Virtually all industries can benefit from artificial intelligence (AI), but few see improvements as meaningful as healthcare. While medical AI faces considerable obstacles, it’s hard to ignore its potential. Applications in chronic disease management are particularly noteworthy.

Roughly 129 million people in the U.S. have a chronic health condition. Despite many of these diseases being treatable, they’re also behind five of the 10 most common causes of death. Consequently, managing them is becoming increasingly crucial, and AI can help in several ways.

Improved Diagnoses and Prognoses

The use of AI to treat chronic conditions starts with a patient’s diagnosis. Because machine learning excels at detecting subtle trends in data, it can often identify diseases early and with impressive accuracy. AI even outperformed doctors at detecting skin cancer in 61.2% of studies and matched human experts in another 29% of cases.

Similarly, predictive analytics models can predict how an individual patient’s case may progress. This insight into the future helps patients and doctors understand the situation so they can better decide how to treat the condition. These predictions become increasingly reliable as AI gathers additional data.

Personalized Healthcare

Once treatment begins, AI can improve health outcomes by tailoring care plans to individual patients. The same disease may play out entirely differently between patients with varying biologies, lifestyles and socioeconomic backgrounds. Consequently, healthcare is most effective when it’s specific to the individual, and AI provides the granular data analysis necessary to personalize it.

AI-powered personalized care apps already exist. Some generate meal plans for individual users to help them meet unique nutritional goals based on their background and biology. Others predict how different medicines may perform for varying patients. Across all cases, machine learning can take the trial and error out of finding the best treatment, leading to more effective long-term care.

Automated Reminders and Updates

As patients try to stick to their nutrition and medicine plans, AI apps can offer automated reminders to help. Many people don’t take their meds as prescribed, leading to 125,000 deaths annually in the U.S. AI can monitor data from wearables to detect when patients may be veering off their treatment plans and alert them so they can respond before it’s too late.

Similarly, AI monitoring tools can provide doctors with real-time updates on how their patients’ conditions are progressing. In some cases, machine learning algorithms could then use this data to predict if a change of course would yield better results. Even without that level of automation, AI’s timely insights enable faster, effective care changes.

Streamlined Administrative Tasks

There are behind-the-scenes benefits to AI in chronic disease management, too. Smart algorithms can automate administrative work like scheduling, data entry and health documentation compliance. As a result, doctors and nurses get more time to spend with patients.

Improving efficiency and accuracy in these tasks also produces financial advantages. Administrative work accounts for up to 25% of healthcare spending in the U.S., so removing errors and minimizing the time spent on it leads to considerable savings. As more hospitals take advantage of this opportunity, the industry could justify lower costs to become increasingly accessible.

Potential Downsides to AI in Chronic Disease Management

While the upsides to AI in chronic disease management show significant promise, there are some complications to consider, too. Before the technology can reach its full potential, medical organizations must grapple with issues of bias, hallucinations and data security.

Bias

One of the biggest challenges of AI in healthcare is its tendency to replicate and even exaggerate human prejudices. Machine learning’s accuracy varies between demographics — facial recognition misidentifies Asian and Black faces up to 100 times more often than white faces. Similar issues could make AI diagnoses far less reliable for patients of color.

Human bias throughout history means there’s less data on Black patients and other minorities. Consequently, AI may be unable to accurately diagnose their conditions or determine which treatments will be most effective. Failing to recognize this gap could worsen the inequality in care already plaguing the U.S. medical system.

Hallucinations

There’s also the hallucination issue to deal with. Even the most accurate machine learning models can hallucinate, thanks to various data-related problems and the fact that AI cannot identify facts — it only sees trends in data. In many applications, hallucinations cause minor hiccups, but they could be dangerous in a chronic disease context.

Doctors could over-rely on AI and fail to account for its tendency to hallucinate. Once that happens, they could prescribe treatments based on false or misleading predictions, possibly leading to risky health complications. Human experts must always have the final say, but the mere presence of AI suggestions can lead to complacency and overreliance.

Data Privacy and Security

AI models also require a significant amount of data, and medical information is highly sensitive. This combination raises questions about patient privacy and cybersecurity. What if an AI service accidentally discloses personal information about patients? What if AI datasets make hospitals a bigger target for cybercriminals?

Healthcare has already seen a troubling rise in cybercrime. The industry suffered 809 data compromises in 2023, more than double the year before. Security standards will likely need to evolve before hospitals can safely feed large volumes of patient information to AI models.

AI Could Revolutionize Chronic Disease Care

While obstacles remain, AI has big potential in the realm of managing chronic diseases. As the industry develops regulations and best practices around using this technology safely, its promise will only grow. Increasing use and technological development will lead to better patient outcomes, lower costs and streamlined medical workflows, benefiting everyone involved.