Six Insights Into the Future of AI in Healthcare for 2021 and Beyond

AI in healthcare is a massive industry. The year 2020 has seen healthcare systems rapidly shift their operating models to deal with the huge influxes of infection cases and deaths caused by the COVID-19. AI has been a big help this year for the leading healthcare and pharma companies and researchers.

In a recent report published by The AI Journal, we revealed how AI is being put to work in the healthcare sector to save time and give core team members on the frontline more solutions to save a loved one, get effective treatment to more people at scale, and understand how a hospital can free up budget, while not losing quality to make the operating model more efficient.

Read Also: COVID-19 Data: How Are Different Communities Being Impacted During The…

But we found that there are many more advancements being made today that will inform the future of the healthcare sector. These are my six thoughts on what to look out for in 2021 and beyond for AI in healthcare:

1. AI is not expected to reduce jobs in healthcare

There have been many concerns that AI and machine learning will end up replacing clinicians. But this is not the direction I expect the sector to go in. As Eric Topol laid out in the recommendations of the Topol Review, AI will actually prepare the current workforce for a digital future with new models of care. We may see shifts in roles and some may need to diversify, but AI will actually gift healthcare professionals with more time to reconnect with the human side of medicine and move them away from the complex processes that often act as barriers to delivering the best possible care.

Read More: GravityAI Unveils Enterprise Data Science Marketplace for Fintech and Healthcare

2. AI is not a ‘set it and forget it’ technology

Unfortunately, AI models cannot be built once and remain useful forever. So-called ‘Black Swan’ health events such as we’ve seen with COVID-19 present unique situations and therefore cannot rely on historical data to make accurate predictions. The way to deal with this is to use an AIDevOps model to challenge the AI system by using some data to train the model, and additional datasets to test it. Outlier events like these should then trigger an alert for human review.

3. Leadership must change to fit the needs of AI

A change in leadership style is required. Leaders need to embrace a mindset of innovation that is no longer sustained by acquired experience and past knowledge but is curious, inquisitive, and responsive. Leaders need to be trained in asking the right questions instead of directing in a known and predictable environment. Such a transformation may be difficult in some organizations, and ones that promote experimentation will thrive. However, the difficulty for many is that their governance is not prepared for this kind of agility.

4. Healthcare needs to be bold

There are two main challenges that need to be overcome for us to progress with next-generation technology in healthcare. The first is that healthcare has always taken a ‘prove the cost savings before investing’ approach. This approach to innovation is very much fear-based and doesn’t take into account the unexpected benefits of a new application.

The second challenge is that healthcare has not traditionally been great at incentivizing health providers to share learnings and results for the greater good because they often compete against one another. When it comes to common hurdles like data interoperability, there is a clear benefit in using consolidated buying power and shared data. But to take advantage of this, there needs to be a change in funding structures and more clarity around budgetary responsibility. 

5. VR and AR could come together in mental health treatments

New treatments for mental health conditions are being developed with AI.

Personalized grading of stress responses could be used to measure the impact of and response to exposure therapy, for instance for the treatment of phobias. Indeed, we could soon be in a position to use virtual reality to immerse ourselves in a familiar simulation, measure our reactions and then provide personalized advice as feedback and advancement. This is one way in which the convergence of AI and VR will open the door to a deeper understanding of human emotion and behavior. 

6. But there are some sobering implications for ethics

With all of the opportunities I’ve mentioned, there are also some sobering implications and reflections for medical ethics. To build a brighter healthcare future aided by AI, we must use our moral imagination while rigorously reinforcing and protecting our ethical standards.

Some specific ethical considerations include patient confidentiality, the role that the bot may play in the diagnostic journey, and the risk of potential biases in the data used by machine learning algorithms. It is essential that developers have access to diverse datasets that include a wide range of ethnicities, genders, ages and other key variables. We cannot allow data gaps to put certain patient populations at greater risk of medical errors when using AI.

Conclusion

As we face this fascinating future, a number of questions arise in my mind that we will need to find answers on. What are the pain barriers that healthcare providers will need to go through before they can realize the opportunities? Will COVID-19 ultimately be seen as a catalyst for the improved integration of AI into healthcare systems, or a missed opportunity?

What challenges must be overcome with regards to data privacy, acceptability and ethics? And how can the industry as a whole work to mitigate bias around ethnic and gender diversity in AI-driven solutions?