Published on: April 2026
AI IN HEALTHCARE : DIAGNOSIS AND PATIENT MONITORING
RAHUL BHATIYA PARAS SHARMA
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Abstract
The adoption of Artificial Intelligence (AI) as an element of medical procedures has transitioned from a mere disruption to become a fundamental part of today's practice. The current research paper addresses how AI revolutionizes the healthcare industry, namely, automated diagnoses and real-time patient monitoring. The utilization of neural network models to analyze imaging via CNNs and time series data using Transformers proves that, indeed, AI is capable of diagnosing patients at least as well as human specialists in areas such as radiology and pathology.
Moreover, this paper sheds light on the move from traditional approaches to treatment to the concept of Remote Patient Monitoring (RPM) enabled by advanced AI wearables and contactless sensors, which are capable of predicting a clinical deterioration in a patient's health up to 16 hours before any symptoms emerge. However, despite the impressive capabilities demonstrated, the opacity of neural networks and algorithmic biases remain significant obstacles to establishing the ubiquitous trust. Therefore, one can conclude that AI greatly decreases diagnostic delay and eliminates clinician's stress, yet the future of the industry lies in XAI solutions and federated learning systems.
How to Cite this Paper
BHATIYA, R. & SHARMA, P. (2026). AI in Healthcare : Diagnosis and Patient Monitoring. International Journal of Creative and Open Research in Engineering and Management, <i>02</i>(04). https://doi.org/10.55041/ijcope.v2i4.280
BHATIYA, RAHUL, and PARAS SHARMA. "AI in Healthcare : Diagnosis and Patient Monitoring." International Journal of Creative and Open Research in Engineering and Management, vol. 02, no. 04, 2026, pp. . doi:https://doi.org/10.55041/ijcope.v2i4.280.
BHATIYA, RAHUL, and PARAS SHARMA. "AI in Healthcare : Diagnosis and Patient Monitoring." International Journal of Creative and Open Research in Engineering and Management 02, no. 04 (2026). https://doi.org/https://doi.org/10.55041/ijcope.v2i4.280.
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Ethical Compliance & Review Process
- •All submissions are screened under plagiarism detection.
- •Review follows editorial policy.
- •Authors retain copyright.
- •Peer Review Type: Double-Blind Peer Review
- •Published on: Apr 17 2026
This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. You are free to share and adapt this work for non-commercial purposes with proper attribution.
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