The article "Artificial Hallucinations in ChatGPT: Implications in Scientific Writing" by Hussam Alkaissi and Samy I. McFarlane explores the potential and pitfalls of using ChatGPT, a large language model introduced by OpenAI, in scientific writing. The authors present two case studies: one on homocystinuria-associated osteoporosis and another on late-onset Pompe disease (LOPD). They document both positive and negative aspects of ChatGPT's performance, highlighting its ability to generate coherent text but also its tendency to produce "artificial hallucinations," or text that seems realistic but lacks factual accuracy.
Key findings include:
1. **Positive Aspects**: ChatGPT can generate factually correct text on specific medical topics, such as the pathogenesis of homocystinuria-associated osteoporosis.
2. **Negative Aspects**: The provided references were often outdated or irrelevant, and ChatGPT sometimes produced incorrect or misleading information, such as suggesting liver involvement in LOPD, which is not a known feature of the disease.
3. **Artificial Hallucinations**: The authors define artificial hallucinations as the generation of seemingly realistic sensory experiences that do not correspond to real-world input. They note that while ChatGPT is designed to respond based on pre-programmed rules, advanced AI systems can produce hallucinations, especially when trained on large amounts of unsupervised data.
4. **Ethical Concerns**: The use of ChatGPT in academic writing raises ethical and integrity issues, including the potential for creating false experts and the risk of generating inaccurate or misleading content.
The authors recommend that journals and medical conferences modify their policies to maintain rigorous scientific standards and advocate for the inclusion of AI output detectors in the editorial process. They also suggest clear disclosure if AI technologies are used in scientific writing.The article "Artificial Hallucinations in ChatGPT: Implications in Scientific Writing" by Hussam Alkaissi and Samy I. McFarlane explores the potential and pitfalls of using ChatGPT, a large language model introduced by OpenAI, in scientific writing. The authors present two case studies: one on homocystinuria-associated osteoporosis and another on late-onset Pompe disease (LOPD). They document both positive and negative aspects of ChatGPT's performance, highlighting its ability to generate coherent text but also its tendency to produce "artificial hallucinations," or text that seems realistic but lacks factual accuracy.
Key findings include:
1. **Positive Aspects**: ChatGPT can generate factually correct text on specific medical topics, such as the pathogenesis of homocystinuria-associated osteoporosis.
2. **Negative Aspects**: The provided references were often outdated or irrelevant, and ChatGPT sometimes produced incorrect or misleading information, such as suggesting liver involvement in LOPD, which is not a known feature of the disease.
3. **Artificial Hallucinations**: The authors define artificial hallucinations as the generation of seemingly realistic sensory experiences that do not correspond to real-world input. They note that while ChatGPT is designed to respond based on pre-programmed rules, advanced AI systems can produce hallucinations, especially when trained on large amounts of unsupervised data.
4. **Ethical Concerns**: The use of ChatGPT in academic writing raises ethical and integrity issues, including the potential for creating false experts and the risk of generating inaccurate or misleading content.
The authors recommend that journals and medical conferences modify their policies to maintain rigorous scientific standards and advocate for the inclusion of AI output detectors in the editorial process. They also suggest clear disclosure if AI technologies are used in scientific writing.