The role of Artificial intelligence in the assessment of the spine and spinal cord

Martín-Noguerol, Teodoro; Miranda, Marta Oñate; Amrhein, Timothy J.; Paulano-Godino, Félix; Xiberta, Pau; Vilanova, Joan C.; Luna, Antonio

doi:10.1016/j.ejrad.2023.110726

Cited by 18 publications

(5 citation statements)

References 70 publications

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“…Generative AI techniques can analyze data from wearables, EHRs, and medical images (eg, x-rays, magnetic resonance imaging, and computed tomography scans) to detect signs, patterns, diseases, anomalies, and risks and generate descriptive findings to improve diagnoses. Systems such as AI-Rad Companion leverage natural language generation models to compose radiology reports automatically, highlighting potential abnormalities and issues for clinician review [20]. This assists radiologists by providing initial draft findings more rapidly.…”

Section: Medical Diagnosticsmentioning

confidence: 99%

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Chen,

Esmaeilzadeh

2024

J Med Internet Res

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

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Section: Medical Diagnosticsmentioning

confidence: 99%

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Chen,

Esmaeilzadeh

2024

J Med Internet Res

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“…Spinal care-a critical aspect of the healthcare system-has been no exception to this trend. Over time, these technologies have been utilized in various capacities in the sphere of spinal care, ranging from disease diagnosis to treatment and even the prediction of adverse events (Table 3) [8,[12][13][14][15]. [16].…”

Section: Current Applications Of Ai and ML In Spinal Carementioning

confidence: 99%

Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

Yagi

Yamanouchi

Fujita

et al. 2023

JCM

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

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“…Murata et al trained a DL model with AP and lateral thoracolumbar radiographs on 300 patients that could localize fractures with the accuracy and sensitivity of 86% and 84.7%, respectively [ 149 ]. They demonstrated their model’s ability to detect vertebral fractures to be equivalent to that of orthopedic surgeons and residents [ 149 , 150 ]. In conjunction with the American Society of Neuroradiology (ASNR) and American Society of Spine Radiology (ASSR), the Radiological Society of North America (RNSA) created a challenge in 2022 to encourage the creation of AI-based algorithms to detect and localize cervical spinal fractures.…”

Section: Spinementioning

confidence: 99%

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

Wagner,

Tilmans,

Peng

et al. 2023

Diagnostics

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

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The role of Artificial intelligence in the assessment of the spine and spinal cord

Cited by 18 publications

References 70 publications

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Generative AI in Medical Practice: In-Depth Exploration of Privacy and Security Challenges

Revolutionizing Spinal Care: Current Applications and Future Directions of Artificial Intelligence and Machine Learning

Artificial Intelligence in Neuroradiology: A Review of Current Topics and Competition Challenges

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