Using Artificial Intelligence To Fight Cancer

As the global population ages, the number of cancer cases is going up. New cancer diagnoses are expected to rise by 70% in the next 2 decades, from 14 million to around 22 million, according to an estimate by the World Health Organisation.  Approximately 70% of deaths from cancer occur in low- and middle-income countries.In sub-Saharan Africa where cervical cancers are the leading cause of female cancer deaths.

Artificial Intelligence (AI) can help doctors make correct treatment decisions, reduce unnecessary surgeries, and help oncologists improve patients' cancer treatment plans.

AI has huge potential for helping scientists manage the mind-boggling complexities of research and other data, to expedite cancer drug discovery and translate scientific findings into real benefits for patients. It can sift through billions of experimental results, identify patterns and make useful predictions about diagnoses, outcomes and responses to treatment.

The founder of Community Healthcare Innovation Lab (CHIL) Artificial Intelligence Group (CHIL AI), Shamim Nabuuma Kaliisa is on a mission to change that. Artificial Intelligence (AI) can manage the use of chemotherapy drugs and predict the tolerance of chemotherapy drugs, so as to optimise the chemotherapy regime.  Having lost her mother to cervical cancer at age 13, and having herself survived breast cancer while in medical school, Nabuuma Kaliisa, now aged 26, decided she wasn’t going to allow other women in rural Uganda to go through the pain of living with cancer, simply because they had no access to screening and testing facilities. 

CHIL AI began by manufacturing cervical and breast cancer self-test kits and through its AI-powered mobile app called Keti, it allowed women to consult with oncology experts, have samples collected and sent to laboratories and have their test results interpreted and advised on what next steps to take. Today, CHIL AI uses the latest technology to offer services including AI guided consultation, Automated Referral, Automated Radiology Reports, Interpretation laboratory and Automated Drug Ordering, all accessed through their conversational chatbot accessible on various mediums. The system also allows disabled patients access to tele-health where this has previously been unavailable. The CHIL AI chatbot allows those with seeing and writing disabilities to have access to and navigate services using only voice prompts. CHIL AI also uses guided e-oncology services and drone-powered transportation to transport cervical cancer specimens from remote rural areas to laboratories. 

Technologies like AI and Machine Learning (ML) can play an important role. This is due to the fact that a large amount of data, which is generated during treatment, can be used to come out with clinically tangible outputs.

In 2019, 70% of all cancer deaths occurred in developing countries. Barriers to early detection include financial resources, distance from testing facilities and even the most basic information on cancer treatments. Many young Ugandan women find they have cancer only when it is in advanced stages and the cancer is untreatable.

AI can be used to predict anticancer drug activity or assist in anticancer drug development. Different cancers and the same drugs may have different reaction modes, and data from high-throughput screening procedures often reveal the relationship between genomic variability of cancer cells and drug activity. The pharmaceuticals firm GSK has recently struck a five-year partnership with King’s College London to use AI to develop personalised treatments for cancer by investigating the role played by genetics in the disease. However, to harness the use of this technology in oncology requires efforts which are collaborative and multidisciplinary and which overarch computer science and medicine. This collaboration involves multiple industries gaining equitable access to large volumes of annotated data and conducting the unbiased training of machine-learning algorithms.

The use of any technology also calls for vigilance and accountability. Hence, issues with AI, including scalability, ethical and legal considerations and efficacy need to be addressed. It is imperative to ensure that the data generated is standardised and anonymised. Other obstacles include validation, concerns over privacy and the knowledge gap between clinical and data science experts.

The exponential growth of AI in the last decade is evidenced to be the potential platform for optimal decision-making by super-intelligence, where the human mind is limited to process huge data in a narrow time range. Cancer is a complex and multifaceted disorder with thousands of genetic and epigenetic variations. AI-based algorithms hold great promise to pave the way to identify these genetic mutations and aberrant protein interactions at a very early stage.

WHO:     Science Direct:    Independent:     WEF:    CBInsights:   Guardian:     Economist:     BioMediaclCentral:

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