]

Real #AIgovernance means risk classification, bias validation, drift monitoring, and regulatory alignment and if your system isn’t auditable and accountable, our experts can help make it enterprise-ready. #ResponsibleAI #EnterpriseAI #AICompliance #ModelMonitoring #spiralmantra

📣 Call for Papers, ICLR 2026 Workshop Catch, Adapt, and Operate (CAO): Monitoring ML Models Under Drift As ML systems move from benchmarks to real-world deployment, distributional drift becomes unavoidable. This workshop brings together researchers and practitioners working on how to monitor, adapt, and reliably operate machine learning models in dynamic environments. 📄 Submission Tracks To encourage a broad range of contributions, CAO offers multiple tracks: 🔹 Main Track Full technical papers with solid methodology and experimental validation. 🔹 Tiny Papers Track Shorter papers for early ideas, focused insights, or promising preliminary results. 🔹 Special Track, Lessons from Failures A dedicated space for negative results, failed experiments, and practical lessons that don’t always fit traditional venues , but are critical for progress in real-world ML. Paper submissions now open ! 📍 Rio de Janeiro, Brazil 🔗 sites.google.com/view/iclr-2…… #ICLR2026 #MachineLearning #MLSystems #ModelMonitoring @iclr_conf #ICLRworkshop #iclr2026

AI products don’t fail because models are weak. They fail because infrastructure, cost control, and evaluation aren’t designed together. Building AI that scales means treating performance, reliability, and economics as one system. #ProductionAI #AIOps #ModelMonitoring #AIAtScale

#MachineLearning #MLOps #AIDrift #ModelMonitoring #DataDrift

How do you keep GenAI apps reliable once they hit production? 🤔 MLflow Ambassador Joana Mesquita just published the latest chapter in her GenAI evaluation series, “𝗘𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗻𝗴 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝘃𝗲 𝗔𝗜 𝘄𝗶𝘁𝗵 𝗠𝗟𝗳𝗹𝗼𝘄: 𝗠𝗼𝗻𝗶𝘁𝗼𝗿𝗶𝗻𝗴 𝗶𝗻 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻.” The post walks through: 🔹 Retrieving and evaluating live production traces with MLflow tracing APIs 🔹 Using versioned scorers to keep evaluation consistent and drift free over time 🔹 Connecting development, deployment, and monitoring in one end-to-end MLflow workflow​ 📖 Read the full post: medium.com/@joana.c.mesquita… #MLflow #GenAI #MLOps #LLMs #MLflowEvaluation #ModelMonitoring #ProductionAI

Measure model drift over time—performance decay reveals data or assumption flaws. #ModelMonitoring @IIBA @IIBAToronto @credly #MDMarketInsights #BusinessAnalysis #CapitalMarkets #FinancialServices #TradeFloor #FinanceIndustry #InvestmentAnalysis #DataAnalytics #RiskManagement #TradingStrategies #MarketResearch #FinancialData #InvestmentManagement #AssetManagement #Fintech #RegulatoryCompliance #PortfolioManagement #Derivatives #MarketAnalysis #FinancialTechnology #TradingTools #QuantitativeAnalysis #InvestmentStrategy #BusinessIntelligence #FinancialInnovation #EconomicAnalysis #TradingSystems #DataScience #HedgeFunds #PrivateEquity #RiskAnalysis

🚀 Exciting release in the Conformal Prediction ecosystem! The open-source library Crepes has just introduced Conformal Martingales — an innovative extension that brings anytime-valid statistical monitoring to machine learning workflows. 🎯 This update takes Crepes beyond traditional conformal prediction intervals into the realm of data and model reliability. Conformal Martingales turn streams of p-values into martingale processes that can automatically detect when data stop behaving as expected — without violating statistical guarantees. 💡 Key Applications 🧠 Detecting concept drift in deployed models ⚙️ Monitoring distribution shifts in live data streams 📈 Performing change-point detection in time-series analysis 🧪 Running sequential A/B tests with valid inference 🩺 Enabling trustworthy, self-monitoring AI systems A great step forward for researchers and practitioners working on reliable, adaptive machine learning. Check out the new notebook on Conformal Martingales to explore the details. crepes.readthedocs.io/en/lat… Notebook -> github.com/henrikbostrom/cre… #MachineLearning #ConformalPrediction #ModelMonitoring #DataDrift #AI #MLops #Statistics #Crepes #OpenSource

