[Impacts and implications of conversational artificial intelligence tools in hematology: a critical evaluation of performance and patient perception]
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View abstract on PubMed
Summary
This summary is machine-generated.Large Language Models (LLMs) show potential in hematology but have performance and bias issues. Medical supervision is crucial for safe, complementary use in interpreting lab results.
Area Of Science
- Medical Informatics
- Hematology
- Artificial Intelligence
Background
- Large Language Models (LLMs) offer accessible medical information.
- Hematology involves complex test interpretation, where LLMs could assist patients and professionals.
- Limited evaluation exists for LLM performance, biases, and user perception in this field.
Purpose Of The Study
- To evaluate the performance and biases of publicly available AI tools in answering hematology questions.
- To assess patient willingness to use LLMs for interpreting biological test results.
- To identify limitations and risks associated with LLM use in hematology.
Main Methods
- A panel of 62 hematology questions was posed to nine AI tools.
- Answers were scored by medical biologists on accuracy, clarity, relevance, and tone.
- A perception survey was conducted among 300 patients.
Main Results
- AI tool performance varied widely (19-67/100).
- OpenAI models improved but still exhibited clinical biases and hallucinations.
- Two-thirds of familiar patients would use LLMs for test result interpretation.
Conclusions
- LLMs have educational potential but limitations like lack of references and biased responses.
- Autonomous use poses risks; medical supervision and training are essential.
- LLMs should serve as complementary aids, not replacements for medical reasoning.
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