AI Solutions for Equality in Healthcare - The Symbiosis Between Human and Machine
What is AI in Healthcare and Why is it Important?
Artificial intelligence is a vastly growing field within healthcare, and it continues to change the way that patients and practitioners interact with data, treatments and research. Machine learning has allowed for predictive analysis, which has been successfully implemented in order to support emergency medical staff or predicting kidney disease, as well as a large number of other applications. (Read Mobihealthnews’s article on ‘Top 10 Use Cases for AI in Healthcare’ for more information).
AI within healthcare can also have the benefit of eliminating extra human errors or biases when processing patient data. These biases are fundamental issues, often causing inequities in terms of race, ethnicity or even gender. As a result, treatments can become flawed and access to resources impaired due to certain preconceived biased notions.
Health Equity - Benefits and Drawbacks in Relation to AI
Awareness surrounding health equity has grown along with AI in healthcare. MIT Jameel Clinic, for example, hosted an online conference which addressed inequalities and healthcare. Thus, the need for improvement of health equity has grown alongside this newfound awareness.
This is where AI comes in - it provides a solution for inequalities, with the ability to provide “unbiased predictions based only on the impartial analysis of the underlying data” (Mobihealthnews).
Yet, simultaneously complications arise when humans and artificial intelligence intersect - and while some inequities may be eliminated, others can begin to appear. AI tools can be designed with small populations in mind and clinical trials often cannot ensure a fully representative sample of participants.
A study in October 2019 found that an algorithm referred black people who were equally as sick as white people to healthcare improvement far less. “The data showed that the care provided to black people cost an average of US$1,800 less per year than the care given to a white person with the same number of chronic health problems,” states Nature.com. The problem with the algorithm lies within the use of cost prediction and systematic racism.
But how can this be taken into account?
A Clearer Solution - The Symbiosis Between Human and Machine
Researchers at Jameel Clinic say that this roadblock can be averted by having AI tools researched and validated across multiple diverse areas of the world with the intention of being more inclusive.
Other facilities such as Stanford University are also working on projects that aim to combine ML and human resources in order to create higher levels of equality and fairness within healthcare. “With purposeful foresight,” they state, “we can recognize the opportunities for future outcomes while mitigating the risk of unintended consequences”.
AI in healthcare can help us immensely through the objectification of data, but AI doesn’t function on its own. Researchers and whoever programs or inputs the data can have a massive impact on how an algorithm processes its data, which can ultimately lead to negative outcomes for minority patients.
Humans and AI can work together to eliminate biases and create a healthcare system that is equitable, and with the growing effort and awareness, we are on our way to fairer healthcare.
Written by:
Maxine Wesley
LinkedIn
