Early Disease Detection Using Artificial Intelligence

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Notice

nThis is an unedited manuscript accepted for publication and provided as an Article in Press for early access at the author’s request. The article will undergo copyediting, typesetting, and galley proof review before final publication. Please be aware that errors may be identified during production that could affect the content. All legal disclaimers of the journal apply.n

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Year : 2025 [if 2224 equals=””]30/09/2025 at 3:47 PM[/if 2224] | [if 1553 equals=””] Volume : 14 [else] Volume : 14[/if 1553] | [if 424 equals=”Regular Issue”]Issue : [/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03 | Page :

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    Pavandeep Kaur, Parul Gogia, Aekansh Khandelwal, Rishabh Raj,

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  1. Student, Student, Student, Student, Department of Computer Science and Engineering Apex Institute of Technology Chandigarh University Mohali, Department of Computer Science and Engineering, Apex Institute of Technology Chandigarh University Mohali, Department of Computer Science and Engineering, Apex Institute of Technology Chandigarh University Mohali, Department of Computer Science and Engineering, Apex Institute of Technology Chandigarh University Mohali, Pubjab, Punjab, Punjab, Punjab, India, India, India, India

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Abstract

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nGrowth in artificial intelligence and machine learning now make it possible for the healthcare sector to be totally transformed by a new chapter, particularly in the era of medical image analysis. This study focuses on harnessing these advancements to develop a sophisticated model for early disease detection across diverse medical domains, majorly in skin disease. By integrating diverse datasets and leveraging advanced algorithms, our methodology aims to identify subtle disease indicators at their inception, facilitating timely interventions and personalized treatment strategies. Through meticulous data collection, preprocessing, and exploratory analysis, the study establishes the groundwork for the development of robust AI models capable of interpreting complex medical imaging data. The proposed methodology emphasizes the integration of domain- specific clinical expertise to ensure the clinical relevance and interpretability of the models. Rigorous validation and evaluation demonstrate the efficacy and generalization capacity of our approach, paving the way for its seamless integration into clinical practice.nn

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Keywords: Early Detection, Artificial Intelligence, Machine Learning, Medical Imaging, Disease Prediction

n[if 424 equals=”Regular Issue”][This article belongs to Research and Reviews : A Journal of Medical Science and Technology ]

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[/if 424][if 424 equals=”Special Issue”][This article belongs to Special Issue under section in Research and Reviews : A Journal of Medical Science and Technology (rrjomst)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nPavandeep Kaur, Parul Gogia, Aekansh Khandelwal, Rishabh Raj. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Early Disease Detection Using Artificial Intelligence[/if 2584]. Research and Reviews : A Journal of Medical Science and Technology. 30/09/2025; 14(03):-.

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How to cite this URL:
nPavandeep Kaur, Parul Gogia, Aekansh Khandelwal, Rishabh Raj. [if 2584 equals=”][226 striphtml=1][else]Early Disease Detection Using Artificial Intelligence[/if 2584]. Research and Reviews : A Journal of Medical Science and Technology. 30/09/2025; 14(03):-. Available from: https://journals.stmjournals.com/rrjomst/article=30/09/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Original Research

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Volume 14
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 03
Received 10/07/2025
Accepted 19/08/2025
Published 30/09/2025
Retracted
Publication Time 82 Days

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