Artificial intelligence technology in MR neuroimaging. А radiologist’s perspective

Artificial Intelligence (AI) has been the subject of particular interest in the field of radiology in recent years. Experts believe that the development and implementation of AI technologies will improve diagnostic accuracy, speed up the acquisition of objective information, reduce its variability, and optimize the workflow of diagnostic departments of medical institutions. Over the years, AI has evolved from simple rule-based systems to sophisticated deep-learning algorithms capable of analysing medical images with high accuracy.Despite some progress, the use of AI in medical imaging is still limited. There are many challenges that need to be overcome before it can be widely adopted in clinical practice. For example, training AI algorithms require large amounts of high quality annotated data, and such data is not yet available for the bulk of pathology and any of the imaging techniques. This article looks at the possibilities of AI and some of the current challenges associated with the application of AI in neuroimaging.

[1]  C. Mazo,et al.  Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide , 2022, Applied Sciences.

[2]  Diana T. Mosa,et al.  Brain Tumor Segmentation Using Deep Capsule Network and Latent-Dynamic Conditional Random Fields , 2022, J. Imaging.

[3]  F. Shi,et al.  Deep learning derived automated ASPECTS on non‐contrast CT scans of acute ischemic stroke patients , 2022, Human brain mapping.

[4]  Steve J. Bickley,et al.  Artificial intelligence in the field of economics , 2022, Scientometrics.

[5]  S. Hajra,et al.  Artificial intelligence in brain MRI analysis of Alzheimer’s disease over the past 12 years: A systematic review , 2022, Ageing Research Reviews.

[6]  J. Rezazadeh,et al.  Deep Learning for Smart Healthcare—A Survey on Brain Tumor Detection from Medical Imaging , 2022, Sensors.

[7]  M. Kroesen,et al.  A healthy debate: Exploring the views of medical doctors on the ethics of artificial intelligence , 2021, Artif. Intell. Medicine.

[8]  Brett A. Becker,et al.  Current Challenges and Future Opportunities for XAI in Machine Learning-Based Clinical Decision Support Systems: A Systematic Review , 2021, Applied Sciences.

[9]  Ravi Manne,et al.  Application of Artificial Intelligence in Healthcare: Chances and Challenges , 2021 .

[10]  Christian Kunder,et al.  3D Registration of pre-surgical prostate MRI and histopathology images via super-resolution volume reconstruction , 2021, Medical Image Anal..

[11]  Beibei Wu,et al.  Genomic Characterization of mcr-1-carrying Salmonella enterica Serovar 4,[5],12:i:- ST 34 Clone Isolated From Pigs in China , 2020, Frontiers in Bioengineering and Biotechnology.

[12]  Derya Yakar,et al.  Patients’ views on the implementation of artificial intelligence in radiology: development and validation of a standardized questionnaire , 2019, European Radiology.

[13]  Alejandro Barredo Arrieta,et al.  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI , 2019, Inf. Fusion.

[14]  María Teresa Martín-Valdivia,et al.  Strengths, Weaknesses, Opportunities, and Threats Analysis of Artificial Intelligence and Machine Learning Applications in Radiology. , 2019, Journal of the American College of Radiology : JACR.

[15]  Seong Ho Park,et al.  Ethical challenges regarding artificial intelligence in medicine from the perspective of scientific editing and peer review , 2019, Science Editing.

[16]  T. Davenport,et al.  The potential for artificial intelligence in healthcare , 2019, Future Healthcare Journal.

[17]  F. Sardanelli,et al.  Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine , 2018, European Radiology Experimental.

[18]  R. Gillies,et al.  Repeatability and Reproducibility of Radiomic Features: A Systematic Review , 2018, International journal of radiation oncology, biology, physics.

[19]  Mathias Paulo Loredo e Silva,et al.  The Use of Smartphones in Different Phases of Medical School and its Relationship to Internet Addiction and Learning Approaches , 2018, Journal of Medical Systems.

[20]  Bram van Ginneken,et al.  A survey on deep learning in medical image analysis , 2017, Medical Image Anal..

[21]  Subhashini Venugopalan,et al.  Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs. , 2016, JAMA.

[22]  Essa Yacoub,et al.  The WU-Minn Human Connectome Project: An overview , 2013, NeuroImage.

[23]  Ronald M. Summers,et al.  Machine learning and radiology , 2012, Medical Image Anal..

[24]  R. E. Novitsky,et al.  Legal regulation of artificial intelligence software in healthcare in the Russian Federation , 2021 .