AI & ML Intelligence

Between healthcare chatbots, smart patient records, doctors armed with predictive analytics and frontline staff often interfacing electronically, patients want healthcare interactions and experiences to be transparent and personalized.

Artificial intelligence has the potential to expand a healthcare organization, reaching more patients and improving outcomes. But how can healthcare leaders help their clinical workforce understand the implications and ensure their buy-in?

Understanding the threat environment, knowing where controls are deployed and other core competencies become that much more critical when artificial intelligence is involved, says the CISO of Mass General Brigham.

Healthcare leaders must demand ethical artificial intelligence and ensure their RFPs list requirements for safeguards, says Sarah M. Worthy of DoorSpace. This will take time, which would mean pulling back from investing in the majority of AI tools.

Advancements in machine learning are enhancing new large language models' ability to undergo continuous learning and generalize to areas in which the model has not been trained – a transformative next step for AI in imaging.

Beyond overseeing the implementation of AI projects, CAIOs consider broader implications, such as changes to operations and culture. And they aim to ensure AI systems are developed and deployed ethically, transparently and responsibly.

Explainable AI aims to make models more interpretable and transparent, ensuring their decision-making processes can be understood and trusted by clinicians and others.

"Telehealth already has proven its value in increasing access to care – but paired with effective, responsible AI usage, it holds the promise of more efficient, effective and personalized mental health services," a telepsychiatry provider CEO says.

From the custodian to the CEO, everyone is required to take a homegrown course – created with generative artificial intelligence – to prepare for transformation coming to healthcare, the VP of planning explains.

Providers should push hard to maximize the use of generative AI and natural language processing tools – but validate performance for each system rigorously before making it available for widespread use, a physician author says.