Artificial Intelligence. Telemedicine. Digital Therapeutics. Virtual Reality. Bioprinting. These are all buzzwords now within the field of medicine and health research that fuel universities and hospitals alike in a race to be the “first” to make waves.
They label it the “New Era” but labeling it as such implies there is an “Old Era” where people are stuck behind the curtain of health inequity and digital health.
So, we must ask ourselves who does this technology benefit. Is it the richest country? Is it our city? Or is it to satisfy annual hospital rankings and stakeholders?
Johns Hopkins University, the University of California, San Francisco, Duke University, and the University of Pennsylvania are just a few of the universities that lead the rankings in the most funding received from the National Institutes of Health (NIH). These four universities in total received over $3 billion in 2022.
All touted as behemoths in the medical and health research space, they all have spent this past year attempting to enter the AI and health tech space to rapidly innovate and transform healthcare. Take for example UCSF, who spent their Annual State of the University Address speaking about leading AI’s “New Era” and how they aim to recenter “human touch” and invest in infrastructure for breakthroughs in a global manner.
To me, these aims, along with similar notions made by other universities, fail to mention who is at risk or who is to benefit. Largely, this vague language captures the public but is difficult to parse out those who might be severely affected by this new health technology.
AI and other health technologies appear to be ill-equipped to address health inequities as machine learning doesn’t factor in the social and cultural determinants of health on a global scale. Thus, this praised “new era” could even further inequities in healthcare throughout the world.
Take, for instance, less developed countries where these new digital innovations are essentially non-accessible. There are issues with lack of infrastructure, lack of resources, less technical skills, inconsistencies in bandwidth and energy supply, and a government’s stress on political issues rather than digital development.
So where does UCSF, John Hopkins, Duke, etc. fit into the paradigm of healthcare in these countries? Essentially, the development of these new technologies is not sustainable in any country that doesn’t have a top-tier hospital and medical infrastructure. Or any country that does not have stable internet access.
This health “gap” in a generation is widened by systematic differences in accessing care between different groups of people. To close this gap, a major aim should be tackling the inequitable distribution of resources, money, and power within a country and on a global scale.
This includes the development of new healthcare technology where action must be needed to make sure there is equitable distribution of these new technologies to ensure a fairer and safer world.
However, there is little to be said about how these universities and global institutions are attempting to take coherent action to minimize this health “gap”.
Artificial intelligence, to some, is an essential aspect of the ever-evolving healthcare landscape and thus professionals need to evolve with it. It can improve efficiencies in healthcare systems and reduce harm produced by human errors.
With overburdened health systems globally, machine learning could serve to reform the global health system by increasing access and equity.
However, this notion still doesn’t address the inequalities that digital developments could further in less developed settings. The impact of these developments could widen gaps if social determinants of health aren’t appropriately addressed for each setting.
As institutions and companies continue to invest in health technology, they need to acknowledge the most marginalized communities that may be disproportionately impacted by technology designed for high-income countries.
In conjunction with investment in these technologies should be working towards the goal of being more structurally competent. This includes training professionals to discern how various issues like clinical symptoms, diseases, etc. are the outcome of upstream decisions like health care and delivery systems, zoning laws, infrastructure, medicalization, and illness and death.
These networks shape how health is delivered and how the health outcomes of the most marginalized communities.
By restructuring our health system to be more equitable, we can then work towards building new technologies, training professionals, and building infrastructure that supports every person in a community.
Compromise is key to health technology or rather a synergy between understanding social determinants of health and building technology around it. Without being structurally competent, the gap in health care systems will be furthered and inequities will continue to persist.
The advancement is impressive, but it needs to not racialize or divide communities when implemented. With these concepts in mind, hopefully, there is a future where AI and medical technology will improve health outcomes globally.
