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Starting Ph.D. research in Medical Application- Tutors India

Medicine is has evolved as a data-centered discipline and, artificial intelligence (AI), in particular, machine learning (ML) has become an attractive field for analyzing the medical data. The current process of industrialization of AI has been reflected by this characterization. Therefore, the issues related to the use of Artificial Intelligence and Machine Learning should not be ignored anymore and definitely not in the medical domain.<br>The main applications of AI are:<br>1. Artificial Intelligence helps to recognize image patterns that are complex in nature. It also provides the opportunity to interpret the images and transform them from a qualitative task to the quantifiable one and reproduce it effortlessly.<br>2. Additionally, Artificial Intelligence can compute the data from the images which is a difficult task for humans and thus harmonizing decision making clinically. <br>3. Artificial Intelligence can also combine multiple data streams and transform them into powerful integrated diagnostic systems spanning genomics, social networks, radiographic images, pathology, and electronic health records.<br>4. Artificial Intelligence performs 3 main clinical tasks in cancer imaging: detecting, characterizing, and monitoring the tumors.<br>To learn more visit: http://www.tutorsindia.com/blog/<br>Contact: <br>Website: www.tutorsindia.com<br>Email: info@tutorsindia.com<br>United Kingdom: 44-1143520021<br>India: 91-4448137070<br>Whatsapp Number: 91-8754446690<br>

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Starting Ph.D. research in Medical Application- Tutors India

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  1. STARTINGRESEARCH IN MEDICAL APPLICATION An Academic presentationby Dr. Nancy Agens, Head, Technical Operations,TutorsIndia Group www.tutorsindia.com Email:info@tutorsindia.com

  2. Today'sDiscussion In Brief Introduction AI Applications in CancerImaging Deep Learning, Medical Imaging, and MRI AI and ML: Shifting the Paradigm Conclusion FutureScopes OUTLINE

  3. InBrief There has been great progress in research based on Artificial IntelligenceinMedicine(AIM). The corresponding evolution of hardware technology, computer science, biomedicine, and communications has also be en tracked by AIM. Visualization of a new world of “high-performance medicine” by researchers and medical experts results from the convergence of humanand artificialintelligence.

  4. Introduction Medicine is has evolved as a data-centered discipline and, artificial intelligence(AI), in particular, machine learning (ML) has become an attractive field for analyzing the medicaldata. The current process of industrialization of AI has been reflected by this characterization. Therefore, the issues related to the use of Artificial Intelligence and Machine Learning should not be ignored anymore and definitely not in the medicaldomain. AI and ML are drawing much interest from the medical society as a solution tothe knowledge extraction fromdata.

  5. AI Applications in CancerImaging Artificial Intelligencehelps to recognize image patterns that are complex in nature. It also provides the opportunity to interpret the images and transform them from a qualitative taskto the quantifiableone. Artificial Intelligencecan compute the data from the images which is a difficult task for humans and thus harmonizing decision makingclinically. Artificial Intelligencecan also combine multiple data streams and transform them into powerful integrated diagnostic systems spanning genomics, pathology, and electronic healthrecords. Artificial Intelligenceperforms 3 main clinical tasks in cancer imaging: detecting, characterizing, and monitoring thetumors.

  6. Deep Learning, Medical Imaging, andMRI To improve the efficiency of clinical practice, many deep learning methods are used which is increasingregularly. The efficiency of radiology practices can be improved using convolutional neural networks through protocoldetermination. Deep learning is also applied in the field ofradiotherapy. Deep learning is also applied in advanced deformable image registration, which enables the quantitative analysisof different physical imagingmodalities. Deep learning is used from image acquisition to retrieval and from segmentation to prediction of thedisease. Contd..

  7. This process is divided into twoparts: The signal processing chain, including restoration of images, and imageregistration. The application of deep learning in the segmentation of images, detection, and prediction of diseases, and systems based on images and reports, which addresses selected organs like the kidney, brain, the spine, and theprostate. Figure 1: Medicalimages

  8. AI and ML: Shifting theParadigm Artificial Intelligencehas the potential to change the way the healthcare service is carriedout. AI and ML provide solutions to complement the work of doctors for enabling the progress of new treatmentparadigms. If there is a sign of large-vessel occlusion stroke in the scan, it is given first preference and sent to the radiologist’s queue, and the stroke team is also alerted. This helps in treating the patient at the correct time, thereby improving their healthcondition.

  9. Conclusion For many decades the investigations of Artificial Intelligenceand Machine Learning have been developed within the academic environment into broader socialdomains. Artificial intelligence is widely used in monitoring health resources and the result will likely improve efficiency and also reducescost. As with any new technology, the possibilities for the development of AI inthe medical field exist beyond currentimagination.

  10. FutureScopes Artificial Intelligenceand Machine Learning assist radiologists to respond to pressures and interpret studies morerapidly. The pressure of radiologists to take the number of scans has increased in the past 5 years by as much as 20% to50%. Blockchain is used in medical imagingapplications. Blockchain helps to prevent the data breaches in the health care systems that have occurred recently.

  11. CONTACTUS UNITEDKINGDOM +44-1143520021 INDIA +91-4448137070 EMAIL info@tutorsindia.com

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