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@ai.nejm.orgOct 9, 2026, 1:41 PM

Original Article by B. Lahrmann et al.: Closing the Automation Gap in HPV-Based Cervical Cancer Screening: Independent External Validation of an AI Model for Dual-Stain Triage https://nejm.ai/4xRsEXe

#ArtificialIntelligence #AIinMedicine

A flowchart consisting of three panels showing the iterative development, validation, and locked evaluation of an ensemble-based AI system. Panel A shows population-based outcome studies that provided whole-slide images of p16/Ki-67 DS cytology prepared with SurePath or ThinPrep. Panel B shows image tiles spanning unstable LH ranges being extracted in batches and processed using Cytoreader-Core for automated labeling. Panel C shows relabeled tiles being used to construct a deep ensemble with predefined parameters.
@ai.nejm.orgOct 8, 2026, 11:55 PM

Editorial by @roxanadaneshjou.bsky.social: Where Are the Prepared Minds? The Impact of AI on Scientific Thinking https://nejm.ai/3TKfJbp

#ArtificialIntelligence #AIinMedicine

The image is page 1 of the editorial "Where Are the Prepared Minds? The Impact of AI on Scientific Thinking" published in NEJM AI. A dark blue banner at the bottom encourages readers to access the full editorial at ai.nejm.org.
@dgarmstrong.bsky.socialOct 8, 2026, 2:10 AM

Verification Is the New Clinical Skill — A Guest Post by Lucian Feraru, DPM #AIinMedicine #ClinicalAI diabeticfootonline.c...

@ai.nejm.orgOct 5, 2026, 5:18 PM

Case Study by V.J. Nolan et al.: Language Models for Value-Informed Proxy Decision Support https://nejm.ai/3UfoOc9

#AIinMedicine #EndOfLifeCare

A bar chart showing the average large language model–patient agreement (blue bars) compared with average human proxy–patient agreement (horizontal brown and black dotted lines) across 10 repetition trials (±95% confidence intervals). A bar chart showing the mean adjudicator score (±95% bootstrapped confidence intervals) assessing the accuracy of large language model value profile generation (score out of 10) for each patient in the electronic health record cohort across the three reading levels: fifth grade, eighth or ninth grade, and college graduate level.A bar chart showing the adjudication of large language model treatment recommendations at the three reading levels (fifth grade, eighth/ninth grade, or college graduate level) averaged across adjudicators and the 35 electronic health record cohort patients.
@ai-medicalcongress.bsky.socialOct 5, 2026, 4:16 AM

Addressing Bias and Ensuring Fairness in Clinical AI is essential for building trustworthy, inclusive, and equitable healthcare systems.
📅 June 03–04, 2027
📍 Amsterdam, Netherlands
🌐 ai-medicalcongress.com
#HealthcareAI #DigitalHealth #HealthTech #AIinMedicine

@ai-medicalcongress.bsky.socialOct 2, 2026, 4:23 AM

Join us at #AIIM2027 (Edition-3 Artificial Intelligence in Medicine Conference)
📅 June 03–04, 2027
📍 Amsterdam, Netherlands
🌐 ai-medicalcongress.com

#PrecisionMedicine #PredictiveAnalytics #HealthcareAI #MedicalAI #DigitalHealth #AIinMedicine

@meducationai.bsky.socialSep 30, 2026, 4:06 PM

#AIinMedicine #MedEd #BoardPrep #HemOnc #OncologyFellow #HematologyFellow

@drmarzouka.bsky.socialSep 29, 2026, 10:54 PM

AI sharpens clinical thinking — it surfaces patterns, flags outliers, and frees up cognitive bandwidth. But judgment, context, and the human connection? That's still ours. Use the tool. Don't become one. #MedTwitter #AIinMedicine

@ai.nejm.orgSep 29, 2026, 5:58 PM

Perspective by I. Nenadic et al.: From Technical Performance to Clinical Readiness: A Phase-Based Framework for Evidence Standards in Clinical Artificial Intelligence https://nejm.ai/4yIi4CK

#AIinMedicine

A quote from a Perspective published in NEJM AI reads as follows: “Clinical AI is unlikely to follow a single regulatory or evidentiary template. . . . Even so, a staged evidentiary model analogous in principle to those used for drugs and devices offers a practical way to align innovation with clinical readiness.” The Perspective is titled “From Technical Performance to Clinical Readiness: A Phase-Based Framework for Evidence Standards in Clinical Artificial Intelligence” and the authors are I. Nenadic et al. The background color is dark blue and includes the NEJM AI logo.
@gradientbrief.bsky.socialSep 29, 2026, 2:01 PM

A new survey reviews recent work on applying large language models in medicine, highlighting their flexibility compared to traditional task-specific AI while flagging concerns about reliability, accuracy, transparency, and…

#LLMs #AIinMedicine #MedicalAI #NLP
https://arxiv.org/abs/2609.34780

@videocurator.bsky.socialSep 28, 2026, 7:05 PM

It looks like AI might be making healthcare costs go up instead of down - hospitals are using it to find more things to diagnose patients with, which means higher bills for insurance companies and ultimately us!

