There are many opportunities within healthcare for AI to assist scientists and healthcare professionals, in improving patient care and outcomes. Although clinical algorithms are undoubtedly important, and AI will play a large part in diagnosing and treating patients, their usage can be multifaceted. 

Embedding AI seamlessly in intuitive and usable workflows can automate laborious and error-prone administrative tasks that are an inevitable part of a physician’s workload. On the other hand, an equally-important application is during the research and discovery stages for new biomarkers and treatments. 

The ability of AI to deliver novel insights into large, heterogeneous and diverse datasets challenges existing statistical and bioinformatics approaches - and early use of AI in the discovery pipeline will also allow early adoption of these approaches when the treatments and biomarkers reach the clinic. 

This end-to-end approach could allow precision medicine to be realisable at scale, and free up clinicians to concentrate on their patients rather than the intricacies of determining the best-individualised treatments for them, which often involves human decisions, and can vary from clinician to clinician and from patient to patient.

AI has the potential to have a major impact on Healthcare AI, particularly Imaging, Integration of Multimodal Data and Drug/Biomarker Discovery. In terms of maturity and adoption, Imaging appears to be a prime area in which to apply AI and there are a number of specialities in medicine which make use of AI for imaging analysis, including Radiology, Cardiology, Histopathology, and Cytopathology.

In Radiology, and also in the arena of image-assisted diagnosis of heart disease, the use of digital images, and image processing is accepted. This is evident in the maturity of the use of AI within the domain. 

There are multiple platforms and applications approved for clinical use, and AI is becoming embedded in the workflows of radiologists. However, it appears that this AI is being used to augment rather than replace radiologists and we are still some way from the vision of Geoff Hinton, who stated in 2016 that "It's quite obvious that we should stop training radiologists now".

In the pathology domain, AI has been applied in a number of ways. In addition to the prime use case of assisting pathologist diagnosis and grading of cancers, a number of other applications of AI have been addressed, including screening of negative results (to pick up false negatives), characterization of tissue, and quantification of tumour.

While the digitisation of pathology is an ongoing process, the recent regulatory approvals for manual primary diagnosis using digital images by Philips and Leica, and subsequent approvals for AI-assisted pathology of prostate cancer and breast cancer (which requires digitisation) will undoubtedly accelerate the use of AI in this complex domain.

Outside of the direct, primary clinical use cases for imaging analysis in pathology, there are many more pathology tasks in the research and discovery domain, which would benefit from automation through AI. Indeed, reviewing the research literature, there are a multitude of applications of AI in the analysis of H&E, IHC and IF images. Often, AI is used to reproducibly automate tasks which pathologists and researchers find challenging and laborious to carry out manually.

Outside of imaging, there is also much development in the application of AI in ‘-omic’ data. Indeed, if ‘traditional’ machine learning approaches can be termed AI (which I would argue they can), then through the use of clustering, nonlinear regression, and classification approaches such as random forests or XGBoost, AI is probably more embedded in these areas than anywhere else in healthcare. 

Given the amount and dimensionality of the data provided by techniques such as gene sequencing, RNA expression, mass spectrometry etc., analysis of such data would not have been possible without using AI to give true insight into the data. However, now we are seeing the use of more broadly-defined AI systems based on deep learning in these domains to provide deeper insights into correlation and causation, and this has the potential to further interrogate new and legacy datasets to obtain new insights, both in research and clinically.

In the drug discovery domain, as in others, the application of AI is becoming, if not common, more prevalent. This is not just using the imaging and genomics AI approaches already discussed, but there are now tremendous advances in applying AI to problems such as protein folding. Indeed the recent announcements from Google’s DeepMind regarding the groundbreaking results from their DeepFold algorithm, definitely suggest that AI will transform all aspects of drug and biomarker development.