AI in Healthcare: ChatGPT's Significance in Transforming Patient-Physician Communication and Clinical Assistance

Artificial intelligence (AI) is bringing revolutionary changes in healthcare, especially in improving communication between patients and doctors and supporting clinical decisions. The background of this study is the increasing complexity of modern healthcare which requires more efficient tools in managing communication and decision-making. The purpose of this study was to evaluate the effectiveness of ChatGPT in improving patient-physician communication, supporting clinical decisions, and increasing patient engagement. The methods used included a combination of qualitative data collection through interviews with healthcare professionals and quantitative data analysis from electronic medical records to evaluate the impact of ChatGPT. The results showed that ChatGPT significantly improved patient-physician communication, provided evidence-based recommendations, and automated repetitive tasks, ultimately improving patient satisfaction and care outcomes. The implications of this study suggest that AI integrations such as ChatGPT can reduce the administrative burden on healthcare workers and enable more efficient and personalized care delivery. Ethical and technical challenges related to data privacy and bias in AI systems remain a concern going forward.

Authors

Publication Details

Journal
Asian Journal of Engineering Social and Health
Published
2024-09-23
DOI
https://doi.org/10.46799/ajesh.v3i9.434
Citations
2
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.24
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

AI in Healthcare: ChatGPT's Significance in Transforming Patient-Physician Communication and Clinical Assistance

Muhammad Fahad, Shah Zeb, Nizamullah FNU, Nasrullah Abbasi et al.
2 citations
Asian Journal of Engineering Social and Health
Artificial Intelligence in Healthcare and Education
0.24
article

AI in Healthcare: ChatGPT's Significance in Transforming Patient-Physician Communication and Clinical Assistance

Muhammad Fahad, Shah Zeb, Nizamullah FNU, Nasrullah Abbasi, Muhammad Umer Qayyum
article en
2 citations

Abstract

Artificial intelligence (AI) is bringing revolutionary changes in healthcare, especially in improving communication between patients and doctors and supporting clinical decisions. The background of this study is the increasing complexity of modern healthcare which requires more efficient tools in managing communication and decision-making. The purpose of this study was to evaluate the effectiveness of ChatGPT in improving patient-physician communication, supporting clinical decisions, and increasing patient engagement. The methods used included a combination of qualitative data collection through interviews with healthcare professionals and quantitative data analysis from electronic medical records to evaluate the impact of ChatGPT. The results showed that ChatGPT significantly improved patient-physician communication, provided evidence-based recommendations, and automated repetitive tasks, ultimately improving patient satisfaction and care outcomes. The implications of this study suggest that AI integrations such as ChatGPT can reduce the administrative burden on healthcare workers and enable more efficient and personalized care delivery. Ethical and technical challenges related to data privacy and bias in AI systems remain a concern going forward.

Asian Journal of Engineering Social and HealthVol. 3(9)
Openalex Percentile: Top 40%
Artificial Intelligence in Healthcare and Education
0.24
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.