concept

medical diagnosis

Also known as: medical diagnoses

Facts (10)

Sources

A Comprehensive Review of Neuro-symbolic AI for Robustness ... link.springer.com Springer Dec 9, 2025 2 facts

accountFenske et al. employed a neuro-symbolic framework for medical diagnosis by integrating neural networks with a Bayesian network structured over diseases and symptoms, where neural models interpret raw inputs to produce probabilistic symptom likelihoods that are propagated through the Bayesian network to infer posterior probabilities of diagnoses.

referenceObot and Uzoka (2009) published 'A framework for application of neuro-case-rule base hybridization in medical diagnosis' in Applied Soft Computing, which proposes a hybrid framework for medical diagnosis.

Medical Hallucination in Foundation Models and Their ... medrxiv.org medRxiv Mar 3, 2025 1 fact

claimEmpirical findings indicate that prompt-based and post-hoc calibration techniques can improve reliability in medical diagnosis tasks (Savage et al., 2024; Gao et al., 2024).

Neurosymbolic AI: The Future of Artificial Intelligence - LinkedIn linkedin.com Karthik Barma · LinkedIn May 24, 2024 1 fact

claimNeurosymbolic AI systems can provide interpretable explanations for their decisions by incorporating symbolic reasoning, which increases transparency and trust in sensitive applications like medical diagnosis and financial forecasting.

Medical Hallucination in Foundation Models and Their Impact on ... medrxiv.org medRxiv Nov 2, 2025 1 fact

claimCalibration techniques improve reliability in medical diagnosis tasks, although empirical findings indicate that larger models do not consistently demonstrate better calibration.

Understanding LLM Understanding skywritingspress.ca Skywritings Press Jun 14, 2024 1 fact

claimDanilo Bzdok from McGill University presented on the use of Large Language Models as aids in medical diagnosis at the 'Understanding LLM Understanding' summer school.

Demographic, Environmental, and Psychosocial Influences on ... pubmed.ncbi.nlm.nih.gov Cureus Aug 27, 2024 1 fact

claimIn a review of 17 studies, Johnson et al. identified medical diagnoses, violence exposure, female sex, stressors/trauma, disaster exposure, and negative coping mechanisms as factors having a negative relationship with resilience.

Large Language Models Meet Knowledge Graphs for Question ... arxiv.org arXiv Sep 22, 2025 1 fact

referenceMedRAG, developed by Nanyang Technological University and other researchers, is a knowledge-graph-elicited, reasoning-enhanced, RAG-based healthcare copilot that generates medical diagnoses and treatment recommendations based on input patient manifestations.

The Synergy of Symbolic and Connectionist AI in LLM-Empowered ... arxiv.org arXiv Jul 11, 2024 1 fact

claimLarge Language Models exhibit emerging capabilities such as writing computer code, playing chess, diagnosing medical conditions, and translating languages as their size increases.

Unlocking the Potential of Generative AI through Neuro-Symbolic ... arxiv.org arXiv Feb 16, 2025 1 fact

claimIn medical diagnosis scenarios using Nested Neuro-Symbolic AI, a rule-based engine oversees the diagnostic process by applying expert guidelines to patient data, while a neural network interprets unstructured radiological images to deliver key indicators such as tumor likelihood.