Abstract
In healthcare sector, the people contemplate the best treatments and services notwithstanding of cost. Even if a huge amount of national budget disburses in this sector but it has not attained the society conjecture. Entire medical statistics are investigated by specialist. The complexities and the minutiae of the images and statistics can only be extrapolated by the specialists which escalate the workload and insistence of the specialists. The circumstances propagate to the need for the automated models for the healthcare systems. Artificial intelligence (AI) is a well-built domain of computer science which is attainable solution to all the real-world complications. Thus, AI can come up with exceptional and accurate solution with inordinate precision for medical imaging. Medical imaging embraces the identification, medicament and surveil the diseases in the particular images of medical fields like CT scan, X-rays, ultrasound images. AI methods can be employed to radiology, pathology, and dermatology for image processing. AI methods like deep neural networks, machine learning algorithms, fuzzy logic are some best solutions for image processing. In this paper, divergent AI techniques with their strength, limitations, and applications are delineate and the paper also provides a cognizance to contemporary approaches that attain optimum results in their respective domains. This paper concluded with the discussion of the barriers which reduced the growth of AI and the future opportunities of AI in the healthcare sector.
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Gupta, R., Tripathi, V., Gupta, A., Bhatla, S. (2022). Transformation of Medical Imaging Using Artificial Intelligence: Its Impact and Challenges with Future Opportunities. In: Sharma, T.K., Ahn, C.W., Verma, O.P., Panigrahi, B.K. (eds) Soft Computing: Theories and Applications. Advances in Intelligent Systems and Computing, vol 1380. Springer, Singapore. https://doi.org/10.1007/978-981-16-1740-9_18
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