Knowledge Tree
clinical decision-making
Facts (13)
Sources
Medical Hallucination in Foundation Models and Their ... medrxiv.org Mar 3, 2025 4 facts
claimDe Nicola et al. (2022) highlight the potential of knowledge graphs to enhance diagnostic accuracy by encoding complex medical relationships and facilitating structured reasoning in clinical decision making.
claimCognitive biases in medical practice, such as anchoring bias, confirmation bias, and availability bias, are systematic errors in judgment and reasoning that cause clinicians to deviate from optimal decision-making, often in high-stress or time-constrained environments.
claimLarge Language Models (LLMs) exhibit systematic errors known as medical hallucinations, where the models generate incorrect or misleading medical information that can adversely affect clinical decision-making and patient outcomes.
claimLarge Language Models primarily rely on statistical correlations learned from text rather than the causal reasoning required for effective clinical decision-making.
Bridging the Gap Between LLMs and Evolving Medical Knowledge arxiv.org Jun 29, 2025 3 facts
referencePaul Hager, Friederike Jungmann, Robbie Holland, Kunal Bhagat, Inga Hubrecht, Manuel Knauer, Jakob Vielhauer, Marcus Makowski, Rickmer Braren, Georgios Kaissis, et al. published 'Evaluation and mitigation of the limitations of large language models in clinical decision-making' in 2024.
claimThe Medical Knowledge Graph (MKG) supports intricate clinical decision-making by accurately representing relationships between medications, such as beta-blockers like Labetalol and Propranolol, and treatments for cardiovascular care like Diltiazem and Nitroglycerin.
claimTreatment guidelines serve as essential references for standardized diagnosis and treatment protocols in clinical decision-making.
Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org Nov 2, 2025 2 facts
claimIn the medical domain, factual accuracy, reliability, and interpretability are paramount for safe and effective clinical decision-making.
claimA substantial proportion of survey respondents reported encountering medical hallucinations—defined as factually incorrect yet plausible outputs with medical relevance—in critical tasks such as literature reviews and clinical decision-making.
Enterprise AI Requires the Fusion of LLM and Knowledge Graph linkedin.com Jan 2, 2025 1 fact
claimClinical decision-making in healthcare faces three primary challenges: high data volume (including evidence and patient data), the prevalence of unstructured data (such as clinical notes, imaging reports, and discharge summaries), and non-deterministic, judgment-driven decision-making.
Day-5 | Anu Anuja - LinkedIn linkedin.com Feb 20, 2026 1 fact
claimThe 'trust' roadblock in HealthTech AI arises when systems fail to explain their outputs in a way that aligns with clinical decision-making, causing clinicians to hesitate in relying on the technology.
A Comprehensive Benchmark and Evaluation Framework for Multi ... arxiv.org Jan 6, 2026 1 fact
referenceAgentClinic is a multimodal agent environment that treats clinical decision-making as a sequential task involving external tools and electronic health records, demonstrating that interactive diagnosis is more challenging for Large Language Models than static answering.
Hallucinations in medical devices sciencedirect.com 1 fact
claimHallucinations in AI applications used in medical devices may influence clinical decision-making and potentially jeopardize patient outcomes.