Can AI be used to Improve Blood Cancer Treatment?

AI is increasingly transforming healthcare, particularly in the diagnosis and treatment of blood cancers.

Professor Daniel Royston is a haematopathologist at Oxford University Hospitals NHS Trust.

He is pioneering research that employs narrow AI to enhance the diagnosis of myeloproliferative neoplasms (MPNs)—a group of blood cancers characterised by the overproduction of blood cells.

Traditionally, diagnosing MPNs involves multiple tests, including bone marrow biopsies, where specialists examine blood cells under a microscope.

This process requires extensive training, as the differences between healthy and diseased cells can be subtle.

Even among experts, interpretations can vary, leading to potential diagnostic inconsistencies.

Professor Royston’s application of AI aims to mitigate these challenges.

By employing advanced algorithms, AI can objectively analyse biopsy images, identifying minute features that may be overlooked by the human eye.

This approach not only increases diagnostic accuracy but also accelerates the process.

This allows for earlier intervention and personalised treatment plans.

Understanding the impact of these advancements on diverse populations is crucial.

In the UK, South Asians – comprising individuals of Indian, Pakistani, Sri Lankan, and Bangladeshi heritage – represent a significant portion of the population.

Research indicates that cancer incidence patterns can vary between South Asians and non-South Asians.

A study in Leicester found that out of 12,128 cancer cases, 862 (7%) occurred in South Asians, who were generally younger at diagnosis compared to non-South Asians.

While specific data on MPN prevalence among South Asians in the UK is limited, it is essential to consider that genetic, environmental, and lifestyle factors may influence cancer patterns in this demographic.

Therefore, tailored diagnostic approaches are necessary to address potential disparities.

The integration of AI in diagnostics holds particular promise for South Asian communities in the UK.

By providing objective and precise analyses, AI can help overcome potential disparities arising from limited access to specialised healthcare services.

Moreover, AI-driven tools can assist in tailoring treatments to the specific needs of individuals within these populations, considering unique genetic and environmental factors.

As AI technologies advance, it is crucial to ensure their equitable implementation across diverse populations.

Studies have indicated that some AI diagnostic tools may be less accurate in individuals with darker skin tones.

Therefore, it is essential to develop and train AI systems using diverse datasets that represent the UK’s multicultural population, ensuring accurate and fair diagnostics for all.

Professor Royston envisions a future where AI is integral to healthcare, particularly in managing complex diseases like blood cancers.

He emphasises that AI is not a replacement for healthcare professionals but a complementary tool.

By embracing AI, the medical community can improve diagnostic accuracy, personalise treatments, and ultimately improve patient outcomes across diverse populations.

As AI continues to evolve, its responsible and ethical application will be paramount.

Ensuring that these technologies are accessible and beneficial to all demographic groups, including South Asian communities in the UK, will be essential in realising their full potential in global healthcare.