New AI Systems Improve Clinical Decision Accuracy
Research-stage agents better automate clinical trial retrieval and treatment recommendations for oncology diagnostics.
Updated on Oct. 20, 2026 in Artificial Intelligence
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Researchers presented new AI systems for clinical decision-making at the EANM 2026 congress in Vienna. These research-stage tools, including a multi-agent system named PRISM, aim to automate evidence retrieval and PET tracer selection.
Why it matters
Current clinical guidelines often lag behind rapid developments in radiopharmaceutical trials, creating a need for AI tools that can integrate real-time trial data to improve treatment recommendations.
The PRISM agent system averaged 4.33 traceable clinical trials per recommendation, a significant increase over the 1.86 average observed in standard LLMs. Separately, a deep learning model for PD-L1 expression reached an internal AUC of 0.90, validated externally at 0.84.
The players
PRISM
A research-stage seven-agent AI system developed in Switzerland for clinical decision support and radiopharmaceutical treatment evidence retrieval.
EANM
The European Association of Nuclear Medicine, which organizes the annual congress for advancements in radiopharmaceuticals and diagnostics.
The details
PRISM functions as a seven-agent system that autonomously retrieves clinical evidence, verifies trial eligibility, and generates recommendations with cited sources. For lung cancer diagnostics, a deep learning model employs a combination of semi-supervised and unsupervised learning techniques to analyze unlabeled PET/CT scan data. The tracer selection tools achieved 95% accuracy in identifying the correct tracer among FDG, PSMA, and DOTATATE options.
Timeline
June 2026: PD-L1 expression prediction study published in Scientific Reports.
October 19, 2026: AI research systems presented at the EANM 2026 congress.
The Tech Race
The development of these AI agents marks a shift toward locally deployed clinical models capable of processing patient data within hospital systems. These tools are currently competing to replace static diagnostic workflows with dynamic, evidence-backed decision support.
These models remain in the research stage and are not yet available for clinical use. Once validated, they may eventually assist oncologists by automating the analysis of PET/CT scans and cross-referencing patient data with the latest clinical trial evidence.
The takeaway
These AI systems demonstrate a measurable improvement in clinical recommendation accuracy and trial traceability. The next milestone will be establishing whether these internal AUC gains translate into improved patient outcomes in prospective, real-world hospital environments.
Further reading
For broader trends in medical diagnostics, visit the Artificial Intelligence section.
Source note: This article includes information reported by AuntMinnieEurope.
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