Credit: @aaronparnas.bsky.social | Source

#HealthcareCosts #AIinMedicine

@ai.nejm.orgSep 28, 2026, 5:40 PM

Policy Corner by A. Wong et al.: How Repeal of the NTAP Alternative Pathway will Impact U.S. Clinical AI Innovation https://nejm.ai/4r7fjbd

#ArtificialIntelligence #AIinMedicine

A quote from a Policy Corner article published in NEJM AI reads as follows: “In a rapidly evolving clinical AI marketplace, coordinated action between the CMS and FDA is necessary to balance fiscally responsible reimbursement standards with the preservation of market competition and diversity.” The article is titled “How Repeal of the NTAP Alternative Pathway will Impact U.S. Clinical AI Innovation” and the authors are A. Wong et al. The background color is orange and includes the NEJM AI logo.
@parsemusfoundation.bsky.socialSep 28, 2026, 11:33 AM

Research shows that AI-enhanced MRI can reveal brain lesions that conventional imaging misses. This could improve future studies and treatment evaluation for people with multiple sclerosis.

Read more: www.parsemus.org/2026/08/ai-e...

#MultipleSclerosis #MSResearch #BrainImaging #MRI #AIinMedicine

@uoft-tcairem.bsky.socialSep 25, 2026, 6:00 PM

❓Can you turn #health data into solutions?
📣 Join UofT-TCAIREM's 2026 National Health #Datathon and team up to tackle real-world health challenges using #AI and #data!
📅 Oct. 2–3
📍 Hybrid
⏳ Register by Sept. 25
🔗 https://ow.ly/TyP150ZLB0L

#AIinMedicine #DataScience

@robertlufkinmd.bsky.socialSep 25, 2026, 4:06 PM

My take: AI can help reveal where metabolic dysfunction and aging intersect. A research map is not yet a personal longevity test.

#MetabolicHealth #HealthyAging #AIinMedicine #HealthLongevitySecrets (3/4)

@ai.nejm.orgSep 25, 2026, 1:23 PM

Original Article by L. Poursoltan et al.: Physician Edits to AI-Drafted Patient Messages and Their Impact on Clinical Workload https://nejm.ai/4c7M3Lv

#ArtificialIntelligence #AIinMedicine

A diagram showing an AI-assisted clinical messaging workflow within EHR system.A bar chart showing the distribution of clinician modification categories by frequency and extent of editing.A pyramid chart showing the four-tier stratification of clinician workload for the categories of edits observed for AI-drafted replies.
@ai.nejm.orgSep 25, 2026, 12:18 AM

Perspective by Susan Michie, DPhil (@susanmichie.bsky.social), Robert West, PhD (@robertjwest.bsky.social), and Janna Hastings, PhD (@jannahastings.bsky.social): Building an AI-Ready Evidence Base for Behavior Change https://nejm.ai/4wJFmX8

#ArtificialIntelligence #AIinMedicine

The image is page 1 of the Perspective "Building an AI-Ready Evidence Base for Behavior Change" published in NEJM AI. A dark banner at the bottom encourages readers to access the full Perspective at ai.nejm.org.
@uoft-tcairem.bsky.socialSep 24, 2026, 6:00 PM

❓Can you turn #health data into solutions?
📣 Join UofT-TCAIREM's 2026 National Health #Datathon and team up to tackle real-world health challenges using #AI and #data!
📅 Oct. 2–3
📍 Hybrid
⏳ Register by Sept. 25
🔗 https://ow.ly/TyP150ZLB0L

#AIinMedicine #DataScience

@ai.nejm.orgSep 23, 2026, 11:52 PM

Case Study by A.M. Kerr et al.: Parental Perspectives on Using Large Language Model–Powered Chatbots in Rare Disease Care https://nejm.ai/4zChqYG

#ArtificialIntelligence #AIinMedicine

A quote from a Case Study published in NEJM AI reads as follows: “Our findings also underscore the need for more research on parental trust in LLM chatbots deployed by academic or clinical institutions. In our study, some parents had trust in the chatbot because of their trust in the institution, not because the results were clinically verified.” The Case Study is titled “Parental Perspectives on Using Large Language Model–Powered Chatbots in Rare Disease Care” and the authors are A.M. Kerr et al. The background color is orange and includes the NEJM AI logo.
@societygim.bsky.socialSep 23, 2026, 9:54 PM

📚 9 must-read UME studies from 2025, reviewed by Elisa M. Sottile, MD, Katherine A. Gielissen, MD, Amy H. Farkas, MD, Cornelia Charles, MD & Manasa Ayyala, MD in the September issue of #SGIMForum.
buff.ly/Ht0l51H
#MedEd #AIinMedicine

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