Following our post on February 12th about effective AI/ML adoption strategies, we continue to research the areas where we will see concrete gains from AI/ML deployments. This is our quest to demonstrate to our customers that results can be achieved now, and the ROI for AI deployment is real. The consensus seems to be that gains will come from facilitating administrative functions and reducing human efforts for executing repetitive tasks.
In the healthcare sphere, significant progress has been made in radiology, drug discovery and patient risk assessment. Anticipation is high for the next wave of eye-catching AI advancements, but the challenge of obtaining sizeable curated data-sources and privacy and security concerns stand in the way.
Simon Marshall points out that the best opportunities for AI in healthcare over the next few years are hybrid models, where clinicians are supported in diagnosis, treatment planning, and identifying risk factors, but retain ultimate responsibility for the patient’s care. This will result in faster adoption by healthcare providers by mitigating perceived risk, and start to deliver measurable improvements in patient outcomes and operational efficiency at scale.
PWC in its AI predictions for 2020 suggest to get on board with boring AI and focus artificial intelligence efforts on back office tasks and automation to reap ROI and lay the foundation for real transformation. They continue to say that considering the size of the AI prize, the disruption of markets and industries is simply a matter of time — and the clock is ticking. So, companies that take the right steps to make AI payoffs a reality have an opportunity to use AI to create the disruption their competitors fear.
At MUUTAA we sought out exactly those areas in the Rx medication development and Rx medication delivery chain, where the efficiency gains in the workflow are significant, the ROI is evident and where the data availability is real for AI/ML to have an impact, now. Learn about these ‘boring’ AI applications on muutaa.com.