Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction

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This 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.

Year : 2025 | Volume : 15 | Issue : 02 | Page :
    By

    Rajeshwaran V,

  • Logeshwaran N,

  • Manjubhasini R,

  • Keerthika,

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

Abstract

Deep 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.

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

[This article belongs to Research and Reviews: A Journal of Health Professions ]

How to cite this article:
Rajeshwaran V, Logeshwaran N, Manjubhasini R, Keerthika. Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction. Research and Reviews: A Journal of Health Professions. 2025; 15(02):-.
How to cite this URL:
Rajeshwaran V, Logeshwaran N, Manjubhasini R, Keerthika. Ai-Driven Healthcare System for Enhanced Diagnosis and Patient Interaction. Research and Reviews: A Journal of Health Professions. 2025; 15(02):-. Available from: https://journals.stmjournals.com/rrjohp/article=2025/view=225114


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Regular Issue Subscription Review Article
Volume 15
Issue 02
Received 25/05/2025
Accepted 23/08/2025
Published 29/08/2025
Publication Time 96 Days
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