The multidisciplinary approach of artificial intelligence is revolutionizing the traditional technologies used in medical image computing, radiology imaging, medical diagnoses, and features based disease identification , . In this paper, an overview of artificial intelligence and its application over medical images, recent challenges, deeper technological background regarding betterment/innovation healthcare, real-life clinical application with parameters of diagnosis, will improve patient health and feature-based disease identification. It provides methodologies in the field of medical imaging by developing a self-driven and intelligent algorithm that manages and extracts meaningful information out of raw data present in the images. It helps in the early diagnosis and treatment of diseases. This paper focuses on various aspects and issues on the growing role of medical image, a fundamental technical advantage of AI over traditional patterns for the betterment of human lives. From this research work, the reader understands the deeper technical aspects of smart algorithms regarding diseases and patient care.
- medical imaging,
- a convolutional neural network,
- artificial intelligence,
- research work,
- intelligent algorithm,
- feature-based disease identification
- Machine learning,
- Medical diagnostic imaging,
- Image segmentation,
- Image registration
Imaging is one of the fastest-growing representations of medical data. The accurate image analysis is now possible with the coming of new algorithms based on technologies such as artificial intelligence, machine learning, and deep learning , . These technologies have been given a lot of emphasis over the past few years because of the efficient results and diagnoses that are produced using the base of the algorithm on these technologies. Medical imaging is one of the most reliable areas of medical innovation. Different techniques of biomedical imaging such as image segmentation, image registration, image recognition, and image labeling could now be implemented with the help of intelligent and self-driven algorithms establishing direct analysis and treatment, . It is believed that AI-enabled products will eventually change the way daily diagnoses and treatments are conducted in hospitals and clinics. Medical imaging is often used in routine preventing screenings for cancers, for example, breast cancer and colon cancer. An intelligent algorithm based on these new technologies would not only do early diagnosis but would also suggest the right treatment of the disease.
We have to accept the fact that in the coming years Artificial intelligence (AI) will be poised to move beyond the proof-of-concept stage and impact many facets of clinical practice. It is not surprising to see a flurry of AI activity in the health care sector whose success has already been demonstrated at a diverse range. We have seen the multidisciplinary approach of AI over medical imaging and its usefulness in the medical industry. The advancement in various processes such as image segmentation, detection, etc., based on different technologies of AI, machine learning, deep learning ensures that the field’s pace of evolution will continue to hasten. Deep learning technologies can potentially improve the consistency and accuracy of the various underdeveloped health care system. Based on our research work, it is clear that AI will become an increasingly important part of clinical studies and will carry with it the accompanying benefits to both patients and physicians.
FULL Paper PDF file:Multidisciplinary Approach of Artificial Intelligence over Medical Imaging: A Review, Challenges, Recent Opportunities for Research
Multidisciplinary Approach of Artificial Intelligence over Medical Imaging: A Review, Challenges, Recent Opportunities for Research
2019 Third International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC), Palladam, India, 2019, pp. 237-242,
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Professor Siavosh Kaviani was born in 1961 in Tehran. He had a professorship. He holds a Ph.D. in Software Engineering from the QL University of Software Development Methodology and an honorary Ph.D. from the University of Chelsea.