Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development of CRCWeb (Preprint) DOI Creative Commons
Sizuo Liu, Yu‐Fen Lin, Runze Yan

et al.

JMIR Cancer, Journal Year: 2024, Volume and Issue: unknown

Published: Nov. 9, 2024

Digital health interventions offer promise for scalable and accessible healthcare, but access is still limited by some participatory challenges, especially disadvantaged families facing literacy, language barriers, low income, or living in marginalized areas. These issues are particularly pronounced colorectal cancer (CRC) patients, who often experience distressing symptoms struggle with educational materials due to complex jargon, fatigue, reading level mismatches. To address these issues, we developed assessed the feasibility of a digital platform, CRCWeb, improve accessibility resources on symptom management CRC patients their caregivers literacy income. CRCWeb was through stakeholder-centered design approach. Two-phase semi-structured interviews caregivers, oncology experts informed iterative process. From interviews, following five key principles: user-friendly navigation, multimedia integration, concise clear content, enhanced individuals vision disabilities, scalability future content expansion. Initial feedback from stakeholder engagements confirmed high user satisfaction, participants rating an average 3.98 out 5 post-intervention survey. Additionally, using GenAI tools, including large models (LLMs) like ChatGPT generation tools such as Pictory, healthcare guidelines were transformed into concise, easily comprehensible made CRCWeb. User engagement notably higher among logged platform 2.52 times more frequently than non-disadvantaged participants. The structured development approach demonstrates that GenAI-powered can effectively barriers faced This highlights how innovations enhance healthcare. RR2-10.2196/48499.

Language: Английский

The Fine-Tuned Large Language Model for Extracting the Progressive Bone Metastasis from Unstructured Radiology Reports DOI Creative Commons
Noriko Kanemaru, Koichiro Yasaka, Nana Fujita

et al.

Deleted Journal, Journal Year: 2024, Volume and Issue: unknown

Published: Aug. 26, 2024

Abstract Early detection of patients with impending bone metastasis is crucial for prognosis improvement. This study aimed to investigate the feasibility a fine-tuned, locally run large language model (LLM) in extracting unstructured Japanese radiology report and compare its performance manual annotation. retrospective included “metastasis” radiological reports (April 2018–January 2019, August–May 2022, April–December 2023 training, validation, test datasets 9559, 1498, 7399 patients, respectively). Radiologists reviewed clinical indication diagnosis sections (used as input data) classified them into groups 0 (no metastasis), 1 (progressive 2 (stable or decreased metastasis). The data group was under-sampled training due imbalance. best-performing from validation set subsequently tested using testing dataset. Two additional radiologists (readers 2) were involved classifying within dataset purposes. fine-tuned LLM, reader 1, demonstrated an accuracy 0.979, 0.996, 0.993, sensitivity 0/1/2 0.988/0.947/0.943, 1.000/1.000/0.966, 1.000/0.982/0.954, time required classification (s) 105, 2312, 3094 ( n = 711), respectively. Fine-tuned LLM extracted metastasis, demonstrating satisfactory that comparable slightly lower than annotation by noticeably shorter time.

Language: Английский

Citations

4
Efficacy of Fine-Tuned Large Language Model in CT Protocol Assignment as Clinical Decision-Supporting System DOI Creative Commons
Noriko Kanemaru, Koichiro Yasaka, Naomasa Okimoto

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 5, 2025

Abstract Accurate CT protocol assignment is crucial for optimizing medical imaging procedures. The integration of large language models (LLMs) may be helpful, but its efficacy as a clinical decision support system protocoling tasks remains unknown. This study aimed to develop and evaluate fine-tuned LLM specifically designed protocoling, well assess performance, both standalone in concurrent use, terms effectiveness efficiency within radiological workflows. retrospective included radiology tests contrast-enhanced chest abdominal examinations (2829/498/941 training/validation/testing). Inputs involve the indication section, age, anatomic coverage. was 15 epochs, selecting best model by macro sensitivity validation. Performance then evaluated on 800 randomly selected cases from test dataset. Two residents two radiologists assigned protocols with without referencing output system. exhibited high accuracy metrics, top-1 top-2 accuracies 0.923 0.963, respectively, 0.907. It processed each case an average 0.39 s. LLM, tool, improved (0.913 vs. 0.936) (0.920 0.926 respectively), improvement being statistically significant ( p = 0.02). Additionally, it reduced reading times 14% 12% radiologists. These results indicate potential LLMs improve diagnostic practice.

Language: Английский

Citations

0
Enhancing healthcare resource allocation through large language models DOI
Fang Wan, Kezhi Wang, Tao Wang

et al.

