Artificial Intelligence (AI) has the potential to revolutionize healthcare, particularly in addressing stroke disparities and providing personalized care. However, concerns about bias and limited access must be acknowledged. With stroke being a leading cause of death and disability worldwide and a projected shortage of physicians in the coming years, AI in healthcare can play a crucial role in bridging the gap between providers and patients. Still, its implementation requires careful consideration to ensure equitable outcomes for underserved communities.
AI, particularly Machine Learning (ML), enables data-driven approaches that offer personalized and precise care to patients based on their situations. By analyzing vast amounts of data, AI can identify non-obvious factors contributing to health inequities, such as zip codes, physical activity levels, doctor’s visit frequency, and proximity to emergency responders, which may impact timely access to care.
Addressing stroke disparities
Strokes disproportionately affect Black patients, leading to severe disability. Data shows that Black patients are less likely to receive specialized treatments, such as clot-busting medication and mechanical thrombectomy, even when receiving care at comparable facilities to their white counterparts. AI can aid underserved hospitals in detecting, triaging, and predicting stroke outcomes, facilitating timely transfers to appropriate facilities for specialized treatment.
Rapid stroke detection and treatment
Regarding strokes, swift action is critical to preserve function and potentially reverse deficits. AI can be instrumental in rapidly detecting strokes and large vessel occlusions. By incorporating AI into stroke protocols, emergency teams can make quicker decisions regarding treatment, potentially leading to better outcomes. The technology also enables the instant sharing of brain scan images with the stroke team, facilitating prompt evaluations and treatment decisions.
Time-sensitive treatment information
AI may also determine the last time a patient was known to be well, which is crucial for certain time-sensitive treatments. This information is particularly valuable for patients living alone, as they may lack someone to provide this critical piece of information.
Challenges of access and bias
Despite the positive impact of AI in healthcare, not all patients have access to the technology. Financial constraints can limit access to smart devices and AI-enabled tools, potentially creating disparities in health outcomes. Moreover, biased data and algorithms rooted in systems of oppression can further exacerbate existing inequities.
Ensuring equitable AI implementation
To address these challenges, AI models must be trained on diverse datasets, and members of underserved communities should be actively involved in creating algorithms. Oversight is crucial to promote equitable algorithms that prioritize patient privacy and data security. By involving diverse representatives in AI development and monitoring, we can harness AI’s potential to narrow the gap in access to care faced by Black and Brown communities.
AI as a tool, not a replacement
It is essential to clarify that AI does not replace medical providers or make clinical decisions. Instead, it empowers healthcare professionals to make rapid diagnoses and deliver timely, definitive treatments. In the case of the patient mentioned earlier, AI provided critical information that the medical team lacked, leading to life-saving management changes.
AI has immense potential in reducing stroke disparities and improving healthcare outcomes for underserved communities. By leveraging AI’s capabilities to deliver personalized care and rapid stroke detection, we can make significant strides in narrowing the healthcare gap. However, addressing concerns about bias, limited access, and equitable implementation is crucial to ensure AI is integrated into healthcare in a productive and just manner. As we embrace AI in healthcare, we must seize this opportunity to improve the health of our most vulnerable communities.
