Artificial intelligence in healthcare

In recent years, enhanced artificial intelligence algorithms and more access to training data have enabled artificial intelligence to augment or supplant certain functions of physicians. Nonetheless, the interest of diverse stakeholders in the application of artificial intelligence in medicine has not resulted in extensive acceptance. Numerous experts have indicated that a primary cause for the limited adoption is the lack of openness surrounding certain artificial intelligence algorithms, particularly black-box algorithms. Clinical medicine, particularly evidence-based practice, depends on transparency in decision-making. If there is no medically explicable artificial intelligence and the physician cannot adequately elucidate the decision-making process, the patient's trust in them will diminish. To resolve the transparency concern associated with specific artificial intelligence models, explainable artificial intelligence has arisen.

Authors

Institutions

Publication Details

Journal
International Journal of Clinical Medical Research
Published
2025-02-15
DOI
https://doi.org/10.61466/ijcmr3020001
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Artificial intelligence in healthcare

Jakub Dominik
International Journal of Clinical Medical Research
Artificial Intelligence in Healthcare and Education
article

Artificial intelligence in healthcare

Jakub Dominik
article en

Abstract

In recent years, enhanced artificial intelligence algorithms and more access to training data have enabled artificial intelligence to augment or supplant certain functions of physicians. Nonetheless, the interest of diverse stakeholders in the application of artificial intelligence in medicine has not resulted in extensive acceptance. Numerous experts have indicated that a primary cause for the limited adoption is the lack of openness surrounding certain artificial intelligence algorithms, particularly black-box algorithms. Clinical medicine, particularly evidence-based practice, depends on transparency in decision-making. If there is no medically explicable artificial intelligence and the physician cannot adequately elucidate the decision-making process, the patient's trust in them will diminish. To resolve the transparency concern associated with specific artificial intelligence models, explainable artificial intelligence has arisen.

International Journal of Clinical Medical ResearchVol. 3(2)
Na Homolce Hospital (CZ)
Openalex Percentile: Top 98%
Artificial Intelligence in Healthcare and Education
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.