Complete Resolution of Persistent Globus Pharyngeus Using Cervical Plexus Block: A Case Report This case describes the successful treatment of persistent globus pharyngeus in a patient refractory to a multitude of traditionally used, mainstay interventions. This report is the first in the Engl...

A paper in The Laryngoscope presents the first successful use of a cervical plexus block for persistent globus pharyngeus, offering a new clinical pathway for chronic throat sensations.
bit.ly/4lOpOOh

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Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis Retinal image analysis has enjoyed groundbreaking advances in the last ten years due to seismic improvements in image analysis techniques from the fie…

New Review 💬 Beyond convolutions and supervised learning with transformers and representation learning for retinal image analysis: www.sciencedirect.com/science/arti...

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What's interesting is that scaling isn't 'plug and play.' Noise in trauma bays and 'note bloat' remain massive hurdles. There's also a risk of automation bias if we trust drafts too much. There are some benchmarks, like SCRIBE, which can ensure safety as AI scribes scale.
#MedSky #MedAI

Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings - npj Digital Medicine npj Digital Medicine - Barriers and opportunities of scaling ambient AI scribes for clinical documentation across diverse healthcare settings

Always nice to run into your friends work!

Ambient AI scribes are moving beyond quiet clinics into noisy EDs and ICUs. While time savings vary (from 5.6 mins to just 18 seconds) the real win is a drop in burnout. It’s about letting clinicians look at patients, not screens.
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The trial used a partially autonomous workflow: AI clears low-risk cases while radiologists focus on the complex ones. The trade-off? A 14.8% higher recall rate. That has to be weigh that against the nearly 2 out of 3 reduction in reading time. #MedSky #MedAI

AI-based triage and decision support in mammography and digital tomosynthesis for breast cancer screening: a paired, noninferiority trial - Nature Medicine The breast cancer screening trial found that using automated artificial intelligence to triage and support decisions in mammography and digital breast tomosynthesis was not noninferior to double…

Radiologists saw a workload reduction of 63.6% in this trial of 31,301 women.
By letting AI triage low-risk mammograms, cancer detection rose 15.2% (from 6.3 to 7.3 per 1,000). It’s a rare win-win for efficiency and accuracy.
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Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority - npj Digital Medicine npj Digital Medicine - Innovating global regulatory frameworks for generative AI in medical devices is an urgent priority

Current rules for medical devices are a poor fit for GenAI’s flexible nature. It’s not just about accuracy; 70% of AI datasets have license issues, and hallucinations make automated monitoring a mess. The authors here call for an adaptive, global frameworks to keep clinical AI safe.
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Workflow order shapes the team. AI as a first opinion is faster and sharper on actionable decisions. Yet, when clinicians go first, they use more humanized language with the system. We aren't "better together" than AI alone yet, but design is key.
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From tool to teammate in a randomized controlled trial of clinician-AI collaborative workflows for diagnosis - npj Digital Medicine npj Digital Medicine - From tool to teammate in a randomized controlled trial of clinician-AI collaborative workflows for diagnosis

Collaborative AI is proving to be more teammate than tool. A new RCT with 70 clinicians found that structured workflows boost diagnostic accuracy to 85%, up from 75% with conventional resources. It effectively "raises the floor" by cutting out low-scoring cases.
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Lithium and the Brain–Bone Axis: A Bridge between Osteoporosis and Alzheimer’s Disease - Current Osteoporosis Reports Purpose of Review We evaluate the converging evidence positioning lithium as a systemic modulator of bone and brain health through shared molecular pathways. This review examines the molecular basis, ...

New Review in @springernature.com 💬 Lithium offers a unique paradigm for understanding and potentially treating age-related decline in multiple organ systems at subclinical dosage and concentration.

bit.ly/4siRZqL

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Auditor models to suppress poor artificial intelligence predictions can improve human-artificial intelligence collaborative performance AbstractObjective. Healthcare decisions are increasingly made with the assistance of machine learning (ML). ML has been known to have unfairness—inconsiste

New Article: Bridge2AI researchers discover suppression of poor-quality ML predictions through an auditor model shows promise in improving collaborative human-AI performance and fairness

🔗 academic.oup.com/jamia/articl...

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A.I. Chatbots Want Your Health Records. Tread Carefully. Following rivals like Amazon and OpenAI, Microsoft is upgrading its artificially intelligent assistant to track your health. There are benefits and risks to consider.

Big tech is moving into your medical records. Microsoft’s Copilot Health will soon merge Apple Watch data with clinical histories to "connect the dots" in seconds. It’s an efficiency win, but the privacy stakes are high: HIPAA doesn't apply here, and AI bias hasn't been solved yet.
#MedSky #MedAI

However, prospective tests showed AI needs constant tuning. A shift in hardware (new scanners) doubled the recall rate, requiring a threshold recalibration.
AI isn't set and forget; it's a tool that needs active, localized maintenance to keep screening fair and safe.
#MedSky #MedAI

What's impressive is the scale. By simulating AI as a second reader, the team saw a 32% drop in radiologist time and a 17% jump in cancer detection. It's especially strong for first-time screens where there's no prior image to compare.
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Diagnostic accuracy, fairness and clinical implementation of AI for breast cancer screening: results of multicenter retrospective and prospective technical feasibility studies - Nature Cancer Kelly et al. assessed an artificial intelligence system for breast cancer screening in retrospective datasets, followed by prospective feasibility evaluation, and report its accuracy, fairness and…

A new massive study of breast cancer screening AI (115,000+ UK cases) is out. Google's mammography AI system didn't just match radiologists, it beat the first reader on sensitivity and caught 25% of interval cancers humans missed.
#MedSky #MedAI

Integrating temporally localized notes into sepsis models boosted predictive performance to an AUC of 0.84, significantly outperforming traditional models using only structured numerical data (AUC 0.77).
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Identifying and timing patient outcomes in clinician notes using large language models Key challenges in leveraging unstructured clinician notes for predictive models include identifying and timing patient outcomes. To address these challenges we applied large language models (LLMs) to identify and temporally localize patient outcomes in clinician notes, and evaluated whether this contextual data enhances predictive modeling for conditions like sepsis.