SCF AI TEVV Assessment Framework Tooling - David Driggers 🛡️ Adversarial & security testing - IBM Adversarial Robustness Toolbox (ART) — generate adversarial examples and evaluate defenses to exercise security/robustness tests. - Microsoft Counterfit — orchestrate automated, red-team–style attacks against models. - CleverHans — run adversarial evaluations for common evasion/robustness scenarios. - Foolbox — create and benchmark evasion attacks to probe model robustness. - Bandit (Python security linter) — pre-commit CI step to block insecure Python code patterns. - Safety (dependency vulnerability scanner) — scan Python dependencies for known CVEs in CI. 🛡️ Explainability / trust (often used in security evaluations) - LIME — local, human-readable explanations to check decision transparency. - SHAP — prediction-level feature attributions to validate explainability. - What-If Tool — interactive slicing/comparison to inspect model behavior across cohorts. 🛡️ Fairness / bias (risk & compliance) - Fairlearn — measure/mitigate group disparities with built-in fairness metrics. - AIF360 — bias detection and mitigation pipelines for risk and compliance checks. 🛡️ Data validation / robustness checks - Google Facets — visual data inspection and validation for schema/distribution issues. - Great Expectations — executable data quality assertions (schema, ranges, nulls). - Deequ — constraint-based data quality checks and profiling in data pipelines. TensorFlow Data Validation (TFDV) — large-scale schema and drift checks. - pytest-ml — ML-oriented testing utilities for CI. mltest — sanity/behavior tests for ML pipelines. - checklist — behavioral test suites for NLP capabilities and regressions. 🛡️ Monitoring & model ops (security/DR/observability adjacent) - Datadog — application-level monitoring (latency, errors, dependencies) for deployed services. - New Relic — APM for production telemetry and incident visibility. - MLflow — experiment tracking and model registry for versioning/rollback. - Weights & Biases — experiment tracking and model monitoring. - Neptune — metadata tracking and model/experiment monitoring. - Evidently (Evidently AI) — production data/prediction quality and drift monitoring. - WhyLabs — production data logging and drift detection (via whylogs). - Fiddler — model monitoring and explanations for production oversight. #AISecurity #LLMSecurity #AdversarialML #ModelRobustness #ResponsibleAI #AIGovernance #AIEvaluation #TEVV #SCF #ModelMonitoring #DataDrift #MLOps #AIObservability #MLFairness #ExplainableAI #BiasMitigation #AIRisk #AIRedTeam #SecureAI #DataValidation #howtogrc @datadoghq, @newrelic, @weights_biases, @neptune_ai, @EvidentlyAI, @WhyLabs, @fiddler_ai, @databricks, @expectgreatdata, @IBMResearch, @msftsecresponse, @TensorFlow

AI is diagnosing patients & writing clinical notes. But when it goes wrong... who signs off? Responsible AI governance in healthcare isn't optional. It’s survival. Who’s accountable? Everyone. 📖 Read more about this topic on our latest blog post: lumenova.ai/blog/responsible… #ResponsibleAIGovernance #AIinHealthcare #HealthTech #AICompliance #EthicalAI #LumenovaAI #DigitalHealth #AIrisks #ModelMonitoring #AIethics #HealthInnovation

MLOps Overview - linkedin.com/feed/update/urn… Machine learning (ML) is becoming increasingly central to business operations, making the security of ML pipelines essential rather than optional. Machine Learning Operations (MLOps) is a set of repeatable processes to build, deploy, and continuously monitor machine learning models, focusing on three main areas: data, software, and the model itself. Unlike traditional software development, MLOps incorporates operations to machine learning, allowing for development and testing in a reliable, incremental, and repeatable way. This comprehensive overview explores how DevSecOps practices apply to the ML lifecycle through MLOps, along with Large Language Model Operations (LLMOps), and AI Agent Operations (AgentOps). It reveals that traditional security approaches are insufficient for ML systems due to novel threats such as data poisoning, model inversion, adversarial attacks, and member inference attacks. This foundational document also sets the stage for a more in-depth MLSecOps research series, which will provide practical guidance on threat modeling ML solutions, implementing DevSecOps practices in MLOps environments, and creating security reference architectures. Source: cloudsecurityalliance.org/ar… by @cloudsa Authors: Roupe Sahans, Abdul Rahman Sattar, Julianna Tchebotareva (JT), @kgoenka7, Oskar Giles, Klaudia, Rahul Kalva, Akhil Mittal, Josh Buker, Stephen Lumpe, @smithstephen, @igilani, Meghana Parwate, Sudheer Vallandas, Bhavya Jain, @Jigarku02549257, Ramesh Pateel, Dharnisha Narasappa, Usman Mustafa, Adam Ennamli, @DrTanuJain1, Srihari, Vathsala Periyasamy, Akshatha Gangadharaiah, @vikramgvs, Deepak Shivrambhai Antiya. #MLOps #MLSecOps #LLMOps #AgentOps #DevSecOps #AISecurity #AdversarialML #DataPoisoning #ModelInversion #MembershipInference #ModelGovernance #DataGovernance #ModelMonitoring #ModelDrift #DataDrift #ResponsibleAI #AICompliance #AIObservability #SecureAI #AIinProduction

