As more health data is being acquired, the utility of that data in improving patient outcomes is also increasing. Artificial intelligence (AI) improves patient care by integrating health data across different platforms. In particular, some of the key areas for AI to improve health care include diagnostic imaging, patient risk analysis, enrollment in clinical trials, and patient communication.

AI’s first success in health care was in the field of diagnostic imaging. Machine-learning algorithms can find patterns in images and identify specific anatomical markers. These algorithms can spot details the human eye can’t catch, perhaps greatly improving a radiologist’s workflow. When a radiologist can validate and add to an AI assessment rather than scanning hundreds of images, she has more time to actually spend with her patients. A recent example of AI improvement in radiology is LabCorp and Mt. Sinai in New York City partnering to create the Mount Sinai Digital and Artificial Intelligence-Enabled Pathology Center of Excellence. This partnership will provide doctors and patients throughout the United States access to a digital pathology solution that has been developed at Mt. Sinai. When complex cases come from a larger network, expert pathologists will have more time to analyze and diagnose such anomalies.

Another area where AI can improve health outcomes is through finer-tuned risk analysis. Self-monitoring devices are giving patients and their doctors access to much more information about a patient’s lifestyle that can be crucial to plans of care. This data can be used to assess if patients are at risk of developing conditions such as diabetes, or even if they are at risk for non-compliance to medication or lifestyle changes. Recently, three Johns Hopkins hospitals set up machine-learning-based systems that constantly scan EHR, lab tests, notes, and radiology tests of every admitted patient. If a patient crosses a threshold for having a high likelihood of developing sepsis, the system sends an alert to just the most relevant providers.

AI can also greatly improve patient enrollment in clinical trials. The current process for clinical trial enrollment often places the onus on the patient to research and find eligible trials. With increased data on a patient’s demographics and diagnosis, integrated with inclusion criteria for ongoing trials, the whole process can be automated to get patients into life-saving treatment. This change can also increase clinical trial success rates if patients are only enrolling in trials that are more well-suited for their conditions.

Finally, AI has the potential to completely overhaul the way in which patients communicate with their doctors. While current systems of communication have no way of ensuring if patients are understanding the information that doctors are conveying to them, metrics on patient responses can add some clarity to these conversations. There are several methods that are emerging for analysis of conversations between providers and patients. AI can look at things such as the language that providers are using (is it all jargon that the patient may not understand?), turn-taking (is the provider sending too much information all at one time and not allowing for questions?), and analysis of tone and speed of spoken communication to give providers insight into the patient’s understanding of his or her own medical condition. This analysis can also be helpful in the setting when a translator is used, ensuring that nothing is being lost in translation.

Additionally, chatbots are being piloted as an alternative where patients can get access to information on medication, doctors’ schedules or clarifications of doctors’ instructions. This can replace the unreliable information that patients find online that is not specific to his or her own conditions or perhaps just inaccurate. One research report by Juniper Research predicts that the success of chatbot interactions where no human interventions take place will go up to 75% in 2022 from 12% in 2017. This study also predicts that chatbots alone will save $8 billion annually by 2022.

Also, chatbots can actually be used to deliver Cognitive Behavioral Therapy in much the same way that a psychologist would for patients. This can be particularly beneficial for patients in rural areas who cannot access adequate mental health care or others who do not have the financial means to pay for a mental health provider.  

Dr. Albert Wu writes that “the great irony today is that artificial intelligence may prove to be the force that helps bring that human touch back to the forefront of medicine.”

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