LLMs can reliably identify and time patient outcomes in unstructured clinician notes. Meta-Llama-3.1-8B hit the best balance of precision and coverage, while the biomedical-focused BioMistral 7B tended toward over-prediction. Contextual reasoning beats exact term matching.
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Generative Artificial Intelligence Methodology Reporting in Otolaryngology: A Scoping Review As the capabilities of large language models (LLMs) have rapidly grown, investigations into their utility within otolaryngology have also proliferated widely. However, LLMs are remarkably dependent o...

New Review 💬 Generative Artificial Intelligence Methodology Reporting in Otolaryngology

🔗 onlinelibrary.wiley.com/doi/10.1002/...

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Bridge2AI March Events: Tuesday, March 17th (12:00 PM PT/3:00 PM ET): Bridge2AI Discussion Forum on Emerging ELSI Issues with Dr. Nicholas Evans: Conjoint Analysis of Perspectives on Ethical Tradeoffs in Data Generation Project Thursday, March 19th (12:00 PM PT/3:00 PM ET): Bridge2AI TRM Novel AI Technology Module Lecture with Dr. William Speier: Multimodal Deep Learning Models for Thyroid Cancer Risk Stratification

Join us for our upcoming seminars in #ArtificialIntelligence 🚀

1️⃣Conjoint Analysis of Perspectives on Ethical Tradeoffs in Data Generation Project

2️⃣Multimodal Deep Learning Models for Thyroid Cancer Risk Stratification

🔗 bridge2ai.org/events/

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As genAI usage increases, health data exposure concerns rise: report | TechTarget Healthcare organizations are increasingly concerned about shadow AI and data protection as generative AI becomes part of clinical and administrative workflows.

Healthcare is shifting from "shadow AI" to secure, managed tools, with personal app usage dropping from 82% to 32%. Yet data risks remain high, regulated data accounts for 89% of policy violations.
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Automated detection of new cerebral infarctions and prognostic implications using deep learning on serial MRI - npj Digital Medicine npj Digital Medicine - Automated detection of new cerebral infarctions and prognostic implications using deep learning on serial MRI

Silent brain infarctions are easy to miss on serial MRI, but they're a huge warning sign. A new model with an 0.89 AUC found that "silent" lesions are linked to a 3.8-fold increased risk of a future stroke and could be a better way to manage risk.
#MedSky #MedAI

Radiology AI makes consistent diagnoses using 3D images from different health centres Vision–language model outperforms second-best models by an average of 20% across hospital sites.

Merlin is a new 3D AI model for abdominal CTs that’s actually holding up across different hospitals. Usually, these models tank when they see data from a new site, but Merlin averaged a 19.7% jump in accuracy over the next best system across three external centers.
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Catalyzing Health AI by Fixing Payment Systems

"In this article, we examine the reimbursement landscape for health #AI, focusing first on tools that fit existing regulatory pathways, outlining payment barriers and proposing policy reforms."

🔗 pubmed.ncbi.nlm.nih.gov/41695240/

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New Publication: Quantum Machine Learning and Data Re-Uploading: Evaluation on Benchmark and Laboratory Medicine Data Sets 🚀

🔗https://pubmed.ncbi.nlm.nih.gov/41728802/

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Benchmarking large language model-based agent systems for clinical decision tasks - npj Digital Medicine npj Digital Medicine - Benchmarking large language model-based agent systems for clinical decision tasks

Medical AI agents show modest accuracy gains but use 10x more tokens and double the response time. Hallucinations still do occur, which means these systems are probably not yet ready for prime time yet.
Keep in mind GPT-4.1, Qwen-3 are older models, but Llama 4 is the current model.
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Large language models for simplifying radiology reports: a systematic review and meta-analysis of patient, public, and clinician evaluations LLM-simplified radiology reports improved patient-perceived understanding and readability and were rated by clinicians as largely accurate and complete, although a small proportion contained clinicall...

LLM simplify radiology reports to aid patient understanding.

A review of 38 studies shows patients perceived LLM-rewritten reports as significantly more understandable than radiologist reports (Likert scores 2.16 to 4.04). Doctors found them accurate despite a small error risk.
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Exploring Real‐Time Tracking of Vocal Fold Polyps in Video‐Stroboscopy Using Deep Learning The study presents a deep learning based pipeline for near real-time detection and tracking of vocal fold polyps in video-stroboscopy. By combining frame-level object detection with a temporal tracki....

#YOLO isn't just a motto, it's a model! 🚀 Explore how You Only Look Once was used to develop a #deeplearning object detection system for identifying vocal fold polyps in stroboscopic video frames: onlinelibrary.wiley.com/doi/10.1002/...

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BRIDGE2AI – Propelling Biomedical Research with Artificial Intelligence

Want to learn more about the Bridge2AI program and our data generation projects addressing grand challenges? Visit: bridge2ai.org 🚀

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Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights Generating a flagship AI-ready and ethically-sourced dataset to support future AI-driven discoveries in diabetes

The AIREADI dataset uses data from participants with and without T2DM, collected via methods like surveys and clinical measurements at 3 sites. The data is hosted in Fairhub and accessible under controlled access.
🔗 aireadi.org/dataset

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