A study analyzed the inherent bias in large language models (LLMs) by simulating physicians making life-and-death decisions in resource-limited settings. Thirteen questions were created to test if LLMs, such as OpenAI's GPT-4, would exhibit bias in clinical decision-making. The simulation involved 1000 iterations per question, with each iteration representing a unique physician and patient. The results showed that simulated physicians consistently favored patients with similar demographic characteristics, including race, gender, age, political affiliation, and sexual orientation. For example, male physicians tended to favor male, White, and young patients, while female physicians preferred female, young, and White patients. Democratic physicians favored Black and female patients, while Republicans preferred White and male patients. Heterosexual physicians favored heterosexual patients over gay/lesbian patients. The study found that publicly available LLMs demonstrate significant biases that could negatively impact patient outcomes if used in clinical decision-making without precautions. The findings highlight the need to address implicit bias in AI systems and to ensure that LLMs are used responsibly in healthcare. The study also emphasizes the importance of understanding how biases in training data and societal biases interact to affect clinical decisions. The results suggest that future research should focus on improving the fairness and accuracy of LLMs through better prompt engineering and more transparent AI systems.A study analyzed the inherent bias in large language models (LLMs) by simulating physicians making life-and-death decisions in resource-limited settings. Thirteen questions were created to test if LLMs, such as OpenAI's GPT-4, would exhibit bias in clinical decision-making. The simulation involved 1000 iterations per question, with each iteration representing a unique physician and patient. The results showed that simulated physicians consistently favored patients with similar demographic characteristics, including race, gender, age, political affiliation, and sexual orientation. For example, male physicians tended to favor male, White, and young patients, while female physicians preferred female, young, and White patients. Democratic physicians favored Black and female patients, while Republicans preferred White and male patients. Heterosexual physicians favored heterosexual patients over gay/lesbian patients. The study found that publicly available LLMs demonstrate significant biases that could negatively impact patient outcomes if used in clinical decision-making without precautions. The findings highlight the need to address implicit bias in AI systems and to ensure that LLMs are used responsibly in healthcare. The study also emphasizes the importance of understanding how biases in training data and societal biases interact to affect clinical decisions. The results suggest that future research should focus on improving the fairness and accuracy of LLMs through better prompt engineering and more transparent AI systems.