Amongst the many difficulties of COVID-19 pandemic that hit the world, as an oncologist, I feel cancer screening and early detection was most impacted and has left a visible impact with more patients battling advanced stage of the disease than in the years before.
When cancer care is delayed or inaccessible there is a lower chance of survival, greater problems associated with treatment, and higher costs of care. It’s well established that early diagnosis improves cancer outcomes and is an important public health strategy in all settings.
AI can transform cancer care
However, the interrupted cancer screening service has taught us some interesting lessons, some of which may change the cancer care landscape in the future. One such area of impetus and growth was the integration of Artificial intelligence (AI) in cancer detection, screening, and treatment. AI, if applied well, has the potential to affect several facets of cancer care, certainly making it more accessible, reasonable, and possibly more accurate.
The American National Cancer Institute also endorsees this viewpoint.
Interestingly, while the past few decades have seen an increase in polarity and a disruption of socio-economic structures caused by healthcare cost and delivery issues, these decades have also seen a huge change in the globalisation and equalisation of the IT and internet sector. From aviation to retail, and elections to rock concerts, we have seen digitalisation as a central and unifying theme.
The gaps between different countries in technology are far smaller than in the past, and there has been an amalgamation of talents globally in each field required to develop and run these technologies. In these ways, digitalisation has strengthened our global socio-cultural structure and has broken down many of the traditional hierarchies and geo-social segregations.
India remains one of the world’s largest users of data and mobile phones, way ahead of many developed nations. The question that thus emerges is if we can harness this strength into something a lot more meaningful including early diagnosis and treatment of cancer. If applied well AI can be put to use in all aspects of cancer detection, diagnosis, and treatment.
AI-based solutions are economical
We know cancer is a complex disease with thousands of genetic and molecular variations adding to its myriad presentations. AI-based algorithms thus hold great promise to pave the way to identify these variations and personalize care. Of course, AI-based methods are less costly and faster thus endorsing their usability.
But the problem remains in finding the right solution. We are also aware that the usability of AI is dependent on big data and its robustness and granularity is the only way to make AI usable and functional. This is far from easy and requires advocacy and policy change. It may, hopefully, and however, be also solved with disruptive innovation. Time will tell.
In the meantime, I propose
- Start with a consortium of thinkers, philanthropists, and innovators in the West developing an open-source data capture for the east.
- Open-source data collection - prompting patients to store data to help others and providing a platform for assimilation of complex data in a seamless and organised fashion
- Promoting talent search amongst the millions of young biology graduates by structuring their goals towards common incentives
- Utilizing the mobile number as a unique health identity, and then following the footsteps of health and disease with a constant focus on the affordability of prevention and cure
- Continuously strengthening AI, with highly variable presentations, approaches, treatments and affordability
- Embracing the world of generics and biosimilars but with the caveat of data as royalty
- Conducting phase 4 studies in millions of patients rather than thousands, by bringing the cost of treatment closer to the marginal resource costs of pharmaceutical companies costs, and leveraging the results against the strength of evidence and in the process deepening the understanding of open source AI
- Availing philanthropic funds to provide health insurance that enables people to detect cancers early, as earlier diagnosis saves costs and using that large data sets to build diagnostic and treatment algorithms
- Exploring amalgamation of traditional Eastern forms of medicine with present-day allopathic treatments to see if toxicity can be reduced, thereby lowering the additional cost of treatments, again while simultaneously using AI to understand and find a solution to this puzzle
If we are to beat cancer, early detection and diagnosis are arguably the most effective means we have at our disposal. Artificial intelligence and machine learning tools have the potential to change the cancer detection landscape. The question now is if we can embrace innovation and change the landscape. The ball is in your court.
The author is Senior Director, Medical Oncology, Fortis Memorial Research Institute, Gurugram. Views are personal
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