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The future of artificial intelligence in health care
Emerging applications of AI in health care
Artificial intelligence (AI) is transforming the way we interact, consume information, and obtain goods and services across industries. In health care, AI is already changing the patient experience, how clinicians practice medicine, and how the pharmaceutical industry operates. The journey has just begun.
- Three categories for applications of AI in health care
- The benefits of AI in health care
- Patient self-service benefits
- Implementing AI in health care: Lessons learned
- Related topics
Three categories for applications of AI in health care
As AI finds its way into everything from our smartphones to the supply chain, applications in health care fall into three broad groupings1:
- Patient-oriented AI
- Clinician-oriented AI
- Administrative- and operational-oriented AI
The future of AI in health care could include tasks that range from simple to complex—everything from answering the phone to medical record review, population health trending and analytics, therapeutic drug and device design, reading radiology images, making clinical diagnoses and treatment plans, and even talking with patients.
The future of artificial intelligence in health care presents:
- A health care-oriented overview of artificial intelligence (AI), natural language processing (NLP), and machine learning (ML)
- Current and future applications in health care and the impact on patients, clinicians, and the pharmaceutical industry
- A look at how the future of AI in health care might unfold as these technologies impact the practice of medicine and health care over the next decade
1 Laura Craft, Emerging Applications of Ai for Healthcare Providers, GARTNER, June 30 2017, accessed June 24, 2019
The benefits of AI in health care
From patient self-service to chat bots, computer-aided detection (CAD) systems for diagnosis, and image data analysis to identify candidate molecules in drug discovery, AI is already at work increasing convenience and efficiency, reducing costs and errors, and generally making it easier for more patients to receive the health care they need.
While NLP and ML are already being used in health care, they will become increasingly important for their potential to:
- Improve provider and clinician productivity and quality of care
- Enhance patient engagement in their own care and streamline patient access to care
- Accelerate the speed and reduce the cost to develop new pharmaceutical treatments
- Personalize medical treatments by leveraging analytics to mine significant, previously untapped stores of non-codified clinical data
While each AI technology can contribute significant value alone, the larger potential lies in the synergies generated by using them together across the entire patient journey, from diagnoses, to treatment, to ongoing health maintenance.
Implementing AI in health care: Lessons learned
Based on our work with clients on applications of AI in health care, we offer these insights:
- Factor in extra time and cost for early adoption: even relatively small projects require additional time and effort up front performing business case validations and proof of concept.
- Reduce cost and complexity by leveraging open-source technologies and limiting customization.
- Build solutions for average transaction length and volumes but with capacity for longer transactions and peak volumes.
- Involve personnel with a combination of technology and health care backgrounds who have a clearer understanding of end users’ needs and preferences, as well as options for technology solutions.
- Carefully select the data used to “train” any AI/ML model: Make sure it accurately represents the production data and does not incorrectly train and bias the model.
- Since training of models is an ongoing process, expected return on investment (ROI) should include the time period and time frame.
Preparing for the future of AI in health care
Health care providers can prepare for the inevitable changes related to the future of AI in health care with the following key considerations.