📣 Deal of the Day 📣 Aug 25 New MEAP! HALF OFF TODAY! AI Model Evaluation & selected titles: hubs.la/Q03Fc2yS0 @inancgumus De-risk AI models, validate real-world performance, and align output with product goals! @LeemayNassery Author Leemay Nassery shares her hard-won experiences specializing in experimentation and personalization across companies such as Spotify, Comcast, Dropbox, and Etsy. The book is packed with insights on what it really takes to get a model ready for production. #AIEvaluations #AI #ML #MLOps #LLMEvaluation #OfflineEvaluation #ResponsibleAI #ModelMonitoring #AIInProduction

🚨 ML model not working well after deployment? Say hello to Evidently your open-source partner for monitoring ML models in production! 💻📊 I wrote a beginner-friendly blog breaking down: What Evidently is Why it matters How to use it (even with just Python scripts) And a free course by their founders! If you're getting started with ML observability, this one's for you. link - medium.com/@akashanandani.56… #MachineLearning #MLOps #DataScience #EvidentlyAI #ModelMonitoring @EmeliDral @elenasamuylova

AI TRiSM by @Gartner_inc: Managing AI Trust, Risk, and Security As organizations adopt AI technologies, new responsibilities emerge—especially around trust, risk, and security. According to Gartner, nearly 30% of companies using AI have experienced data security incidents related to its use. So how can AI be managed effectively and responsibly? ➡️ Gartner proposes a framework called AI TRiSM — AI Trust, Risk, and Security Management. It’s not just a collection of tools, but a strategic approach spanning people, processes, and technology. What is AI TRiSM? AI TRiSM is structured around four core layers: 1️⃣ AI Governance – Oversight and validation across the AI lifecycle 2️⃣ Runtime Inspection & Enforcement – Monitoring models, apps, and agents in real time 3️⃣ Information Governance – Protecting, classifying, and managing data used by AI systems 4️⃣ Infrastructure & Traditional Security – Securing compute resources and applying proven security practices Key principles for implementation: ▶️ Cross-team collaboration – Align security, legal, IT, and operational functions ▶️ Visibility and control – Understand what AI systems are in place and how they behave ▶️ Continuous monitoring – Regularly assess AI performance and detect issues early ▶️ AI-assisted oversight – Use AI to help monitor other AI systems, where appropriate Gartner notes that many current issues arise not from attacks but from unclear responsibilities or unanticipated outcomes. Still, risks tied to open models or third-party tools should not be overlooked. Ultimately, organizations remain responsible for managing AI risks, even when relying on external platforms or models. Why it matters: AI TRiSM is already influencing how enterprise AI is evaluated and governed. In the coming years, structured approaches like this may become standard practice for AI deployment. #AITRISM #Gartner #AITrust #AIRisk #AISecurity #AICompliance #AIGovernance #ModelMonitoring #AIOversight #AIFrameworks #ResponsibleAI #EnterpriseAI #AIAdoption #AIManagement #TrustworthyAI #AIAudit #AICybersecurity #SecureAI #AIInfrastructure #TechGovernance