Swarm and Evolutionary Computation, Journal Year: 2025, Volume and Issue: 94, P. 101859 - 101859

Published: Feb. 5, 2025

Language: Английский

Citations

0
Brain Tumor Classification Using a Hybrid Ensemble of Xception and Parallel Deep CNN Models DOI Creative Commons

sang-jin yoon

Informatics in Medicine Unlocked, Journal Year: 2025, Volume and Issue: unknown, P. 101629 - 101629

Published: Feb. 1, 2025

Language: Английский

Citations

0
Assessing large language models for Lugano classification of malignant lymphoma in Japanese FDG-PET reports DOI Creative Commons
Rintaro Ito, Keita Kato,

Kosuke Nanataki

et al.

Deleted Journal, Journal Year: 2025, Volume and Issue: 9(1)

Published: March 9, 2025

This study evaluates the performance of four large language models (LLMs) in classifying malignant lymphoma stages using Lugano classification from free-text FDG-PET reports Japanese Specifically, we assess GPT-4o, Claude 3.5 Sonnet, Llama 3 70B, and Gemma 2 27B their ability interpret unstructured radiology texts. In a retrospective single-center study, 80 patients who underwent staging FDG-PET/CT for were included. The "Findings" sections analyzed without pre-processing. Each LLM assigned based on these reports. Performance was compared to reference standard determined by expert radiologists. Statistical analyses involved overall accuracy, weighted kappa agreement. GPT-4o achieved highest accuracy at 75% (60/80 cases) with substantial agreement (weighted κ = 0.801). Sonnet had 61.3% (49/80, 0.763). 70B showed accuracies 58.8% 57.5%, respectively, all indicating outperformed other LLMs assigning demonstrated potential advanced clinical While immediate utility automatically predicting stage an existing report may be limited, results highlight value understanding standardizing data.

Language: Английский

Citations

0
Exploring finetuned audio-LLM on heart murmur features DOI

Adrian Nicolas Florea,

Xilin Jiang,

Nima Mesgarani

et al.

Smart Health, Journal Year: 2025, Volume and Issue: unknown, P. 100557 - 100557

Published: March 1, 2025

Language: Английский

Citations

0
Leveraging Large Language Models for accurate Classification of Liver Lesions from MRI Reports DOI Creative Commons

Daniel Spitzl,

Markus Mergen,

Ulrike Bauer

et al.

Computational and Structural Biotechnology Journal, Journal Year: 2025, Volume and Issue: unknown

Published: May 1, 2025

Language: Английский

Citations

0
A New Step Forward in the Extraction of Appropriate Radiology Reports DOI
Koichiro Yasaka, Osamu Abe

Radiology, Journal Year: 2025, Volume and Issue: 315(1)

Published: April 1, 2025

Language: Английский

Citations

0
Leveraging Artificial Intelligence for Digital Symptom Management in Oncology: The Development and Evaluation of CRCWeb (Preprint) DOI
Sizuo Liu, Yu‐Fen Lin, Runze Yan

et al.

Published: Nov. 9, 2024

BACKGROUND Digital health interventions offer promise for scalable and accessible healthcare, but access is still limited by some participatory challenges, especially disadvantaged families facing literacy, language barriers, low income, or living in marginalized areas. These issues are particularly pronounced colorectal cancer (CRC) patients, who often experience distressing symptoms struggle with educational materials due to complex jargon, fatigue, reading level mismatches. OBJECTIVE This study aimed address disparities improving the accessibility of resources on symptom management CRC patients their caregivers. METHODS To these disparities, we proposed a generative AI (GenAI)-powered system customize into multimedia content, delivered mobile platform, CRCWeb. We conducted an 8-week single-arm prospective 40 participants (20 20 caregivers) both non-disadvantaged backgrounds. RESULTS Analysis user login activity showed that frequency group 2.52 times higher than group. Post-intervention ratings from groups demonstrated significant equivalence (P ≤ .001) overall satisfaction The pre- post-intervention data reductions scores (a difference 0.108, P = .123) 0.211, .002). CONCLUSIONS Our findings highlight potential GenAI-powered digital solutions gaps healthcare access, offering support promotes care equity populations. CLINICALTRIAL ClinicalTrials.gov NCT05663203. INTERNATIONAL REGISTERED REPORT RR2-10.2196/48499

Language: Английский

Citations

0
Navigating the Artificial Intelligence Revolution in NeuroOncology: A Multidisciplinary Viewpoint DOI
Sanjay Saxena, Soumyaranjan Panda, Ekta Tiwari

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0