AI in Medical Diagnosis and Prognosis: Current State and Challenges

Weiyi Jing

doi:10.54254/2753-8818/2026.AU30046

Theoretical and Natural ScienceOpen access

AI in Medical Diagnosis and Prognosis: Current State and Challenges

Research Article

Open Access

AI in Medical Diagnosis and Prognosis: Current State and Challenges

Weiyi Jing ^1*

¹ Brearley School, New York City, USA

^*Corresponding author: Wjing1105@gmail.com

Published on 26 November 2025

TNS Vol.152

ISSN (Print): 2753-8826

ISSN (Online): 2753-8818

ISBN (Print): 978-1-80590-565-3

ISBN (Online): 978-1-80590-566-0

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Abstract

The COVID-19 pandemic led to physical distancing, thus increasing the use of digital health programs such as artificial intelligence (AI) platforms. Throughout this paper, we will be using AI to describe a system that performs actions that typically require human thinking skills. AI has the potential to transform into a healthcare organization using machine learning, deep learning, and natural language processing. This review will include AI-based diagnostic and prediction tools, and their potential in changing the detection and management of disease in various fields of medicine. AI algorithms are needed to analyze complex medical data, such as X-ray images and heart signals, and often exceed the accuracy of early human detection. Additionally, there are now AI-based wearable devices and supported systems available for real-time detection and personal management, which could also advance a person’s capability toward earlier detection and prevention of disease. Of course, data privacy challenges, constant data access issues, and algorithmic inequity are still present. Collaboration in addressing data collection, algorithm design, and constant monitoring or evaluation will be needed across disciplines. As institutions of health care, it is critical to ensure that the data collected, and the algorithm designed are transparent for AI to be applied in the real-world healthcare field.

Keywords:

AI, diagnosis, health care, monitoring, algorithm

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Cite this article

Jing,W. (2025). AI in Medical Diagnosis and Prognosis: Current State and Challenges. Theoretical and Natural Science,152,66-72.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

About volume

Volume title: Proceedings of ICMMGH 2026 Symposium: Biomedical Imaging and AI Applications in Neurorehabilitation

ISBN: 978-1-80590-565-3(Print) / 978-1-80590-566-0(Online)

Editor: Sheiladevi Sukumaran, Alan Wang

Conference website: https://www.icmmgh.org/auckland.html

Conference date: 14 November 2025

Series: Theoretical and Natural Science

Volume number: Vol.152

ISSN: 2753-8818(Print) / 2753-8826(Online)

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