Data to Diagnosis: A Systematic Review of AI/ML in Healthcare

Notice

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 : 03 | Issue : 02 | Page :
    By

    Sheetal Singh,

  • Khushi Singh,

  • Sujeet Kumar,

  • Aritro Chakraborty,

  • Harshal Singh,

  1. Student, Department of Computer Application Echelon Institute of Technology Faridabad, Haryana, India
  2. Student, Department of Computer Application Echelon Institute of Technology Faridabad, Haryana, India
  3. Assistant Professor, Department of Computer Application Echelon Institute of Technology Faridabad, Haryana, India
  4. Student, Department of Computer Application Echelon Institute of Technology Faridabad, Haryana, India
  5. Professor, Department of Computer Application GL Bajaj Institute of Management Greater Noida, Uttar Pradhesh, India

Abstract

Artificial Intelligence (AI) and Machine Learning (ML) are fast revolutionizing the diagnosis of healthcare by augmenting accuracy, speed, and efficiency. AI/ML technologies facilitate earlier and more accurate disease identification with advanced algorithms for image processing, predictive modelling, and pattern recognition, frequently outperforming conventional diagnostic techniques. This review delves into the key contribution of AI/ML in contemporary healthcare, such as its use in clinical data analysis, imaging reports, and patient histories to enable more timely and informed diagnoses. Emphasis is given to real-time diagnostic functions based on AI/ML, whose potential to streamline processes, eliminate diagnostic errors, and maximize resource allocation is stressed. Data security, ethical considerations, algorithm openness, and regulatory compliance are some of the obstacles that must be properly addressed despite their revolutionary potential. This paper gives an extensive overview of contemporary AI/ML deployments in medical diagnostics, advantages they bring, and challenges that need to be overcome to realize safe, efficient, and large-scale implementation.

Keywords: Artificial Intelligence (AI), Machine Learning (ML), healthcare diagnostics, real-time diagnosis, predictive modeling, image processing, clinical data analysis, medical imaging, pattern recognition, diagnostic accuracy, early disease detection

[This article belongs to International Journal of Biomedical Innovations and Engineering ]

How to cite this article:
Sheetal Singh, Khushi Singh, Sujeet Kumar, Aritro Chakraborty, Harshal Singh. Data to Diagnosis: A Systematic Review of AI/ML in Healthcare. International Journal of Biomedical Innovations and Engineering. 2025; 03(02):-.
How to cite this URL:
Sheetal Singh, Khushi Singh, Sujeet Kumar, Aritro Chakraborty, Harshal Singh. Data to Diagnosis: A Systematic Review of AI/ML in Healthcare. International Journal of Biomedical Innovations and Engineering. 2025; 03(02):-. Available from: https://journals.stmjournals.com/ijbie/article=2025/view=233261


References

  1. Zeb S, N FNU, Abbasi N, Fahad M. AI in healthcare: Revolutionizing diagnosis and therapy. Int J Multidiscip Sci Arts. 2024;3(3):118.
  2. Muthukumarasamy S, Kumar TA, Ayeelyan J, Adimoolam M. Machine learning in healthcare diagnosis. Puducherry Technological University, IFET College of Engineering, Galgotias University, Government Polytechnic College Sevvapet; 2020.
  3. Chen X. AI in healthcare: Revolutionizing diagnosis and treatment through machine learning. MZ J Artif Intell. 2021;1(2).
  4. Pandian NR, Krishna M. Real-time diagnostics with AI/ML: An assessment of its usefulness in smart health care. In: Proceedings of INCOFT 2023. 2023. p. 1–6.
  5. Shweta N, Yadav S, Jayakumar SRS. Time-efficient diagnostics using AI/ML algorithms in smart healthcare. In: Proceedings of the 2022 13th International Conference on Computing, Communication and Networking Technologies (ICCCNT). 2024. p. 1–6.
  6. Kamruzzaman MM. New opportunities, challenges, and applications of Edge-AI for connected healthcare in smart cities. In: Proceedings of the 2022 IEEE Globecom Workshops (GC Wkshps). 2021. p. 1–6.
  7. Lin L, Dacal E, Díez N, Carmona C, Martín-Ramírez A, Barón Argos L, et al. Edge Artificial Intelligence (AI) for real-time automatic quantification of filariasis in mobile microscopy. PLoS Negl Trop Dis. 2024;18(4):e0012117.
  8. Xu Y, Khan TM, Song Y, Meijering E. Edge deep learning in computer vision and medical diagnostics: a comprehensive survey. Artif Intell Rev. 2025;58:93.
  9. “Edge AI for Early Detection of Chronic Diseases and the Spread of Infectious Diseases.” Future Internet. 2023;15(11):370.
  10. Lindroth H, Nalaie K, Raghu R, Ayala IN, Busch C, Bhattacharyya A, et al. Applied Artificial Intelligence in Healthcare: A Review of Computer Vision Technology Application in Hospital Settings. J Imaging. 2024;10(4):81.
Regular Issue Subscription Review Article
Volume 03
Issue 02
Received 10/07/2025
Accepted 11/09/2025
Published 29/11/2025
Publication Time 142 Days
42

Login


My IP

PlumX Metrics