The use of AI is rapidly growing in healthcare. We estimate there are between 150-250 dedicated AI companies in the US and Europe who offer various applications in healthcare. Whereas most data is incomprehensible for humans, AI can extract what we need. This can transform diagnostics and clinical decision making, treatment strategies and operational processes. In this third blog of our AI series about healthcare, we describe these three main areas in which we see the majority of AI innovations and opportunities.
Whereas most data is incomprehensible for humans, AI can extract what we need. This can transform diagnostics and clinical decision making, treatment strategies and operational processes.
1. Diagnostics
The success of precision medicine relies on a good diagnostic process. About one third of healthcare AI companies focuses on diagnostics. Many of them aim to solve the discrepancy of the higher availability of diagnostic techniques that has come with an increment in pressure and difficulties for medical practitioners using them. With the current capacity of AI to recognise images and patterns in data, the main focal points in AI development are currently imaging & radiology, genomics & oncology and pathology.
We believe that successful innovations are those that fit in fully automated AI workflows. Although the number of AI solutions for diagnostics is growing rapidly, we see that most of them focus too much on single specific tasks. Such point solutions are hard to integrate into the clinical workflow and may face difficulties with implementation into large clinical sites that seek complete solutions. Being able to serve entire medical infrastructures will be a key driver for scalable growth of AI in healthcare. AI platforms that integrate both molecular biology techniques, bioinformatics and biostatistics with radiology images in a multivariate diagnostic approach will get a strong market position. Large multinational healthcare vendors can take advantage of the fact that they already serve entire health infrastructures and have the capacity to make integrated platforms.
2. Treatment
Drug discovery continues to be too expensive and time consuming, while adverse effects have severe impact. Moreover, common complicating issues in choosing the right treatment strategy are disease complexity, disease heterogeneity and large variability in survival rates. Most drugs never reach the market because they do not show efficacy in the disease for which they were developed. We foresee the most interesting opportunities for AI in identifying targets for developing new interventions, discovering drug candidates, improvement of prediction of survival rate, multitarget strategies, combinatorial therapies and repurposing and speeding up clinical trials.
We see a change in the market from the “one target one disease” approach in drug discovery to a multitarget approach and combinatorial therapies to make medicines more efficient, especially for complex diseases. Clinical trials can be accelerated by using AI to automatically identify suitable candidates as well as ensure the correct distribution of groups of trial participants. AI can also be used to create an early warning system for a clinical trial that is not producing conclusive results.
Altogether, we see a rising synergy between tech firms (large or small), pioneering or established pharmaceutical developers and universities or research institutes. With these collaborations, the complementary key drivers for AI are brought together: the extensive use of cloud computing, data lakes and warehouses on one the hand, and expertise in medical fields such as neuroscience, biochemistry, oncology on the other hand.
3. Operational processes in medical workflow
Healthcare providers are facing compliance to heavy regulations and strict privacy and data protection requirements. Patients demand personalisation in their care and assistance with complex requests. Coding is under significant pressure due to the expanding volume of data, resulting in incomplete data capture, and shortage in manpower. Introducing AI into operational processes can have an enormous impact on cutting costs and reducing stress on healthcare practitioners and patients.
Natural Language Processing may improve the process of clinical coding as it can read and interpret electronic free text at scale. The development of the electronic health record (EHR) system is an important step towards implementing enterprise cognitive computing in medical business operations. Several companies have developed tools based on the EHR to streamline processes such as the diagnostic workflow. Automated call centres, appointment scheduling, financial administration are other operational processes that allow for automation and use of AI.
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