AI for all: Bridging healthcare gaps with equity and accessibility

Editor’s note: This is the final installment of a two-article series, powered by DHI and sponsored by Neurealm, looking at how AI-based technology can be leveraged and guided to improve empathy (article 1), equity, and accessibility in healthcare for improved outcomes and experience. 

A patient’s ZIP code, primary language, or access to broadband should not dictate the quality of care they receive — yet systemic disparities in healthcare persist. While digital transformation is often hailed as a catalyst for progress, without deliberate strategy, technology risks widening, rather than closing, these gaps. Artificial intelligence (AI) offers a powerful tool to advance healthcare equity and accessibility, but only when implemented with a conscious focus on fairness, inclusivity, and practical impact.

Defining and addressing equity in healthcare operations

Healthcare equity is not simply about offering the same services to all — it is about ensuring fair access to care, tailored to the needs of diverse populations regardless of race, socioeconomic status, or gender. AI can help by analyzing patterns of inequity in patient treatment, ensuring that underserved groups receive the necessary interventions to close these gaps. Particularly in analytics and decision support, AI is proving instrumental in identifying and addressing disparities in care delivery. By analyzing unstructured data — such as social determinants of health, patient outcomes, and provider availability — AI can highlight inequities in diagnostic rates, treatment access, and resource distribution.

For example, AI tools are reducing biases in clinical decision-making. By analyzing vast datasets, AI can help standardize treatment recommendations, ensuring that patients receive equitable care regardless of demographic factors. Moreover, AI-driven key performance indicators (KPIs) are now being used to monitor equity in healthcare delivery—tracking disparities in diagnostic rates, treatment access, and patient outcomes to identify areas for improvement.

Voice analytics is another promising frontier. AI-driven sentiment and voice analysis can detect stress levels, cognitive impairment, or early signs of neurological disorders by analyzing speech patterns. When applied equitably, this technology enables non-invasive, cost-effective diagnostics that can be deployed in resource-limited settings.

AI is also addressing equity in staff education by helping standardize training and assessment processes. One such example is AI-driven assessment tools leveling the playing field for medical professionals in training. Neurealm developed a GenAI-powered Multiple-Choice Question (MCQ) generator for a large U.S. cardiovascular medical association to streamline and standardize certification exams. By automating the creation of high-quality, unbiased exam questions, this solution not only reduced human error but also ensured consistency in assessment difficulty. As a result, healthcare professionals, regardless of geography or institutional resources—have a fairer pathway to certification and career advancement.

AI’s role in expanding accessibility

Beyond equity, accessibility remains a significant challenge, particularly for rural and underserved communities. AI-driven innovations are addressing barriers related to infrastructure, language, and real-time care access.

Closing the digital divide

The digital divide, characterized by unequal access to technology and digital literacy, poses a significant challenge to equitable healthcare delivery. Many underserved areas lack high-speed internet, limiting access to telehealth and remote monitoring solutions. AI developers are responding with tools designed for low-bandwidth environments, enabling predictive analytics and automated decision support even in constrained settings.

Access to resources

AI-driven predictive analytics are helping healthcare providers optimize resource allocation by forecasting patient needs and ensuring that staff, equipment, and facilities are distributed efficiently. AI models can analyze historical and real-time data to identify high-demand periods, allocate personnel effectively, and ensure that underserved communities receive the necessary resources to meet patient care demands.

AI-enabled wearable technology for remote monitoring

Innovative AI-powered medical devices are transforming patient monitoring, particularly for those with chronic conditions who struggle with access to consistent care. Neurealm collaborated with a medical device startup to develop a smart wearable and implantable sensor system that continuously tracks key health parameters — such as body temperature, oxygen saturation, and pulse rate. The device synchronizes with mobile and cloud-based applications, allowing clinicians to monitor patient health in real-time. This solution not only improves accessibility by reducing hospital visits but also provides early warnings of potential complications, ensuring timely intervention for at-risk patients.

Urgent and emergency care

AI-powered tools are enhancing emergency response by identifying at-risk patients before critical situations arise. Predictive models can analyze patient records, wearable data, and social determinants of health to flag individuals at high risk of severe health events, such as strokes or heart attacks. These insights allow healthcare providers to intervene earlier, reducing emergency department overcrowding and improving patient outcomes.

Overcoming language barriers with AI

Linguistic diversity remains a hurdle in equitable healthcare delivery. Many patients face difficulties communicating with providers, leading to misdiagnoses or poor adherence to treatment plans. AI-powered natural language processing (NLP) solutions are now enabling real-time, multilingual communication, ensuring that patients can interact with healthcare professionals in their preferred language.

Ethical considerations and challenges

While AI holds promise in promoting equity, it is not without challenges. Bias in AI algorithms remains a concern, as training data often reflects systemic disparities present in traditional healthcare records. Without careful oversight, AI models can perpetuate, rather than mitigate, bias. Strategies such as diverse dataset inclusion, regular audits, and transparent AI governance frameworks are critical to ensuring ethical AI deployment.

The use of AI in healthcare often involves the collection and analysis of sensitive patient data. It is essential to safeguard this data and ensure that it is used responsibly and ethically. This is particularly important for underserved populations who may already face systemic vulnerabilities and may be disproportionately affected by data breaches or privacy violations.

As AI plays an increasingly prominent role in healthcare decision-making, questions of liability and governance become more complex. It is essential to establish clear frameworks for accountability and ensure that AI systems are used in a transparent and responsible manner.

Moving toward inclusive AI in healthcare

AI’s potential to advance healthcare equity and accessibility is immense — but only if developed and deployed with intentionality. From improving fairness in professional assessments to enabling real-time remote patient monitoring, AI is already demonstrating its value.

AI is reshaping proactive healthcare by identifying gaps in care and ensuring timely interventions. AI-driven systems can detect missed vaccinations, overdue chronic condition follow-ups, or gaps in preventative screenings and notify both patients and providers. By leveraging real-time data from wearable devices and patient records, AI ensures continuous care and reduces the likelihood of preventable health complications.

To ensure this progress benefits all patients, healthcare organizations must prioritize inclusivity, mitigate bias, and design AI systems that work for diverse populations, not just the digitally connected. By embracing AI with a focus on equity and accessibility, healthcare leaders can help bridge the gaps that have long defined the system, making quality care truly universal.

About Neurealm

Neurealm (formerly GS Lab | GAVS) is the right-sized partner for Engineering, Modernization, and RunOps, blending human intelligence with the latest technologies to help businesses across industries such as Healthcare, Technology, and others, make smart progress.

With offerings in Digital Platform Engineering, Data, AI, Cybersecurity, and Technology Operations, and delivery centers in India and the US, we empower 250+ global enterprises. Driven by an engineering mindset and powered by Neurealm Labs—our innovation engine—we transform ideas into real-world impact through new-age offerings, solutions, frameworks, and accelerators. Our strong technology alliances and academic partnerships further power the future-ready ecosystems we build for our clients.