Artificial intelligence-driven virtual tumorboard enhances precision care in myelodysplasticsyndromes

Background: Large language models (LLMs) perform well on standardized medical exam questions, but their reliability for complex hematology decision making is uncertain. We compared four general-purpose LLMs (GPT-4o, GPT-o3, Claude Sonnet 4, and DeepSeek-V3) with a Virtual MDS Panel (VMP), a coordinated multi-agent AI system in which domain-specialized, rule-bound software agents (WHO/ICC guidelines; IPSS-R/IPSS-M; NCCN) collaborate to generate tumor-board-level recommendations. Methods: Each model generated diagnostic, prognostic, and treatment recommendations for 30 myelodysplastic syndrome cases. Nine international MDS experts from five institutions, blinded to model identity, completed 3,000 structured ratings using 5-point Likert scales for diagnosis, prognosis, and therapy and classified errors by severity. Results: General-purpose LLMs achieved modest expert ratings (overall mean scores: 3.7 for GPT-o3, 3.2 for GPT-4o, 3.1 for DeepSeek, and 3.0 for Claude) and contained major factual errors in at least 24% of responses. The VMP increased the proportion of outputs rated 4 or higher to 87% (vs. 34-66% for general-purpose models), improved mean scores to 4.3 overall (4.3 for diagnosis, 4.4 for prognosis, and 4.1 for therapy), and reduced major errors to 8%. Conclusions: In this blinded evaluation of 30 complex MDS cases, general-purpose LLMs produced clinically important errors at rates that raise safety concerns for autonomous hematology decision making. The VMP, a rule-bound, multi-agent architecture, approached expert-level accuracy supporting its potential role as an effective decision-support tool for MDS in the future.