AI-Driven Biomedical and Health Informatics: Harnessing Artificial Intelligence for Improved Healthcare Solutions DOI
Usman Ahmad Usmani,

Mohammed Umar Usmani

Published: Nov. 24, 2023

This paper explores the convergence of Artificial Intelligence (AI) with Biomedical and Health Informatics, focusing on transformative potential AI-driven solutions in healthcare domain. With growing availability data advancements AI technologies, there is an increasing emphasis leveraging techniques to enhance medical diag- nosis, treatment, patient care. highlights recent research applications that demonstrate impact areas such as image analysis, disease prediction, drug discovery, personalized medicine. Additionally, it addresses challenges ethical considerations associated integrating into systems, emphasizing need for robust interpretable models, privacy, trustworthiness. By delving opportunities presented by this aims inspire further collaboration promising critical intersection disciplines.

Language: Английский

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges DOI Creative Commons
Yan Chen, Pouyan Esmaeilzadeh

Journal of Medical Internet Research, Journal Year: 2024, Volume and Issue: 26, P. e53008 - e53008

Published: March 8, 2024

As advances in artificial intelligence (AI) continue to transform and revolutionize the field of medicine, understanding potential uses generative AI health care becomes increasingly important. Generative AI, including models such as adversarial networks large language models, shows promise transforming medical diagnostics, research, treatment planning, patient care. However, these data-intensive systems pose new threats protected information. This Viewpoint paper aims explore various categories care, drug discovery, virtual assistants, clinical decision support, while identifying security privacy within each phase life cycle (ie, data collection, model development, implementation phases). The objectives this study were analyze current state identify opportunities challenges posed by integrating technologies into existing infrastructure, propose strategies for mitigating risks. highlights importance addressing associated with ensure safe effective use systems. findings can inform development future help organizations better understand benefits risks By examining cases across diverse domains contributes theoretical discussions surrounding ethics, vulnerabilities, regulations. In addition, provides practical insights stakeholders looking adopt solutions their organizations.

Language: Английский

Citations

106
Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning DOI Open Access
Mitsuru Yagi,

Kento Yamanouchi,

Naruhito Fujita

et al.

Journal of Clinical Medicine, Journal Year: 2023, Volume and Issue: 12(13), P. 4188 - 4188

Published: June 21, 2023

Artificial intelligence (AI) and machine learning (ML) are rapidly becoming integral components of modern healthcare, offering new avenues for diagnosis, treatment, outcome prediction. This review explores their current applications potential future in the field spinal care. From enhancing imaging techniques to predicting patient outcomes, AI ML revolutionizing way we approach diseases. have significantly improved by augmenting detection classification capabilities, thereby boosting diagnostic accuracy. Predictive models also been developed guide treatment plans foresee driving a shift towards more personalized Looking future, envision further ingraining themselves care with development algorithms capable deciphering complex pathologies aid decision making. Despite promise these technologies hold, integration into clinical practice is not without challenges. Data quality, hurdles, data security, ethical considerations some key areas that need be addressed successful responsible implementation. In conclusion, represent potent tools transforming Thoughtful balanced technologies, guided considerations, can lead significant advancements, ushering an era personalized, effective, efficient healthcare.

Language: Английский

Citations

36
Osteoporosis management-current and future perspectives – A systemic review DOI
Rajamohanan Jalaja Anish,

Aswathy Nair

Journal of Orthopaedics, Journal Year: 2024, Volume and Issue: 53, P. 101 - 113

Published: March 2, 2024

Language: Английский

Citations

15
Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges DOI Creative Commons

Daniel T. Wagner,

Luke Tilmans,

Kevin A. Peng

et al.

Diagnostics, Journal Year: 2023, Volume and Issue: 13(16), P. 2670 - 2670

Published: Aug. 14, 2023

There is an expanding body of literature that describes the application deep learning and other machine artificial intelligence methods with potential relevance to neuroradiology practice. In this article, we performed a review identify recent developments on topics in neuroradiology, particular emphasis large datasets large-scale algorithm assessments, such as those used imaging AI competition challenges. Numerous applications relevant ischemic stroke, intracranial hemorrhage, brain tumors, demyelinating disease, neurodegenerative/neurocognitive disorders were discussed. The these spinal fractures, scoliosis grading, head neck oncology, vascular also reviewed. examined perform variety tasks, including localization, segmentation, longitudinal monitoring, diagnostic classification, prognostication. While research topic ongoing, several have been cleared for clinical use augment accuracy or efficiency neuroradiologists.

Language: Английский

Citations

11
Automated diagnosis and grading of lumbar intervertebral disc degeneration based on a modified YOLO framework DOI Creative Commons
Aobo Wang, Tianyi Wang, Xingyu Liu

et al.

