In past two years Artificial Intelligence (AI) and Machine Learning
(ML) approach has made a high impact in medical field and completely
redefined the methodology of medical diagnosis with the help of
advanced Computational Algorithms (CA). High performance
computing Systems (HPCS) and community powered large-scale
database can be accessed through the algorithms. AI has illustrated
proficiency performance mainly in cancer analysis. The advanced
computer programs called CA helps doctor to identify illness or
condition of a patient through analysing medical data. Through
analysis progresses in health of patient and future predictions can be
identified by the doctor and based on medical history and condition of
patient’s treatments can be provided. Nevertheless, only hardly one or
two AI applications have been upgraded and moved forwarded to real
world clinical environments. There is always a debate on AI where few
AI can improve, enhance and human proficiency by giving correct,
quicker and easy understanding in dept analysis of medical data
through which health of patient can be monitored. Others worry that
AI can completely replace the role and jobs of doctors and decrease in
interaction between human and doctor. This article provides in dept
knowledge about AI can be used in health care assistance.
Methodologies such as radiographic imaging, drug identification, data
analysis , electronic health record has been discovered and challenges
, opportunities has been highlighted in this article. Finally, we address
the critical challenges impeding AI’s transition from research to
clinical application, such as data privacy, regulatory hurdles, and
integration with clinical workflows, while providing insights into the
future role of AI in precision oncology and personalized medicine.
Finally result has been evaluated in means of accuracy, sensitivity,
specificity for CNN, SVM and Random Forest.
N. Ganapathi Ram, S. Karthikeyan
Rathinam College of Arts and Science, India
AI Healthcare, Cancer Diagnosis, Deep Learning, Machine Learning, SVM, CNN, Random Forest
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| Published By :
ICTACT
Published In :
ICTACT Journal on Image and Video Processing
( Volume: 15 , Issue: 3 , Pages: 3495 - 3500 )
Date of Publication :
February 2025
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