Transforming Medical Diagnostics with Artificial Intelligence

This review talks about how Artificial Intelligence will bring about a sea change in medical diagnosis, particularly in imaging, genomics, and personalized medicine. AI algorithms are developed to increase the speed and accuracy of interpretation of medical images like X-rays and MRIs, greatly improving diagnostic accuracy and allowing rapid interventions. AI also supports radiologists by outlining areas of concern, reducing human error, and optimizing workflow efficiency. The applications of AI in genomics bring out the exact identification of genetic mutations, which can help a doctor derive a treatment plan based on the needs of each patient. However, there are many challenges to integrating AI into healthcare, such as possible biases, integration problems, and high costs that may increase healthcare disparities. Unless these challenges are addressed, there could be unequal creation and access to AI-driven diagnostic tools, and their potential for improving patient outcomes will not be completely realized.

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Publication Details

Journal
International Journal of Medical Science
Published
2025-05-31
DOI
https://doi.org/10.14445/23939117/ijms-v12i3p101
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
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article

Transforming Medical Diagnostics with Artificial Intelligence

Labdhi Jain
International Journal of Medical Science
Machine Learning in Healthcare
article

Transforming Medical Diagnostics with Artificial Intelligence

Labdhi Jain
article en

Abstract

This review talks about how Artificial Intelligence will bring about a sea change in medical diagnosis, particularly in imaging, genomics, and personalized medicine. AI algorithms are developed to increase the speed and accuracy of interpretation of medical images like X-rays and MRIs, greatly improving diagnostic accuracy and allowing rapid interventions. AI also supports radiologists by outlining areas of concern, reducing human error, and optimizing workflow efficiency. The applications of AI in genomics bring out the exact identification of genetic mutations, which can help a doctor derive a treatment plan based on the needs of each patient. However, there are many challenges to integrating AI into healthcare, such as possible biases, integration problems, and high costs that may increase healthcare disparities. Unless these challenges are addressed, there could be unequal creation and access to AI-driven diagnostic tools, and their potential for improving patient outcomes will not be completely realized.

International Journal of Medical ScienceVol. 12(3)
Film Independent (US)
Openalex Percentile: Top 94%
Machine Learning in Healthcare
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Transforming Medical Diagnostics with Artificial Intelligence — Labdhi Jain · International Journal of Medical Science (2025) | TGRS Research Map | TGRS