How the landscape of biomedicine and health is changing from ChatGPT and other large language models (LLM)? Recently paper titled “Opportunities and Challenges for ChatGPT and Large Language Models in Biomedicine and Health” delves into the multifaceted role of Large Language Models (LLMs) such as ChatGPT in the biomedical and health sectors, highlighting their significant contributions as well as the challenges and limitations they face upright.
In the field of biomedicine and health, LLMs are revolutionizing several key areas. They are a biomedical information retrieval tool, support literature searches, answer questions, and recommend articles—all critical to informed clinical decision making and knowledge acquisition. Another important application is in question answering systems, where these models support clinical decisions and contribute to medical education. The ability of LLMs to summarize medical texts is also remarkable, as it helps to condense extensive medical information into more manageable and understandable summaries. Information extraction is another area where these models excel, organizing unstructured biomedical textual data into structured formats. Finally, the use of LLMs in medical education marks a burgeoning area of research and development, opening up new avenues of learning and teaching.
However, implementing LLMs in these high-stakes fields is not without its challenges. A major problem is the limitations of these models, especially when applied to critical areas such as biomedicine and healthcare. Issues of equity and bias are also important, as LLMs may inadvertently retain biases present in their training data, which may lead to health care disparities. Privacy concerns are another significant challenge given the sensitive nature of patient data and the potential for privacy breaches. The legal and ethical implications of the use of LLMs in medicine and healthcare are also the subject of ongoing debate, highlighting the need for a robust legal framework to ensure the safe and responsible application of these technologies. Finally, the paper states difficulty in the overall evaluation of these models, especially given the labor-intensive and expensive nature of expert evaluations required for tasks such as question answering and text summarization.
In conclusion, while LLMs such as ChatGPT have made remarkable strides in the field of biomedicine and health, surpassing previous methods in text generation and showing potential to revolutionize various aspects of the field, their application is accompanied by significant risks and challenges. These include fabricated information, legal and privacy concerns, and the need for comprehensive evaluations to ensure their safety and effectiveness in sensitive areas such as healthcare.
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