AI and big data to cure disease

Artificial intelligence has the potential to revolutionize the healthcare industry in many aspects; it can be used in areas such as diagnosis, treatment and drug discovery. Although their full benefits are still yet to be realized, they are likely to be the future of healthcare.

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For years, artificial intelligence (AI) may have merely seemed to be a pipe dream; only alive in movies and our imaginations. But as we delve deeper into an age of Siri and Alexa, AI is becoming increasingly commonplace in our day-to-day lives. With big data forming the building blocks for AI technologies, could we be in a position for AI applications to forever change the healthcare industry as we know it?

Potential of AI in healthcare

There exist many varieties of AI technologies, many of which may be applied to healthcare.2 These applications have the potential to change the industry; time, cost and efficiency could all be improved by their introduction.3 For example, the predicted future deficit of 12.9 million healthcare professionals (HCPs) globally by 2035, could be supported/minimized by the application of AI.4 Could these technologies serve to streamline processes, reduce administrative time and support HCP decision-making so as to reduce the impact of this deficit?4

The use of AI in healthcare has been of great interest since the 1970s, when a predictive technology called ‘MYCIN’ was developed as a potential tool to provide therapy recommendations for patients with bacterial infections.2 Despite never being introduced into clinical practice, MYCIN demonstrated a 69% success rate in choosing effective therapy and set a foundation for the future of AI in healthcare.2 Since then, plenty more developments have been in made in the field.

Machine learning (ML) is one of the most commonly used forms of AI and can be especially helpful in healthcare as it can be used to learn, confirm and predict disease through analyses of structured data sets, such as imaging and genetic data.5 One example of ML is neural networks, a technology based on the way in which neurons fire in the brain and that can be used to determine binary queries, for instance a ‘yes or no’ answer to a clinical question.2 As with any technology, ML is constantly progressing and new branches of ML are regularly being developed. Deep learning is one such branch, it uses neural networks with many layers and has potential to benefit the complex world of healthcare.6 The first applications of deep learning were in image analysis and were used to detect Alzheimer’s disease from magnetic resonance images (MRIs).6 Convolutional neural networks (CNNs) have also demonstrated diagnostic capabilities on par with 21 board-certified dermatologists in classifying images of different types of skin cancer from over 130 thousand images.6 Application of deep learning in medical research almost doubled in 2016 alone, showing great promise for the future.5

AI in diagnostics

As of today, AI applications are mainly being used in image analysis.5 AIs have the ability to detect abnormalities that human eyes may not and can assist physicians in their decision-making processes.5 AI algorithms can be developed to learn clinical features from a large volume of existing healthcare data and use this to aid clinical diagnoses, whilst also learning and self-correcting as it is used more and more.5 The greatest advances with ML in healthcare to date have been seen in imaging, for example deep learning has been applied to detect malignant lung nodules from a dataset of more than 42,000 computerized tomography (CT) images.7

Another example is in the field of cancer biomarkers. Freenome,a US-based company has been offering screens that may help to detect cancer, such as colorectal cancer, earlier than using conventional methods.8 Freenome’s multiomics platform detects key biological signals from a routine blood draw. Its predictions are made based on thousands of cancer-positive blood samples with biomarker patterns that can be used to identify cancer staging, type, and most effective treatment pathways.8

Despite the many benefits afforded by the use of AI in healthcare, the complexity of diseases and their innate variability makes their effective use more challenging than in other sectors. Thus, the constant progression and changing of healthcare may prove a limiting factor in how AI is implemented.6 The approach taken to deep learning with medical AIs must consider a way in which to handle continuously changing healthcare data.6

AI in drug discovery

Artificial neural networks (ANNs) have already been used to aid in drug discovery, for example in predicting the sensitivity of cancer cells to drug compounds through analysis of the genomic profile of the tumor cell line.6 AI can be beneficial to drug discovery in many different ways; it can be used to predict drug interactions, targets, and side effects when testing for new drugs.6 Platforms exist in which data from research papers, clinical trials, patents and patient records are used to train AI models so as to produce ‘knowledge graphs’ of conditions and their associated genes and/or compounds affecting it.10 AI can put these data into context, identify useful  information for drug-discovery and can be used to find existing drugs that may already currently be used.10 One such system suggested over 100 existing compounds that might have potential for treating motor neuron disease that could otherwise have been missed.10 Additionally, AI software can be used in clinical trial recruitment to identify patients who would make the best candidates for a particular trial, thus streamlining the selection process.3 Although these assistances may seem quite small, their implementation can improve efficiency of the drug discovery process, saving both time and money for all involved.3

AI has the potential to be used in various aspects of healthcare; its ability to predict disease risk, personalized prescriptions and streamline clinical trial recruitment6 are seemingly just the tip of the iceberg. However, there is still more work to be done to truly realize the role of AI in medicine and we must be wary to not over-inflate our expectations of AI.9 Overall, it seems that the use of big data and AI solutions in healthcare is beginning to become the leader in this technological age, yet time is still needed for them to start curing disease rather than just diagnosing it.

Discover more about AIs and big data in our articles on how AI is changing healthcare, and Big Data. Sign up for our monthly updates too here.

References

  1. Forbes. How much data do we create every day? The mind-blowing stats everyone should read. Available at: https://www.forbes.com/sites/bernardmarr/2018/05/21/how-much-data-do-we-create-every-day-the-mind-blowing-stats-everyone-should-read/#4ebb608c60ba. [Accessed April 2020].
  2. Tsigelny I. Briefings in Bioinformatics 2019; 20(4): 1431­–1448.
  3. No longer science fiction, Ai and robotics are transforming healthcare. Available at: https://www.pwc.com/gx/en/industries/healthcare/publications/ai-robotics-new-health/transforming-healthcare.html [Accessed April 2020].
  4. World Economic Forum. Here are 3 ways AI will change healthcare by 2030. Available at: https://www.weforum.org/agenda/2020/01/future-of-artificial-intelligence-healthcare-delivery/ [Accessed April 2020].
  5. Jiang F et al. Stroke and Vascular Neurology 2017; 2: e000101. doi:10.1136/svn-2017-000101.
  6. Miotto R, et al. Briefings in Bioinformatics 2018; 19(6): 1236–1246.
  7. Burki T. The Lancet Respiratory Medicine 2019; 7(12) 1015–1016.
  8. Spot the pattern, treat the cancer. Available at: https://www.freenome.com [Accessed April 2020].
  9. 4 Ways In Which AI Is Revolutionizing Respiratory Care. Available at: https://www.forbes.com/sites/shourjyasanyal/2018/11/26/4-ways-in-which-ai-is-revolutionizing-respiratory-care/#12f8223a318d [Accessed May 2020]
  10. Fleming N. Nature 2018; 557(7706): s55+

December 2020 RESP-42263

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