Session Introduction: AI and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface

Artificial Intelligence (AI) technologies are increasingly capable of processing complex and multilayered datasets. Innovations in generative AI and deep learning have notably enhanced the extraction of insights from both unstructured texts, images, and structured data alike. These breakthroughs in AI technology have spurred a wave of research in the medical field, leading to the creation of a variety of tools aimed at improving clinical decision-making, patient monitoring, image analysis, and emergency response systems. However, thorough research is essential to fully understand the broader impact and potential consequences of deploying AI within the healthcare sector.

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Publication Details

Published
2024-11-21
DOI
https://doi.org/10.1142/9789819807024_0003
Citations
1
Primary Topic
Machine Learning in Healthcare
Type
article
Field-Weighted Citation Impact
0.35
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Session Introduction: AI and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface

Geoffrey H. Tison, Roxana Daneshjou, Jonathan H. Chen, Fateme Nateghi Haredasht et al.
1 citations
Machine Learning in Healthcare
0.35
article

Session Introduction: AI and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface

Geoffrey H. Tison, Roxana Daneshjou, Jonathan H. Chen, Fateme Nateghi Haredasht, Joseph D. Romano, Dokyoon Kim
article en
1 citations

Abstract

Artificial Intelligence (AI) technologies are increasingly capable of processing complex and multilayered datasets. Innovations in generative AI and deep learning have notably enhanced the extraction of insights from both unstructured texts, images, and structured data alike. These breakthroughs in AI technology have spurred a wave of research in the medical field, leading to the creation of a variety of tools aimed at improving clinical decision-making, patient monitoring, image analysis, and emergency response systems. However, thorough research is essential to fully understand the broader impact and potential consequences of deploying AI within the healthcare sector.

Vol. 30
University of California, San Francisco (US), University of Pennsylvania (US), Stanford University (US)
Openalex Percentile: Top 31%
Machine Learning in Healthcare
0.35
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Session Introduction: AI and Machine Learning in Clinical Medicine: Generative and Interactive Systems at the Human-Machine Interface — Geoffrey H. Tison, Roxana Daneshjou, et al. · (2024) | TGRS Research Map | TGRS