Frontiers in Bioengineering and Biotechnology, Journal Year: 2025, Volume and Issue: 13

Published: Jan. 22, 2025

Background The high prevalence of low back pain has led to an increasing demand for the analysis lumbar magnetic resonance (MR) images. This study aimed develop and evaluate a deep-learning-assisted automated system diagnosing grading intervertebral disc degeneration based on T2-weighted sagittal axial MR Methods included total 472 patients who underwent scans between January 2021 November 2023, with 420 in internal dataset 52 external dataset. images were evaluated labeled by experts according current guidelines, results considered ground truth. annotations Pfirrmann degeneration, herniation, high-intensity zones (HIZ). diagnostic model was YOLOv5 network, modified adding attention module Cross Stage Partial part residual Spatial Pyramid Pooling-Fast part. model’s performance calculating precision, recall, F1 score, area under receiver operating characteristic curve. Results In test set, achieved precisions 0.78–0.91, 0.90–0.92, 0.82 recalls 0.86–0.91, 0.90–0.93, 0.81–0.88 grading, herniation diagnosis, HIZ detection, respectively. precision values detection 0.73–0.87, 0.86–0.92, 0.74–0.84 0.79–0.87, 0.88–0.91, 0.77–0.78, Conclusion proposed demonstrated relatively classification exhibited considerable consistency expert evaluation.

Language: Английский

Citations

0
Artificial Intelligence in Spine Imaging DOI
Kushal Patel,

P R Cooper,

Puneet Belani

et al.

Magnetic Resonance Imaging Clinics of North America, Journal Year: 2025, Volume and Issue: 33(2), P. 389 - 398

Published: Feb. 14, 2025

Language: Английский

Citations

0
Spinal Metastasis—Imaging Using XAI and RAI Techniques DOI Open Access
Arti A. Bagada, Priya Patel

Published: March 3, 2025

Citations

0
Oncologic Applications of Artificial Intelligence and Deep Learning Methods in CT Spine Imaging—A Systematic Review DOI Open Access

Wilson Ong,

Aric Lee,

Wei Chuan Tan

et al.

Cancers, Journal Year: 2024, Volume and Issue: 16(17), P. 2988 - 2988

Published: Aug. 28, 2024

In spinal oncology, integrating deep learning with computed tomography (CT) imaging has shown promise in enhancing diagnostic accuracy, treatment planning, and patient outcomes. This systematic review synthesizes evidence on artificial intelligence (AI) applications CT for tumors. A PRISMA-guided search identified 33 studies: 12 (36.4%) focused detecting malignancies, 11 (33.3%) classification, 6 (18.2%) prognostication, 3 (9.1%) 1 (3.0%) both detection classification. Of the classification studies, 7 (21.2%) used machine to distinguish between benign malignant lesions, evaluated tumor stage or grade, 2 (6.1%) employed radiomics biomarker Prognostic studies included three that predicted complications such as pathological fractures AI's potential improving workflow efficiency, aiding decision-making, reducing is discussed, along its limitations generalizability, interpretability, clinical integration. Future directions AI oncology are also explored. conclusion, while technologies promising, further research necessary validate their effectiveness optimize integration into routine practice.

Language: Английский

Citations

3
Are AI and VR tools changing spine education and training? DOI Open Access
Melissa Baker,

Ronald Lontchi,

Zorica Buser

et al.

Artificial Intelligence Surgery, Journal Year: 2025, Volume and Issue: 5(1), P. 73 - 81

Published: Feb. 14, 2025

From diagnostics and treatments to surgical techniques postoperative outcomes, the field of spine surgery is advancing at a historically unprecedented rate. Given widespread integration artificial intelligence (AI) in various industries, its implementation medical not question if, but when it will happen. AI’s ability sort, analyze, summarize vast quantities data demonstrates great potential assisting professionals all levels training. Virtual reality (VR) enables users explore interact three-dimensional, computer-generated environment, application can include bringing awareness exposure students, training repetition residents fellows, planning for attendings. Augmented (AR) has significant through versatile applications, offering benefits education While there are costs associated with AI VR curriculums professionals, long-term savings stakeholders outweigh initial investment. This paper intends offer focused summary impact tools

Language: Английский

Citations

0
Artificial intelligence in spinal imaging - a narrative review DOI Open Access
Muhammad Ibrahim,

Eric Milliron,

Elizabeth A. Yu

et al.

Artificial Intelligence Surgery, Journal Year: 2025, Volume and Issue: 5(1), P. 139 - 49

Published: March 8, 2025

Clinical integration of artificial intelligence (AI) in spinal surgery is still its early stages, with imaging being the most prominent. We present a review recent literature on topic. The reporting traditional has been slow due to overburdened staff and unreliable some patients. AI applications have shown promising results improving speed quality while reducing costs radiation exposure. Specific examples clinical implementation include osteoporosis screening, diagnosing degenerative spine diseases differentiating tuberculous pyogenic spondylitis, helping preoperative measurements surgical planning. Other tools demonstrated ability help clinicians real time reduce rates missed fractures rule out cord impingement emergency settings. Novel variants magnetic resonance (MRI) synthetic computed tomography (sCT) scans, without ionizing radiation, successful resource burden scan time, maintaining utility. At current stage, potential improve significantly expected tremendously enhance efficiency accuracy radiologists care providers. However, validation studies are required before widespread direct patient care.

Language: Английский

Citations

0