What does this research mean for the field?

Artificial intelligence improves diagnostic accuracy and efficiency in healthcare, but its large-scale implementation requires it to be used as a supportive tool under strict clinical supervision, ethical standards, and regulatory frameworks due to ongoing challenges like data privacy and algorithmic bias. Novelty: ClaimNovelty.SYNTHESIS. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

To review recent trends, clinical applications, implementation challenges, and future directions for artificial intelligence integration across healthcare domains.

August 24, 2026International Journal of Technology & Emerging ResearchOpen Access

Artificial Intelligence in Healthcare. A review of recent trends, applications, challenges and future directions

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Authors

NMNasheeda T M

Discussion

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Overview

Literature review demonstrates improved diagnostic accuracy and efficiency across clinical workflows, highlighting the need for ethical oversight and regulatory frameworks.

Key Points

To review recent trends, clinical applications, implementation challenges, and future directions for artificial intelligence integration across healthcare domains.
Conducted a narrative review analyzing six influential studies on artificial intelligence in healthcare published between 2017 and 2024.
Evaluated functional domains including medical imaging, disease diagnosis, drug discovery, precision medicine, remote monitoring, and clinical decision support.
Machine learning, deep learning, and explainable AI enhance diagnostic accuracy, operational workflow efficiency, and individualized patient care.
Primary barriers to widespread implementation include algorithmic bias, privacy vulnerabilities, cybersecurity concerns, lack of model explainability, and regulatory gaps.
Optimal healthcare delivery relies on human–AI collaboration, using models as assistive decision-support tools under strict clinical supervision.

Cite This Study

Nasheeda T M (2026) studied this question.

synapsesocial.com/papers/6a8c00d0bca056c88e6dfd0f https://doi.org/10.64823/ijter.2621034

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August 24, 2026International Journal of Technology & Emerging ResearchOpen Access

Artificial Intelligence in Healthcare. A review of recent trends, applications, challenges and future directions

View Full Paper

Ask AI

Bookmark

Authors

NMNasheeda T M

Discussion

Member takes

Overview

Literature review demonstrates improved diagnostic accuracy and efficiency across clinical workflows, highlighting the need for ethical oversight and regulatory frameworks.

Key Points

To review recent trends, clinical applications, implementation challenges, and future directions for artificial intelligence integration across healthcare domains.
Conducted a narrative review analyzing six influential studies on artificial intelligence in healthcare published between 2017 and 2024.
Evaluated functional domains including medical imaging, disease diagnosis, drug discovery, precision medicine, remote monitoring, and clinical decision support.
Machine learning, deep learning, and explainable AI enhance diagnostic accuracy, operational workflow efficiency, and individualized patient care.
Primary barriers to widespread implementation include algorithmic bias, privacy vulnerabilities, cybersecurity concerns, lack of model explainability, and regulatory gaps.
Optimal healthcare delivery relies on human–AI collaboration, using models as assistive decision-support tools under strict clinical supervision.

Cite This Study

Nasheeda T M (2026) studied this question.

synapsesocial.com/papers/6a8c00d0bca056c88e6dfd0f https://doi.org/10.64823/ijter.2621034

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