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Lykke Hinsch Gylvin։ Boehringer Ingelheim and Microsoft Explore AI and Digital Twins to Accelerate Clinical Research - OncoDaily

Boehringer Ingelheim and Microsoft are collaborating to speed up clinical research using AI and data capabilities, including simulation models and patient digital twins, with a focus on data-backed, patient-centric trial design while preserving scientific rigor.

Clinical Trial Intelligence & Pharma Pipeline Strategy

The document outlines a four-stage AI-enabled pharma pipeline intelligence workflow that links data across the drug development lifecycle, extracts key clinical and program data, monitors competitive moves, and emphasizes human validation to ensure clinical relevance.

Atlas Bio will simulate drug trials with artificial intelligence

Translated title. Original: Atlas Bio, yapay zeka ile ilaç deneylerini simüle edecek

Atlas Bio secures a $12.5 million round led by Creandum to build an AI-driven drug-trial simulation platform, aiming to cut costs and improve success in clinical development by modeling trials virtually. The team, led by Simon Romanski with involvement from ex-Google engineer Sebastian Bayer, argues the current drug development model is flawed and cites a Basel biotech partner beginning human trials directly after virtual simulations; results are expected within six weeks.

China slams US ban on DeepSeek AI over security concerns | Live Updates from Fox News Digital

AstraZeneca and Oracle are collaborating to use AI to streamline clinical trials, identify suitable patients, and monitor safety, with an initial focus on hypertension in Abu Dhabi and other disease areas; the deal is non-binding and aims to speed development and grow revenue, despite past trial setbacks and enrollment challenges.

Part I: How FDA predetermined change control plans create a market for AI medical device validation, clinical data procurement, software release controls, and recurring regulatory evidence services

FDA guidance allows pre-authorized AI device updates but requires a full evidence package for each release; governance of evidence across a full release program is key to managing drift and risk, illustrated by Beacon Biosignals' 510(k) clearance and the importance of study design and data dependencies.

AstraZeneca and Oracle will explore using AI to find patients for trials - DiarioBitcoin

Translated title. Original: AstraZeneca y Oracle explorarán usar IA para encontrar pacientes para ensayos - DiarioBitcoin

AstraZeneca and Oracle plan an initial, non-binding program to use AI and health data to speed up recruiting patients for clinical trials, starting in Abu Dhabi with M42 for uncontrolled hypertension. The effort, aiming to shorten recruitment time, will involve cloud infrastructure and AI tools from Oracle and pharmaceutical expertise from AstraZeneca, subject to legal and ethical reviews and without disclosed budgets or specifics; future regional and disease-area projects are contemplated but not defined.

Organ donation appeal from patients who were brought back to life with a liver transplant in Samsun

Translated title. Original: Samsun'da Karaciğer Nakliyle Yeniden Hayata Tutunan Hastalardan Organ Bağışı Çağrısı

Two Samsun patients credit liver transplants with a renewed life and urge organ donation. The report highlights OMÜ’s liver transplant program, the role of cadaveric and living-donor surgeries, and the region’s ongoing efforts to raise awareness and increase transplants amid long waiting lists.

[2610.41368] RECITE: Revising Eligibility Criteria Incorporating Textual Evidence

RECITE proposes an automated framework to revise clinical trial eligibility criteria using textual evidence, backed by a large curated dataset, substantial LLM benchmarking, and an agentic literature-discovery system that links papers to trial-design modifications. The findings suggest current clinical knowledge can be translated into actionable trial-design recommendations.

Part II: Clinical AI Across Medical Specialties: What Trials Reveal About Diagnostic Accuracy, Patient Outcomes, Enterprise Adoption, Specialist Work, and the Commercial Limits of Autonomous Care

Trials of AI across medical specialties show improved diagnostic detection and reduced reading workloads, with notable gains in breast screening and performance in cardiology, gastroenterology, and ophthalmology; however, workload reductions do not equate to staffing cuts.

How AI is Used in Clinical Research: Benefits and Limitations

AI speeds up patient screening in clinical trials by quickly flagging potential participants from health records, reducing screening time without altering who qualifies; a 2025 JAMA trial found AI completed eligibility in 15 days versus 50 days manually, with similar eligibility rates in both groups.