This is a recap of the industry insights I shared during the Practical Applications of AI in Healthcare workshop held on Sat Dec 12, 2020 10–11:30am ET. The workshop was hosted by Hiren Dossani and the Artificial Intelligence & Cloud Toronto meetup.
Artificial intelligence in healthcare is an overarching term used to describe the utilization of machine-learning algorithms and software, or artificial intelligence (AI), to emulate human cognition in the analysis, interpretation, and comprehension of complicated medical and healthcare data. Specifically, AI is the ability of computer algorithms to approximate conclusions based solely on input data.
The primary aim of health-related AI applications is to analyze relationships between prevention or treatment techniques and patient outcomes. As widespread use of AI in healthcare is relatively new, there are several unprecedented ethical concerns related to its practice such as data privacy, automation of jobs, and representation biases.
In this session, you will learn about how multiple companies all across the globe are developing technologies that help Homo Sapiens i.e. Human Evolution!
Complexities in the Healthcare Industry
Innovation Opportunities & AI Use Cases (Sample)
At the start of 2020 during the pre-COVID era, CB Insights had highlighted 7 areas of innovation in healthcare in China. AI was the top of list and, in fact, was set to enable innovation in the other 6 areas identified on the list — telehealth, medical devices, digital health in China, women’s health, mental health and regenerative medicine.
Existing Healthcare AI Use Cases
We spent some time reviewing and discussing existing AI solutions in healthcare. The conversation included many qualitative and quantitative dimensions. Apart from the revenue capabilities we had already discussed in the previous section, we talked about many additional dimensions.
Upcoming Healthcare AI Use Cases (Sample)
Next we dove into the use cases which are currently under exploration and have seen more interest due to lifestyle changes brought on by the pandemic.
AI Supported Radiology
As Eunji P. mentioned off-screen, “also one of the differences between this image recognition and previous technology is that, there’s no direct touch point between patients and robots, which means definitely the lower barrier to enter into our real life”. This is a very important point which can drive adoption of some use cases over others (radiology vs. surgery).
Marcia H. also shared her own work in this field, as seen in the discussion above. Here is a link to her paper in this area https://arxiv.org/abs/1711.11117
Innovation’s Impact on Jobs & Public Policy
We also touched upon the social enterprise aspect of AI, and innovation in general. This involves the relationship between disruptive tech and how we deal with job-loss & public policy changes. In my opinion, innovation efforts are often highly localized, often only focused on introducing changes within part of a business ecosystem. We often miss the holistic impact on other stakeholders of this ecosystem, including partners, businesses, people and even public policy.
The healthcare conversations during the pandemic are beginning to change this. For instance: the data tracking, ownership and use from the COVID-tracing apps. How the conversation evolves as the COVID situation resolves itself remains to be seen. If we do not learn from our historic innovation journeys, then we are doomed to repeat it. In my opinion, 2021 innovation efforts need to rake in more holistic perspectives and actively address confirmation bias to ensure economic growth and inclusive tech.
Emerging Healthcare AI Use Cases
Pandemic has re-prioritized many use cases that AI could support in healthcare. Some of these solutions are still awaiting possible solutions and there is high interest from the healthcare community in resolving these issues to subside, control and eliminate COVID-19.
There are some areas that my research and interaction with healthcare professionals have brought to light.
A Summary of Ethical Questions in AI
What’s Next? Orchestrating AI at an Enterprise-level
As we discussed throughout the session, there are a number of considerations and stakeholders involved in successfully developing, deploying and scaling AI solutions. My focus as a professional is to help answer, ‘how can a business, its customers, global policy makers, and other stakeholders work together towards an ethical and sustainable AI solution for the real-world business problem?’. This is an orchestration problem that requires strategy, governance and planning considerations.
I will host workshops in 2021 diving deeper into how enterprises can build business cases for these use cases, what foundational technologies are needed, how to map the desired customer journeys to the technology tools and capabilities needed to implement these solutions at scale within the enterprise.