IJSMT Journal

International Journal of Science, Strategic Management and Technology

An International, Peer-Reviewed, Open Access Scholarly Journal Indexed in recognized academic databases · DOI via Crossref The journal adheres to established scholarly publishing, peer-review, and research ethics guidelines set by the UGC

ISSN: 3108-1762 (Online)
webp (1)

Plagiarism Passed
Peer reviewed
Open Access

LEVERAGING ARTIFICIAL INTELLIGENCE FOR EARLY DIAGNOSIS AND PREDICTIVE HEALTHCARE SOLUTIONS

AUTHORS:
Karan Mahato
Mentor
Ankur Chaudhary
Affiliation
Department of CSE-IT Noida Institute of Engineering and Technology
CC BY 4.0 License:
This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Abstract

The rapid emergence and evolution of AI has revolutionized the healthcare industry by providing new tools and techniques that can help improve the diagnosis and prognosis of diseases. The integration of these technologies has allowed medical professionals to make more informed decisions and improve their efficiency.This paper explores the role of AI in enhancing healthcare systems, with a primary focus on early diagnosis and predictive healthcare solutions. AI-driven models analyze large volumes of medical data, including electronic health records, medical imaging, and patient history, to identify patterns and detect diseases at an early stage. These capabilities help in reducing diagnostic errors, improving treatment outcomes, and enabling personalized healthcare.


Using AI-driven predictive analytics, healthcare professionals can anticipate possible health problems and take steps to prevent them, which helps reduce the seriousness and expense of illnesses. The study also talks about how AI is used in areas like finding cancer, predicting heart problems, and keeping track of health in smart ways. Even though AI has many benefits, using it in healthcare comes with some difficulties. These include worries about keeping patient information private, questions about the ethics of AI decisions, the need to understand how AI models work, and the fact that they require very good quality data to function properly. This paper talks about these challenges and gives ideas about what could come next in AI-based healthcare systems.


Overall, the research emphasizes that AI has the potential to revolutionize healthcare by supporting clinicians, improving diagnostic accuracy, and enabling proactive patient care through predictive analysis.

Keywords
Article Metrics
Article Views
34
PDF Downloads
0
HOW TO CITE
APA

MLA

Chicago

Copy

Mahato, K. (2026). Leveraging Artificial Intelligence for Early Diagnosis and Predictive Healthcare Solutions. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.267

Mahato, Karan. "Leveraging Artificial Intelligence for Early Diagnosis and Predictive Healthcare Solutions." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.267.

Mahato, Karan. "Leveraging Artificial Intelligence for Early Diagnosis and Predictive Healthcare Solutions." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.267.

References
1.Jena, S. Das, and J. Panda, “AI-driven smart e-healthcare for intracranial hemorrhage diagnosis using deep transfer learning and hybrid CNN-Bi-LSTM radiomics,” Discover Computing, vol. 29, 2026.

2.Ben Ahmed, L. O. Hall, D. Goldgof, and R. Fogarty, “Achieving multi-site generalization for CNN-based disease

diagnosis models by mitigating shortcut learning,” IEEE Access, vol. 10, 2022.

3.“Generative AI in different imaging modalities for disease diagnosis: A review,” Expert Systems with Applications, vol. 309, 2026.

4.“AI Integration in Medical Imaging: Advanced Analysis of Chest X-ray,” IEEE Conference Proceedings, 2024.

5.Aryendu and Y. Wang, “RAIDER: Rapid AI Diagnosis at Edge using Ensemble Models for Radiology,” IEEE Access, 2024.

6.“A review of convolutional neural network based methods for medical image classification,” Computer Methods and Programs in Biomedicine, 185, 2025.

7.Gao et al., “Deep residual inception encoder-decoder network for medical imaging synthesis,” IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 1, pp. 39–49, 2020.

8.Li et al., “Path R-CNN for prostate cancer diagnosis and Gleason grading of histological images,” IEEE Transactions on Medical Imaging, vol. 38, no. 4, pp. 945–954, 2019
Ethics and Compliance
✓ All ethical standards met
This article has undergone plagiarism screening and double-blind peer review. Editorial policies have been followed. Authors retain copyright under CC BY-NC 4.0 license. The research complies with ethical standards and institutional guidelines.
Indexed In
Similar Articles
A Study on the Performance Appraisal of Employees at Sri Lakshmi Saraswathi Textile Mills Arni
string(15) "Dr.A.Sivanandam" Dr.A.Sivanandam,
(2026)
DOI: 10.55041/ijsmt.v2i5.384
AI-Powered Resume Screening and Ranking System
string(13) "Jerin Samuvel" Samuvel, J.
(2026)
DOI: 10.55041/ijsmt.v2i3.337
Fintech Apps as Tools for Promoting Financial Literacy: A Study Among Students in Coimbatore City
string(8) "Lohith.G" Lohith.G, et al.
(2026)
DOI: 10.55041/ijsmt.v2i3.051
Scroll to Top