Warning Signs of Bias: AI Medical Tools Downplay Symptoms of Women and Ethnic Minorities
Artificial intelligence tools used by doctors worldwide risk exacerbating health disparities as they often downplay symptoms of women and ethnic minorities, according to a growing body of research. A series of recent studies have found that the adoption of large language models (LLMs) across the healthcare sector can lead to biased medical decisions, perpetuating existing patterns of under-treatment in Western societies.
The studies conducted by researchers at top US and UK universities reveal that LLMs, such as Gemini and ChatGPT, tend to underestimate the severity of symptoms among female patients. Furthermore, they display less empathy towards Black and Asian individuals seeking medical attention for mental health issues. These findings have significant implications for the development and deployment of AI tools in healthcare.
The Origins of Bias
One major contributor to this issue is the data used to train LLMs. General-purpose models like GPT-4, Llama, and Gemini are trained on vast amounts of internet data, which inevitably reflect existing biases. Additionally, developers can inadvertently perpetuate these biases by adding safeguards after the model has been trained.
The potential consequences of biased AI tools in healthcare are far-reaching. Researchers have warned that they can reinforce patterns of under-treatment, particularly for women’s health issues, which often face chronic underfunding and research. A study last year found that GPT-4 failed to account for demographic diversity in medical conditions, tending to stereotype certain races, ethnicities, and genders.
The Impact on Patients
The impact of biased AI tools on patients can be severe. For instance, a recent study showed that patients whose messages contained typos or informal language were between 7-9 percent more likely to be advised against seeking medical care by LLMs used in a medical setting. This could result in individuals who are not fluent in English or uncomfortable using technology being unfairly treated.
The problem of biased AI tools is complex and multifaceted. It requires careful consideration of the data used to train these models, as well as the potential consequences for patients. Experts have suggested that one way to reduce medical bias in AI is to identify what datasets should not be used for training in the first place and then train on diverse and more representative health data sets.
The Role of Developers
Developers play a crucial role in addressing this issue. Open Evidence, an AI medical information start-up used by 400,000 doctors in the US, has implemented measures to reduce bias in its models. The company trains its models on medical journals, FDA labels, health guidelines, and expert reviews. Every AI output is also backed up with a citation to a source.
Other developers are working to address this issue as well. For example, researchers at University College London and King’s College London have partnered with the UK’s NHS to build a generative AI model called Foresight. This model was trained on anonymized patient data from 57 million people, allowing it to represent a diverse range of demographics and diseases.
The Way Forward
While there are challenges ahead, researchers remain optimistic about the potential benefits of AI in healthcare. "My hope is that we will start to refocus models in health on addressing crucial health gaps, not adding an extra percent to task performance that doctors are honestly pretty good at anyway," said Marzyeh Ghassemi, associate professor at MIT’s Jameel Clinic.
To ensure that AI tools do not perpetuate existing biases, developers must prioritize transparency and accountability. This includes identifying and addressing potential biases in the data used to train LLMs and implementing safeguards to prevent their propagation. By working together, we can harness the power of AI to improve healthcare outcomes for all patients, regardless of their background or demographics.
Conclusion
The use of AI tools in healthcare has the potential to revolutionize patient care, but it also poses significant risks if not addressed properly. The findings outlined above highlight the need for a concerted effort from developers, researchers, and policymakers to address bias in AI models. By prioritizing transparency, accountability, and diverse data sets, we can create AI tools that truly benefit all patients, without exacerbating existing health disparities.
The use of AI tools in healthcare has become increasingly widespread, with many hospitals and doctors relying on LLMs like Gemini and ChatGPT to auto-generate transcripts of patient visits, highlight medically relevant details, and create clinical summaries. However, a growing body of research suggests that these tools may perpetuate existing biases and exacerbate health disparities.
The Need for Diverse Data Sets
One major contributor to this issue is the data used to train LLMs. General-purpose models like GPT-4, Llama, and Gemini are trained on vast amounts of internet data, which inevitably reflect existing biases. Additionally, developers can inadvertently perpetuate these biases by adding safeguards after the model has been trained.
The potential consequences of biased AI tools in healthcare are far-reaching. Researchers have warned that they can reinforce patterns of under-treatment, particularly for women’s health issues, which often face chronic underfunding and research.
