Three Phases AI Adoption in Healthcare

As healthcare stakeholders realize the essentiality of AI and data analytics to make better-informed healthcare decisions, AI’s integration with healthcare processes will scale in three phases. However, what requires changing to encourage the introduction and scaling of AI in healthcare?


Building on analytics, artificial intelligence (AI) has the potential to revolutionize healthcare. AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the day-to-day life of healthcare practitioners, letting them spend more time looking after patients and in so doing, raise staff morale and improve retention. It can even get life-saving treatments to market faster.

As the healthcare industry starts to embrace artificial intelligence and analytics to improve decision making and anticipate patient needs, executives are excited about integrating it in the health systems soon.

In a recent survey of more than 600 global companies, nearly half (48 percent) of healthcare executives and board members expect AI and machine learning (ML) to play a significant role in their digital strategies three years from now. Nearly one in three (32 percent) expect that assessing and adopting new digital tools, like AI, will be one of the three most critical aspects of digital decision-making in the coming year.

Phases of scaling AI in healthcare

We are in the early days of our understanding of AI and its full potential in healthcare, in particular with regards to the impact of AI on personalization. Nevertheless, over the time we could expect to see three phases of scaling AI in healthcare, looking at solutions already available and the pipeline of ideas.

  • First, solutions are likely to address the low-hanging fruit of routine, repetitive and largely administrative tasks, which absorb significant time of doctors and nurses, optimizing healthcare operations and increasing adoption. In this first phase, we would also include AI applications based on imaging, which are already in use in specialties such as radiology, pathology, and ophthalmology.
  • In the second phase, we expect more AI solutions that support the shift from hospital-based to home-based care, such as remote monitoring, AI-powered alerting systems, or virtual assistants, as patients take increasing ownership of their care. This phase could also include a broader use of NLP solutions in the hospital and home setting, and more use of AI in a broader number of specialties, such as oncology, cardiology, or neurology, where advances are already being made. This will require AI to be embedded more extensively in clinical workflows, through the intensive engagement of professional bodies and providers. It will also require well designed and integrated solutions to use existing technologies effectively in new contexts.
  • In the third phase, we would expect to see more AI solutions in clinical practice based on evidence from clinical trials, with increasing focus on improved and scaled clinical decision-support (CDS) tools in a sector that has learned lessons from earlier attempts to introduce such tools into clinical practice and has adapted its mind-set, culture and skills.

Ultimately, we expect to see AI as an integral part of the healthcare value chain, from how we learn, to how we investigate and deliver care, to how we improve the health of population.

Let’s understand this transition in detail as we discuss and investigate what needs to change to encourage the introduction and scaling of AI in healthcare during the upcoming CXO Digital Innovation Health Summit 2021. The panel discussion titled, “How AI & Analytics can redefine Healthcare?” will analyze in detail on how Covid-19 impacted the data-driven healthcare processes, how AI and analytics can be integrated into clinical workflows, choosing the right use-cases, and how industry leaders are driving AI investment decisions on February 11, 2021 at 2PM. Don’t miss this webinar – be sure to register today.

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