How Generative AI Can Help Bridge the Gap in Health Equity
The application of generative AI in healthcare will play a major role in mitigating health disparities that affect historically disadvantaged populations who are most likely to have limited access to healthcare. Health disparities are defined as differences in health risk factors, including diseases, access to care, life expectancy, and other health indicators, among distinct populations and communities. Research indicates that health disparities are amplified among persons living in poverty and in rural communities. Individuals living in low-income, rural communities are often the ones with the least access to adequate healthcare resources and are the ones with the worst health outcomes and who live the longest with the highest burden of disease compared with their urban and higher-income counterparts.
Generative AI which is a type of artificial intelligence that can create new content, designs, and solutions based on existing data, can be used to combat these disparities. By looking for commonalities and special needs across different populations, generative AI can then create tailored healthcare solutions for those groups. Many current healthcare models adopt a more ‘one-size-fits-all’ approach, but generative AI has the potential to overcome these disparities, helping to avoid or reduce inequalities.
One example is designing health interventions specially tailored to individual preferences and cultural and linguistic contexts. AI can be trained to help care providers, communities and patients design culturally and linguistically informed, personalized health education that has the potential of resulting in more effective public health campaigns. For example, generative AI can review a community’s preferences, beliefs and health behaviors, and then design content that speaks directly to them – all ultimately improving the effectiveness of health education.
Generative AI also plays a crucial role in improving access to care. A common problem with providing healthcare to people in underserved areas is lack of access. AI-driven tools can help providers optimize delivery to patients by developing systems tailored to the needs of specific communities. For instance, Generative AI can help to create telemedicine platforms that operate effectively for rural populations. Generative AI driven solutions can also help in creating new healthcare models, like mobile clinics or virtual consultations, that extend healthcare services to those who might otherwise struggle to access them.
Furthermore, generative AI can improve health outcomes with predictive pre-care, allowing medical professionals to intervene before it’s too late. With technology that analyzes health-risk data from a given community, generative AI can help healthcare providers offer preventive care that specifically caters to the identified risks. This type of anticipatory and responsive generative-AI technology helps to equip healthcare professionals with the perspective and insight to assist patients in managing the best possible health outcomes, ultimately reducing health disparities within the community.
Yet, the potential of generative AI for public health also raises difficult questions. We need to understand how to ensure that AI models aren’t biased in the first place and perpetuating existing inequalities. We also need to tackle the ethical questions about the use of people’s data responsibly, and balancing privacy concerns – for example, who should have access to a model that can generate case histories of ovarian cancer? Finally, we must work with technologists, providers, and the wider communities they serve to design and implement risk assessments that are both responsible and just. With the right collaborative mindset and engagement among all these stakeholders, we can embrace generative AI as an exciting tool that can truly help us ‘level the playing field’.
In conclusion, generative AI will enable us to tackle health equity by providing individualized solutions and improving access to care for disadvantaged communities. By enabling us to develop bespoke interventions and deliver better healthcare, generative AI brings the possibility of a healthcare system where everyone – irrespective of their background and geographical location – can have access to high-quality healthcare. With continued development of such approaches, it may in the future be possible to have equality in healthcare for all.
