Artificial Intelligence for Neuroimaging in the Clinic - How compelling is the evidence?

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Background

Artificial intelligence (AI) has rapidly emerged as a potential game-changer in various fields of medicine, including neuroimaging. With advancements in machine learning and image analysis techniques, AI has the potential to improve diagnostic accuracy, prognosis, and treatment planning in neurological disorders. However, there remains a paucity of evidence to support the clinical utility of AI-based neuroimaging tools, and it is essential to obtain more high-quality evidence before supporting their broad use. This Research Topic aims to assess the current state of evidence and investigate the reliability, validity, generalizability, and impact of AI in neuroimaging for clinical applications.
This Research Topic invites contributions from researchers working in the fields of neurology, radiology, computer science, and clinical neuroscience. It focuses on evaluating the evidence surrounding the use of AI in neuroimaging for clinical purposes, including diagnosis, prognostication, treatment response prediction, and personalized medicine. Studies assessing the performance of AI algorithms using diverse neuroimaging modalities, large patient cohorts, comprehensive validation strategies, and comparative analyses against standard clinical practices are encouraged.
Objectives:
1. Assess the accuracy and reliability of AI-based neuroimaging tools: AI algorithms require rigorous validation to establish their accuracy and reliability for clinical use. This Research Topic calls for studies evaluating the performance of AI models in neuroimaging analysis, including image segmentation, lesion detection, disease classification, and outcome prediction. Investigations comparing AI algorithms with conventional methods and assessing their reproducibility and inter-rater agreement are crucial for establishing the reliability of AI-based neuroimaging tools.
2. Evaluate the generalizability and transferability of AI models across different populations: AI algorithms trained on specific datasets may exhibit limitations when applied to diverse patient populations. This Research Topic welcomes studies examining the generalizability and transferability of AI models across different cohorts, institutions, ethnicities, and imaging protocols. Investigations assessing the robustness of AI performance in real-world clinical settings and evaluating external validation using independent datasets provide valuable insights into the applicability of AI-based neuroimaging tools.
3. Investigate the clinical impact and utility of AI in neuroimaging: A key objective of this Research Topic is to evaluate the clinical impact of AI-based neuroimaging tools on patient outcomes, healthcare efficiency, and cost-effectiveness. Studies investigating the influence of AI on diagnostic accuracy, treatment planning, patient management, and disease monitoring are encouraged. Additionally, investigations reporting patient-centered outcomes, such as quality of life, patient satisfaction, and healthcare resource utilization, can provide a comprehensive understanding of the utility of AI in the clinical neuroimaging context.
4. Address challenges and limitations in the implementation of AI in clinical practice: The widespread integration of AI-based neuroimaging tools into clinical practice faces various challenges including regulatory considerations, ethical concerns, interpretability, and transparency issues. This Research Topic calls for studies discussing these challenges and proposing solutions to address them. Investigations elucidating the ethical and legal implications, potential biases, and necessary infrastructure for AI implementation can facilitate the responsible and effective utilization of AI in neuroimaging.
Evaluating the evidence supporting the clinical utility of AI-based neuroimaging tools is essential before widespread implementation. This Research Topic aims to critically review the current evidence, accuracy, reliability, generalizability, and impact of AI in neuroimaging for clinical applications. By assessing the performance of AI algorithms, evaluating generalizability, investigating clinical impact, and addressing implementation challenges, researchers can gain valuable insights into the potential and limitations of AI for neuroimaging in the clinic. Continued research, collaboration, and translation of findings into clinical practice will be instrumental in harnessing the full potential of AI for improving patient care and outcomes in the field of neuroimaging.

Keywords: Neural networks, Machine learning, Convolutional neural networks (CNN), Deep learning, Automatic segmentation, Image analysis, Data augmentation, Algorithm development

Important note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.

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