Integration of large language models in Nigeria's healthcare: doctors early perspectives, operational barriers, and privacy concerns

Abstract The advent of large language models (LLMs) like ChatGPT has the potential to revolutionize healthcare, offering opportunities for improved patient engagement, clinical decision support, and administrative efficiency. This pioneering study aimed to capture the early perspectives of Nigerian medical professionals on LLMs, particularly ChatGPT, immediately following its 2022 introduction by OpenAI. It explores initial responses, anticipated barriers, and data privacy concerns during this transformative period. A nationwide cross-sectional survey was conducted between February and March 2023, involving 406 medical doctors across Nigeria's six geopolitical zones. Among those aware of ChatGPT, 66.9% were willing to use it. Most (41.4%) were "not very confident" using LLMs for patient care. The majority (54.0%) rated LLMs "somewhat important" for healthcare integration, and 76.6% believed they could improve outcomes. No explored factors significantly influenced willingness to use LLMs. Primary perceived benefits included improved patient information access (65.2%), clinical decision-making efficiency (57.7%), and patient-provider communication (46.4%). Major concerns were misinterpretation (80.1%), technology dependence (66.3%), liability (65.5%), lack of human interaction (60.7%), and privacy/security (51.3%). These findings highlight both optimism and apprehension towards LLMs, recognizing potential benefits and concerns about misinterpretation, overreliance, legal implications, and privacy, thus underscoring the need for educational programs, ethical frameworks, and robust data protection.

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Publication Details

Journal
Discover Health Systems
Published
2024-12-03
DOI
https://doi.org/10.1007/s44250-024-00168-5
Citations
2
Primary Topic
Artificial Intelligence in Healthcare and Education
Type
article
Field-Weighted Citation Impact
0.24
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article

Integration of large language models in Nigeria's healthcare: doctors early perspectives, operational barriers, and privacy concerns

Chikodi Felicitas Anarado, Ugo N. Chikani, Ezinne Ifeyinwa Nwaneli, Arinze Ejike Ulasi et al.
2 citations
Discover Health Systems
Artificial Intelligence in Healthcare and Education
0.24
article

Integration of large language models in Nigeria's healthcare: doctors early perspectives, operational barriers, and privacy concerns

Chikodi Felicitas Anarado, Ugo N. Chikani, Ezinne Ifeyinwa Nwaneli, Arinze Ejike Ulasi, Thomas Obiajulu Ulasi, Amaechi Chinedu Nwachukwu, Chisom Adaobi Nri-Ezedi, Ifeanyi O. Okonkwo, Sani Musa, Ijeoma Onyinyechukwu Ohuche, Ugochukwu Stanley Ezidiegwu, Nnaemeka Izuchukwu Ulasi
article en
2 citations

Abstract

Abstract The advent of large language models (LLMs) like ChatGPT has the potential to revolutionize healthcare, offering opportunities for improved patient engagement, clinical decision support, and administrative efficiency. This pioneering study aimed to capture the early perspectives of Nigerian medical professionals on LLMs, particularly ChatGPT, immediately following its 2022 introduction by OpenAI. It explores initial responses, anticipated barriers, and data privacy concerns during this transformative period. A nationwide cross-sectional survey was conducted between February and March 2023, involving 406 medical doctors across Nigeria's six geopolitical zones. Among those aware of ChatGPT, 66.9% were willing to use it. Most (41.4%) were "not very confident" using LLMs for patient care. The majority (54.0%) rated LLMs "somewhat important" for healthcare integration, and 76.6% believed they could improve outcomes. No explored factors significantly influenced willingness to use LLMs. Primary perceived benefits included improved patient information access (65.2%), clinical decision-making efficiency (57.7%), and patient-provider communication (46.4%). Major concerns were misinterpretation (80.1%), technology dependence (66.3%), liability (65.5%), lack of human interaction (60.7%), and privacy/security (51.3%). These findings highlight both optimism and apprehension towards LLMs, recognizing potential benefits and concerns about misinterpretation, overreliance, legal implications, and privacy, thus underscoring the need for educational programs, ethical frameworks, and robust data protection.

Discover Health SystemsVol. 3(1)
Chukwuemeka Odumegwu Ojukwu University (NG), Nnamdi Azikiwe University (NG), Ahmadu Bello University Teaching Hospital (NG), Nnamdi Azikiwe University Teaching Hospital (NG), University of Nigeria Teaching Hospital (NG)
Peace, Justice and strong institutions
Openalex Percentile: Top 39%
Artificial Intelligence in Healthcare and Education
0.24
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