AI in Public Health: Promise or Peril?
Artificial intelligence (AI) is rapidly reshaping the landscape of public health, promising not only rapid analysis of large data sets, but the ability to automate routine tasks through chat bots and email replies. The integration of AI in public health spaces opens the doors to new opportunities while also introducing an unknown terrain.
In order to discuss the pros and cons of AI in healthcare, we must first define what AI is. Contrary to many people’s belief, AI does not always mean large language models like ChatGPT. While these programs are what many attribute AI to, it is only a small category of AI applications. As defined by the World Health Organization (WHO), AI is a computer that has the ability to perform tasks that are associated with a human being.
Public health organizations heavily rely on the analysis and collection of large data sets, which is time consuming and prone to human error, even in the hands of the most trained biostatisticians. Through AI usage, this process can be done in mere seconds, quickly identifying potential breakouts such as those in the COVID-19 pandemic. This data can then be analyzed with AI and applied through resource allocation or tailored community messages.
In the field of behavioral epidemiology, AI can take data that is already available through social media and mobile applications to track the health behaviors of various groups. This data can then be used to evaluate the effectiveness of already existing interventions. Machine learning algorithms are able to extract people’s beliefs from their social media interactions and this capability has been applied to various mental health applications.
In order to discuss the pros and cons of AI in healthcare, we must first define what AI is. Contrary to many people’s belief, AI does not always mean large language models like ChatGPT. While these programs are what many attribute AI to, it is only a small category of AI applications. As defined by the World Health Organization (WHO), AI is a computer that has the ability to perform tasks that are associated with a human being.
Public health organizations heavily rely on the analysis and collection of large data sets, which is time consuming and prone to human error, even in the hands of the most trained biostatisticians. Through AI usage, this process can be done in mere seconds, quickly identifying potential breakouts such as those in the COVID-19 pandemic. This data can then be analyzed with AI and applied through resource allocation or tailored community messages.
In the field of behavioral epidemiology, AI can take data that is already available through social media and mobile applications to track the health behaviors of various groups. This data can then be used to evaluate the effectiveness of already existing interventions. Machine learning algorithms are able to extract people’s beliefs from their social media interactions and this capability has been applied to various mental health applications.
Acceptance and collaboration with AI is the key to public health advancements.
Image Source: Ordered Chaos
However, the field of public health faces many challenges when it comes to the application of AI. AI cannot function independently of humans, which introduces the problem of algorithmic bias, a clear reflection of inequalities embedded in the data AI is trained on. Machine learning models rely on existing information, much of which originates from historical research that includes racial, gender, and socioeconomic biases. If AI tools are trained on biased and inaccurate data they risk reinforcing existing health disparities.
Another major concern when it comes to AI usage is data privacy. While the data used by AI generates valuable information, it also raises questions about consent and confidentiality. Unauthorized data collection or breaches can compromise individuals’ privacy and erode trust in the public health system. In low resource communities and emergency situations, data regulations are put on the back burner in order to address the problem at hand. This creates ethical dilemmas about balancing the public good with individuals’ right to privacy.
Artificial Intelligence has the chance to transform the public health world by improving efficiency, accuracy, and responsiveness. However, these benefits cannot be appreciated without realizing the issues of bias, equity, and data sharing. The integration of AI in health spaces is not only a matter of technological innovation, but also one of ethical regulation. This has been seen in the European Union’s 2024 Artificial Intelligence Act, as well as an ethical guideline issued by the WHO. By ensuring that AI tools serve all communities fairly and securely, public health institutions can harness the potential of AI to promote a healthier and equitable future.
Another major concern when it comes to AI usage is data privacy. While the data used by AI generates valuable information, it also raises questions about consent and confidentiality. Unauthorized data collection or breaches can compromise individuals’ privacy and erode trust in the public health system. In low resource communities and emergency situations, data regulations are put on the back burner in order to address the problem at hand. This creates ethical dilemmas about balancing the public good with individuals’ right to privacy.
Artificial Intelligence has the chance to transform the public health world by improving efficiency, accuracy, and responsiveness. However, these benefits cannot be appreciated without realizing the issues of bias, equity, and data sharing. The integration of AI in health spaces is not only a matter of technological innovation, but also one of ethical regulation. This has been seen in the European Union’s 2024 Artificial Intelligence Act, as well as an ethical guideline issued by the WHO. By ensuring that AI tools serve all communities fairly and securely, public health institutions can harness the potential of AI to promote a healthier and equitable future.
Featured Image Source: Yamu Jay
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