AI should make healthcare fairer, not faster

 

Letter to Editor

ARTIFICIAL Intelligence is quickly becoming part of everyday healthcare. From faster diagnoses to automated appointment systems, AI is often praised for making hospitals run more efficiently.

While speed is important, it should not be the main goal because we can see in healthcare system, it is already struggle with inequality including Malaysia’s.

Thus, AI must focus on something more meaningful and fairness. Technology should not only help patients get treated faster but also needs to ensure that no one is left behind.

Even though making healthcare faster often improves convenience, it does not automatically make care better or more equal. Faster systems usually benefit patients who already have good access to hospitals, specialists and digital tools.

Urban hospitals and private healthcare providers are more likely to adopt AI quickly, while rural clinics and public hospitals may struggle to keep up. When speed becomes the priority, AI risks serving the few instead of the many.

On the other hand, fairness means ensuring that everyone receives appropriate care regardless of where they live, how much they earn or how familiar they are with technology.

In Malaysia, healthcare inequality remains a real concern and gives a clear example of why fairness must guide innovation. For instance, specialists like cardiologists, oncologists and orthopaedic surgeons are heavily located in major cities which makes it difficult for rural patients to receive timely care. 

While public hospitals are heavily subsidized and more affordable, it often faces overcrowding and long waiting times, whereas we can see private hospitals have faster and more comfortable service but at higher costs.

However, the vulnerable groups which include the elderly, people with disabilities, migrant workers and low-income communities, may face additional barriers from transportation challenges to digital illiteracy.

These inequalities show that efficiency alone cannot address systemic disparities. AI must be used to close these gaps, not widen them.

However, it can be seen that AI systems are designed to improve speed but unintentionally make inequalities worse. First, biased datasets can perpetuate inequities because many AI tools are trained using existing healthcare data which may not fully represent rural populations or marginalized communities. 

This can lead to biased outcomes that are delivered quickly but unfairly. If such systems are deployed with minimal human oversight, biased recommendations can be delivered rapidly and at scale which potentially leads to misdiagnoses or delayed care for those who are already underserved. 

Second, unequal access to technology can limit AI’s benefits. It could be seen that many applications rely on internet connectivity, smartphones or electronic health records which are more readily available in urban or private settings.

Without these resources, patients and clinics may be excluded which means could widening the gap in care. Third, over reliance on automation can also reduce clinician oversight. The speed driven AI may prioritize workflow efficiency over personalized judgment which it is critical for patients with atypical or complex conditions.

When speed is prioritized over understanding, patient care can suffer. AI must be designed with fairness at its core and ethical in order to avoid these pitfalls. AI systems should work well for diverse populations and be tested across different settings including rural, low income and digitally disadvantaged patients.

Plus, transparency is needed to allow clinicians and patients to understand AI recommendations and limitations. Ethical design helps reduce bias, protects patient privacy and ensures that vulnerable groups are not treated unfairly. 

Most importantly, AI should support doctors and nurses, not to replace them so that the judgment, empathy and context remain central to patient care. In Malaysia, this approach is practical and achievable for instance the AI assisted screening can help the rural clinics to detect high risk patients earlier.

Not only that, AI supported telemedicine can guide patients who live far from hospitals and predictive tools can help hospitals plan staff and resources more effectively to meet patient needs.

Responsibility does not lie with developers alone. Engineers and data scientists play a crucial role in ensuring that AI systems are safe, reliable and carefully tested to minimize bias.

Along with that, hospitals also important to set up review processes before using AI tools, training all the healthcare workers to use them responsibly and make sure the technology does not favor certain groups of patients over others. 

Not only that, the policymakers and regulators are also equally important as they establish ethical guidelines, technical standards and independent oversight to ensure patient safety and fairness. Together with it, these groups can form a shared system of responsibility that covers every stage of AI development and use.

For this reason, close collaboration between engineers, clinicians and policymakers is important. Engineers contribute technical knowledge, clinicians bring real world understanding of patient care and policymakers create systems that protect ethics and equity.

By working together from the outset, they can design AI systems that serve all patients which do not matter urban and rural, rich and poor, young and old rather than focus solely on efficiency or productivity.

Success should not be measured by how fast patients move through the system but by how fair care is delivered. AI should support equity not just efficiency and its true value lie in improving outcomes for all patients.

In conclusion, AI has great potential to improve healthcare, but speed alone is not enough. In Malaysia and similar healthcare systems which placing fairness at the centre of AI development that help reduce inequality, build public trust and ensure that innovation benefits everyone.

By embedding inclusivity, transparency, ethical design and patient centred care into AI and by fostering interdisciplinary collaboration, we can ensure that AI improves healthcare not only efficiently, but equitably. Fairness must not only speed but should guide the future of healthcare innovation. 

Alya Khadiejah Mohamad Adry is a final year student of biomedical engineering at Faculty of Engineering, Universiti Malaya. 

The views expressed are solely of the author and do not necessarily reflect those of  MMKtT.

- Focus Malaysia.