The Impact on Patients
The impact of biased AI tools on patients can be severe. For instance, a recent study showed that patients whose messages contained typos or informal language were between 7-9 percent more likely to be advised against seeking medical care by LLMs used in a medical setting. This could result in individuals who are not fluent in English or uncomfortable using technology being unfairly treated.
The Role of Developers
Developers play a crucial role in addressing this issue. Open Evidence, an AI medical information start-up used by 400,000 doctors in the US, has implemented measures to reduce bias in its models. The company trains its models on medical journals, FDA labels, health guidelines, and expert reviews.
Other developers are working to address this issue as well. For example, researchers at University College London and King’s College London have partnered with the UK’s NHS to build a generative AI model called Foresight. This model was trained on anonymized patient data from 57 million people, allowing it to represent a diverse range of demographics and diseases.
The Way Forward
While there are challenges ahead, researchers remain optimistic about the potential benefits of AI in healthcare. "My hope is that we will start to refocus models in health on addressing crucial health gaps, not adding an extra percent to task performance that doctors are honestly pretty good at anyway," said Marzyeh Ghassemi, associate professor at MIT’s Jameel Clinic.
To ensure that AI tools do not perpetuate existing biases, developers must prioritize transparency and accountability. This includes identifying and addressing potential biases in the data used to train LLMs and implementing safeguards to prevent their propagation. By working together, we can harness the power of AI to improve healthcare outcomes for all patients, regardless of their background or demographics.
Conclusion
The use of AI tools in healthcare has the potential to revolutionize patient care, but it also poses significant risks if not addressed properly. The findings outlined above highlight the need for a concerted effort from developers, researchers, and policymakers to address bias in AI models. By prioritizing transparency, accountability, and diverse data sets, we can create AI tools that truly benefit all patients, without exacerbating existing health disparities.
The use of AI tools in healthcare has become increasingly widespread, with many hospitals and doctors relying on LLMs like Gemini and ChatGPT to auto-generate transcripts of patient visits, highlight medically relevant details, and create clinical summaries. However, a growing body of research suggests that these tools may perpetuate existing biases and exacerbate health disparities.
The Need for Diverse Data Sets
One major contributor to this issue is the data used to train LLMs. General-purpose models like GPT-4, Llama, and Gemini are trained on vast amounts of internet data, which inevitably reflect existing biases. Additionally, developers can inadvertently perpetuate these biases by adding safeguards after the model has been trained.
The potential consequences of biased AI tools in healthcare are far-reaching. Researchers have warned that they can reinforce patterns of under-treatment, particularly for women’s health issues, which often face chronic underfunding and research.
The Impact on Patients
The impact of biased AI tools on patients can be severe. For instance, a recent study showed that patients whose messages contained typos or informal language were between 7-9 percent more likely to be advised against seeking medical care by LLMs used in a medical setting. This could result in individuals who are not fluent in English or uncomfortable using technology being unfairly treated.
The Role of Developers
Developers play a crucial role in addressing this issue. Open Evidence, an AI medical information start-up used by 400,000 doctors in the US, has implemented measures to reduce bias in its models. The company trains its models on medical journals, FDA labels, health guidelines, and expert reviews.
Other developers are working to address this issue as well. For example, researchers at University College London and King’s College London have partnered with the UK’s NHS to build a generative AI model called Foresight. This model was trained on anonymized patient data from 57 million people, allowing it to represent a diverse range of demographics and diseases.
The Way Forward
While there are challenges ahead, researchers remain optimistic about the potential benefits of AI in healthcare. "My hope is that we will start to refocus models in health on addressing crucial health gaps, not adding an extra percent to task performance that doctors are honestly pretty good at anyway," said Marzyeh Ghassemi, associate professor at MIT’s Jameel Clinic.
To ensure that AI tools do not perpetuate existing biases, developers must prioritize transparency and accountability. This includes identifying and addressing potential biases in the data used to train LLMs and implementing safeguards to prevent their propagation. By working together, we can harness the power of AI to improve healthcare outcomes for all patients, regardless of their background or demographics.
Conclusion
The use of AI tools in healthcare has the potential to revolutionize patient care, but it also poses significant risks if not addressed properly. The findings outlined above highlight the need for a concerted effort from developers, researchers, and policymakers to address bias in AI models. By prioritizing transparency, accountability, and diverse data sets, we can create AI tools that truly benefit all patients, without exacerbating existing health disparities.