Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction

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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=””]29/08/2025 at 4:26 PM[/if 2224] | [if 1553 equals=””] Volume : 15 [else] Volume : 15[/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] 02 | Page :

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    Rajeshwaran V, Logeshwaran N, Manjubhasini R, Keerthika,

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  1. Student, Student, Student, Professor, Department of Artificial Intelligence and Data Science, Karpagam College of Engineering Coimbatore, Department of Artificial Intelligence and Data Science, Karpagam College of Engineering Coimbatore, Department of Artificial Intelligence and Data Science, Karpagam College of Engineering Coimbatore, Department of Artificial Intelligence and Data Science Karpagam College of Engineering Coimbatore, Tamil Nadu, Tamil Nadu, Tamil Nadu, Tamil Nadu, India, India, India, India

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Abstract

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nDeep learning techniques are used in an AI-driven healthcare system to improve disease identification and medical picture analysis. Data collection, preprocessing, model training, and evaluation are all part of the system’s systematic workflow. Various deep learning architectures, such as ResNet50, VGG-16, and U-Net, are employed for precise classification and segmentation of medical images. The approach incorporates advanced techniques such as optimization, transfer learning, and data augmentation to significantly enhance the overall performance and robustness of the model. Optimization ensures that the computational resources are effectively utilized, leading to improved convergence rates and reduced training time. Transfer learning leverages pre-trained models on large benchmark datasets, allowing the framework to extract high-level feature representations and achieve superior accuracy even with limited domain-specific data. Data augmentation further strengthens the system by artificially increasing the diversity of the training dataset, helping the model generalize better and reducing the risk of overfitting. The framework supports multiple classification tasks, enabling the identification and interpretation of complex patterns in images for efficient and reliable classification. To validate its dependability and efficiency in predictive analytics, the system is rigorously evaluated using critical performance indicators such as accuracy, precision, recall, and F1-score. By leveraging large-scale datasets and refining computational models, this solution provides a scalable and automated diagnostic support tool, enhancing decision-making processes across various applications.nn

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Keywords: AI-driven Healthcare, Deep Learning, Medical image analysis, Disease Detection, Data Collection.

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

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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 Health Professions (rrjohp)][/if 424][if 424 equals=”Conference”]This article belongs to Conference [/if 424]

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How to cite this article:
nRajeshwaran V, Logeshwaran N, Manjubhasini R, Keerthika. [if 2584 equals=”][226 wpautop=0 striphtml=1][else]Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction[/if 2584]. Research and Reviews: A Journal of Health Professions. 29/08/2025; 15(02):-.

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nRajeshwaran V, Logeshwaran N, Manjubhasini R, Keerthika. [if 2584 equals=”][226 striphtml=1][else]Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction[/if 2584]. Research and Reviews: A Journal of Health Professions. 29/08/2025; 15(02):-. Available from: https://journals.stmjournals.com/rrjohp/article=29/08/2025/view=0

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[if 424 not_equal=””]Regular Issue[else]Published[/if 424] Subscription Review Article

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Volume 15
[if 424 equals=”Regular Issue”]Issue[/if 424][if 424 equals=”Special Issue”]Special Issue[/if 424] [if 424 equals=”Conference”][/if 424] 02
Received 25/05/2025
Accepted 23/08/2025
Published 29/08/2025
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Publication Time 96 Days

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