Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision support
Large Language Models (LLMs) are transforming patient education in medication management by providing accessible information to support healthcare decision-making. Building on our recent scoping review of LLMs in patient education, this perspective examines their specific role in medication guidance...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Medicine |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fmed.2025.1527864/full |
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| author | Serhat Aydin Mert Karabacak Victoria Vlachos Konstantinos Margetis |
| author_facet | Serhat Aydin Mert Karabacak Victoria Vlachos Konstantinos Margetis |
| author_sort | Serhat Aydin |
| collection | DOAJ |
| description | Large Language Models (LLMs) are transforming patient education in medication management by providing accessible information to support healthcare decision-making. Building on our recent scoping review of LLMs in patient education, this perspective examines their specific role in medication guidance. These artificial intelligence (AI)-driven tools can generate comprehensive responses about drug interactions, side effects, and emergency care protocols, potentially enhancing patient autonomy in medication decisions. However, significant challenges exist, including the risk of misinformation and the complexity of providing accurate drug information without access to individual patient data. Safety concerns are particularly acute when patients rely solely on AI-generated advice for self-medication decisions. This perspective analyzes current capabilities, examines critical limitations, and raises questions regarding the possible integration of LLMs in medication guidance. We emphasize the need for regulatory oversight to ensure these tools serve as supplements to, rather than replacements for, professional healthcare guidance. |
| format | Article |
| id | doaj-art-7652f74836b1471791a951df716ca399 |
| institution | Kabale University |
| issn | 2296-858X |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Medicine |
| spelling | doaj-art-7652f74836b1471791a951df716ca3992025-01-23T06:56:18ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2025-01-011210.3389/fmed.2025.15278641527864Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision supportSerhat Aydin0Mert Karabacak1Victoria Vlachos2Konstantinos Margetis3School of Medicine, Koç University, Istanbul, TürkiyeDepartment of Neurosurgery, Mount Sinai Health System, New York, NY, United StatesCollege of Human Ecology, Cornell University, Ithaca, NY, United StatesDepartment of Neurosurgery, Mount Sinai Health System, New York, NY, United StatesLarge Language Models (LLMs) are transforming patient education in medication management by providing accessible information to support healthcare decision-making. Building on our recent scoping review of LLMs in patient education, this perspective examines their specific role in medication guidance. These artificial intelligence (AI)-driven tools can generate comprehensive responses about drug interactions, side effects, and emergency care protocols, potentially enhancing patient autonomy in medication decisions. However, significant challenges exist, including the risk of misinformation and the complexity of providing accurate drug information without access to individual patient data. Safety concerns are particularly acute when patients rely solely on AI-generated advice for self-medication decisions. This perspective analyzes current capabilities, examines critical limitations, and raises questions regarding the possible integration of LLMs in medication guidance. We emphasize the need for regulatory oversight to ensure these tools serve as supplements to, rather than replacements for, professional healthcare guidance.https://www.frontiersin.org/articles/10.3389/fmed.2025.1527864/fullLarge Language ModelsChatGPTpatient educationself-medicationartificial intelligencemachine learning |
| spellingShingle | Serhat Aydin Mert Karabacak Victoria Vlachos Konstantinos Margetis Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision support Frontiers in Medicine Large Language Models ChatGPT patient education self-medication artificial intelligence machine learning |
| title | Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision support |
| title_full | Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision support |
| title_fullStr | Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision support |
| title_full_unstemmed | Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision support |
| title_short | Navigating the potential and pitfalls of large language models in patient-centered medication guidance and self-decision support |
| title_sort | navigating the potential and pitfalls of large language models in patient centered medication guidance and self decision support |
| topic | Large Language Models ChatGPT patient education self-medication artificial intelligence machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fmed.2025.1527864/full |
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