What Is the Difference Between Augmented Intelligence and Artificial Intelligence in Healthcare?
The term AI is used generically, but it actually refers to two forms of intelligence.
Artificial Intelligence
An artificial intelligence is designed to perform tasks autonomously. One example is the DAX Copilot from Microsoft, which listens and takes notes during healthcare appointments. It analyzes the data and generates a clinical summary that can be added to the electronic health record.
Augmented Intelligence
Augmented intelligence refers to devices that are created to help people work more effectively. Augmented intelligence tools in radiology, for example, can be used to analyze images and detect anomalies.
“I think the biggest impact in healthcare is from augmented intelligence,” says Chris Darland, CEO of Peerbridge Health. “You’re making better tools for the doctor to help them do their job.”
How Is AI Being Used in Healthcare?
Current AI applications in healthcare span multiple areas:
AI in Medical Diagnosis
AI can analyze medical data sets more thoroughly than a human can and won’t get tired in the process. As described above, tools powered by augmented intelligence are currently used to assist radiologists. Systems like the Precision Imaging Network from Nuance Communications examine images such as MRI scans and X-rays to help radiologists make the most accurate diagnoses possible and detect diseases sooner.
Wearable devices are also helping to improve patient outcomes by gathering critical data that can inform care decisions. For example, Peerbridge Health developed an AI-powered, wireless electrocardiogram patch that monitors cardiac activity remotely. “It gives cardiologists a full view of the heart,” Darland explains, “and the data can be used to create a treatment plan.”
AI in Drug Discovery
Clinical drug development projects take several years and have a 90% failure rate, but AI has the potential to enhance this type of work. Systems such as AlphaFold 3 from Google are designed to help researchers better understand how different molecules will interact and help them predict whether a particular drug structure will be effective against diseases.
Armed with this data, scientists can “find the winners faster, which means getting to clinical trials sooner and helping more patients,” Darland says.
Advancements are happening in real time. A team at Stanford Medicine, for example, says it used a generative AI model called SyntheMol to develop drugs that target antibiotic-resistant bacteria.
READ MORE: What does the growth of generative AI mean for drug discovery and clinical trials?
AI in Patient Experience
Healthcare chatbots are increasingly used to assist patients with routine tasks such as scheduling appointments and requesting prescription refills. Tools such as IBM Watson Text to Speech service enable healthcare organizations to communicate with patients in multiple languages.
Electronic patient portals are being used to enhance patients’ experiences at in-person appointments, Warrelmann explains. “We have a customer whose GPS-enabled health app, with the help of AI, knows when a patient arrives and automatically checks him or her in,” he says. “It verifies insurance and pulls up the patient’s chart. It makes the intake and triage process more efficient.”
