ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ITS POTENTIALS AND RISKS

Artificial intelligence has moved from science fiction into the clinic with remarkable speed. Machine learning systems now read medical scans, flag sepsis before symptoms declare themselves, draft clinical notes, and coach patients through their smartphones. This article examines the promise and the peril of this transformation in narrative form, and then applies the lens to a single, vivid case: the intertwined epidemics of sleep deprivation and migraine in young adults, where wearable sensors and predictive algorithms are beginning to change how these conditions are detected, studied, and managed. The evidence suggests that AI's greatest near-term contribution is not replacing clinicians but extending their reach — while its greatest dangers, including algorithmic bias, opaque decision making, data privacy erosion, and over-reliance, demand deliberate governance. The article argues that the future of medical AI will be decided less by the sophistication of the algorithms than by the wisdom of the humans who deploy them.

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Publication Details

Journal
TECHNICAL SCIENCE RESEARCH IN UZBEKISTAN
Published
2026-10-15
DOI
https://doi.org/10.5281/zenodo.23401064
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
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article

ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ITS POTENTIALS AND RISKS

Sriyansu Tripathy, Prabhu Prittam Behera, Mohd Shahida, Iram Shamsi et al.
TECHNICAL SCIENCE RESEARCH IN UZBEKISTAN
Artificial Intelligence in Healthcare and Education
article

ARTIFICIAL INTELLIGENCE IN HEALTHCARE: ITS POTENTIALS AND RISKS

Sriyansu Tripathy, Prabhu Prittam Behera, Mohd Shahida, Iram Shamsi, Zunaira khan
article en

Abstract

Artificial intelligence has moved from science fiction into the clinic with remarkable speed. Machine learning systems now read medical scans, flag sepsis before symptoms declare themselves, draft clinical notes, and coach patients through their smartphones. This article examines the promise and the peril of this transformation in narrative form, and then applies the lens to a single, vivid case: the intertwined epidemics of sleep deprivation and migraine in young adults, where wearable sensors and predictive algorithms are beginning to change how these conditions are detected, studied, and managed. The evidence suggests that AI's greatest near-term contribution is not replacing clinicians but extending their reach — while its greatest dangers, including algorithmic bias, opaque decision making, data privacy erosion, and over-reliance, demand deliberate governance. The article argues that the future of medical AI will be decided less by the sophistication of the algorithms than by the wisdom of the humans who deploy them.

TECHNICAL SCIENCE RESEARCH IN UZBEKISTAN
Good health and well-being
Openalex Percentile: Top 21%
Artificial Intelligence in Healthcare and Education
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