🚨 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗹𝗲𝗿𝐭! 🚨 𝗖𝗮𝗻 𝗔𝗜 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝐰𝗵𝗲𝗿𝗲 𝗱𝗼𝗰𝐭𝗼𝗿𝘀 𝗹𝗼𝗼𝗸 𝐰𝗶𝐭𝗵 𝗔𝗜-𝗴𝗲𝗻𝗲𝗿𝗮𝐭𝗲𝗱 𝗲𝐱𝗽𝗹𝗮𝗻𝗮𝐭𝗶𝗼𝗻𝘀 𝐭𝗼 𝗶𝗺𝗽𝗿𝗼𝐯𝗲 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗶𝗺𝗮𝗴𝗲 𝘀𝗲𝗴𝗺𝗲𝗻𝐭𝗮𝐭𝗶𝗼𝗻 𝐰𝗶𝐭𝗵𝗼𝘂𝐭 𝗲𝐱𝐭𝗲𝗻𝘀𝗶𝐯𝗲 𝗮𝗻𝗻𝗼𝐭𝗮𝐭𝗶𝗼𝗻𝘀? @UniofOxford presents 𝗙𝗿𝗼𝗺 𝗚𝗮𝘇𝗲 𝐭𝗼 𝗜𝗻𝘀𝗶𝗴𝗵𝐭: 𝗮 𝗿𝗲𝐯𝗼𝗹𝘂𝐭𝗶𝗼𝗻𝗮𝗿𝘆 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗯𝗿𝗶𝗱𝗴𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻 𝐯𝗶𝘀𝘂𝗮𝗹 𝗮𝐭𝐭𝗲𝗻𝐭𝗶𝗼𝗻 𝐰𝗶𝐭𝗵 𝐯𝗶𝘀𝗶𝗼𝗻 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗺𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝐰𝗲𝗮𝗸𝗹𝘆-𝘀𝘂𝗽𝗲𝗿𝐯𝗶𝘀𝗲𝗱 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗶𝗺𝗮𝗴𝗲 𝘀𝗲𝗴𝗺𝗲𝗻𝐭𝗮𝐭𝗶𝗼𝗻. By Jingkun Chen, Haoran Duan, Xiao Zhang, Boyan Gao, Tao Tan, Vicente Grau, and Jungong Han Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels! YouTube: youtu.be/-7PUg4Cb3cY Spotify: creators.spotify.com/pod/sho… Here's why it's exciting: 👇🧵 1/10 #MedicalAI #Healthcare #AIinImaging #ModelMonitoring #HealthTech

🚨 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗔𝗜 𝗥𝗲𝘀𝗲𝗮𝗿𝗰𝗵 𝗔𝗹𝗲𝗿𝐭! 🚨 𝗖𝗮𝗻 𝗔𝗜 𝗰𝗼𝗺𝗯𝗶𝗻𝗲 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗶𝗺𝗮𝗴𝗲𝘀 𝐰𝗶𝐭𝗵 𝗲𝐱𝗽𝗲𝗿𝐭 𝗱𝗲𝘀𝗰𝗿𝗶𝗽𝐭𝗶𝗼𝗻𝘀 𝐭𝗼 𝗮𝗰𝗰𝘂𝗿𝗮𝐭𝗲𝗹𝘆 𝗶𝗱𝗲𝗻𝐭𝗶𝗳𝘆 𝐭𝘂𝗺𝗼𝗿𝘀 𝗳𝗼𝗿 𝗿𝗮𝗱𝗶𝗮𝐭𝗶𝗼𝗻 𝐭𝗵𝗲𝗿𝗮𝗽𝘆? @JohnsHopkins presents 𝗢𝗻𝗰𝗼𝗹𝗼𝗴𝘆 𝗖𝗼𝗻𝐭𝗼𝘂𝗿𝗶𝗻𝗴 𝗖𝗼𝗽𝗶𝗹𝗼𝐭 (𝗢𝗖𝗖): 𝗮 𝗟𝗮𝗻𝗴𝘂𝗮𝗴𝗲 𝗩𝗶𝘀𝗶𝗼𝗻 𝗠𝗼𝗱𝗲𝗹 𝗮𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗳𝗼𝗿 𝗮𝘂𝐭𝗼𝗺𝗮𝐭𝗲𝗱 𝐭𝘂𝗺𝗼𝗿 𝗰𝗼𝗻𝐭𝗼𝘂𝗿𝗶𝗻𝗴 𝗶𝗻 𝗿𝗮𝗱𝗶𝗮𝐭𝗶𝗼𝗻 𝗼𝗻𝗰𝗼𝗹𝗼𝗴𝘆. By Yi Luo, Hamed Hooshangnejad, Xue Feng, Gaofeng Huang, @CJerry391, @RuiZhang1229, Quan Chen, Wil Ngwa, and Kai Ding from @JohnsHopkins, Carina Medical LLC, @UMNews, and @MayoClinic. Now you can watch and listen to the latest Medical AI papers daily on our YouTube and Spotify channels! YouTube: youtu.be/GMb-2DxvB0M Spotify: creators.spotify.com/pod/sho… Here's why it's exciting: 👇🧵 1/10 #MedicalAI #Healthcare #AIinOncology #ModelMonitoring #HealthTech