While machine learning has the potential to transform disease diagnosis and detection, it needs to get past patients’ reluctance to give up access to sensitive information.

From fitness trackers to high-end imaging devices, consumers and clinicians are increasingly embracing devices using AI technologies, even as regulators struggle to determine the best way to test and approve them.

Under a higher standard, say the experts, new predictive analytics must demonstrate impact on the delivery of health care and patient outcomes.

It hasn’t taken long for AI to dominate conversation in the world of healthcare data analytics, but that doesn’t mean all the pieces are fully in place for optimal success.

Clinical AI is vitally important, says one healthcare stakeholder, but financial and operational AI are equally important, while also being easier systems to implement.

With lifespans rising and healthcare offerings evolving exponentially, costs are bound to rise, but AI can bring a degree of simplicity and transparency to the insurance market for both customers and insurance providers alike.

The ultimate vision is to reinvent the entire doctor-patient experience in a way that allows clinical documentation to essentially write itself – and help cure physician burnout.

Inaccuracy and inconsistency still plagues healthcare Big Data, says one data expert, not to mention that sometimes the data being analyzed aren’t even the right kind of information.

AI will have widespread impact, HIMSS19 attendees predicted, particularly by influencing clinical decision-making, while telehealth and devices that facilitate the transition to home-based care will change the dynamics of healthcare delivery.