The COVID-19 pandemic brought to light the shortcomings in the healthcare system, like a shortage of healthcare workers and uneven access to treatment. This catalyzed the patients to increasingly seek an active role in managing their health. However, with low health literacy levels, patients can fall prey to misleading health information. The advent of generative AI technology can aid in engaging with patients and gathering insights throughout their entire health journey. With its ability to mimic human speech and process unstructured data, it can tackle these challenges by communicating directly with patients.

Health Education Assistance

Almost all US healthcare consumers, according to a survey, use the internet to educate themselves about their health, self-diagnose, understand treatment protocols, and many other things. Nonetheless, only a few health websites, such as Mayo Clinic, offer expansive health information that meets the established quality thresholds for accurate healthcare consulting, and healthcare consumers have limited health literacy. This is where generative AI may be able to provide healthcare consumers with reliable and accessible health information.

Large language models (LLMs) trained exclusively on health data can offer correct health information that is accessible and actionable. They can be trained in multiple languages, providing information to users regardless of their language fluency, education level, or cultural context. For example, Ada Health, a Germany-based medical AI company, has built a digital symptom checker that helps people understand their symptoms by answering health-related questions. This tool is based on natural language processing (NLP) along with generative AI. Ada has been curated, clinically vetted and continuously updated, which makes its health assessments consistent, meaning if you input the same symptoms, you will get the same advice every time.

Patient Care Navigation

With the shortage of healthcare workers, healthcare systems need help shifting their resources from non-urgent patients to urgent patients. The general public is suffering from aging and chronic diseases due to the increasing healthcare disparities and rising costs. A patient-facing triage solution—MyndYou have attempted to address navigation gaps by utilizing chatbots that function as virtual assistants. They offer solutions to healthcare providers to shift resources from non-urgent patients to those who require timely care. However, their failure to scale has been attributed to the lack of open-source models for over 7000 languages and the limited understanding of cultural nuances and health systems by predictive AI.

Generative AI, with its domain-specific and culturally diverse training, can begin addressing many of these gaps by entering the healthcare system and routing their care more effectively. Additionally, generative AI can automate patient intake, reducing administrative burden and assisting patients in navigating the first-mile journey. A real-world example is the Cedars-Sinai Connect app, which uses generative AI technology and has not only automated many aspects of patient intake and data entry but also allows 24/7 virtual access to doctors for personalized acute, chronic, and preventive care.

Early Disease Detection

Early detection and diagnosis of the disease can have effective treatment and improve patient outcomes. Generative AI can enhance medical imaging and, given the vast amount of medical data, can detect anomalies in X-rays, CT scans and other diagnostic images with greater accuracy. According to a recent study, AI-powered tools can reduce diagnostic errors by 80%, which leads to early detection and timely diagnosis. Additionally, generative AI can also analyze a person’s medical history and genetic makeup and create personalized risk profiles for various diseases. This data helps the doctor to intervene early and prevent the disease from worsening.

By analyzing the vast amount of patient data, generative AI is also helping uncover new biomarkers that correlate with specific diseases. For instance, the incidence of skin cancer is on the rise, and early diagnosis facilitates its easy detection and treatment. But most patients don’t know what to look for, resulting in unnecessary consultations. SkinVision, a regulated medical service, uses generative AI to analyze skin images and recommend the next steps to take, aiding in the early detection, timely intervention, and treatment to prevent the spread of cancer.

Disease Management

Treatment adherence is a key facet of managing an illness, as the diagnosis and the start of the treatment are just the beginning of a patient’s journey. However, studies have found that in the first year of treatment, 60% of the patients suffering from chronic illness miss doses, take the wrong dosage, or abandon treatment altogether. This inadequate medication adherence costs healthcare systems hundreds of billions per year. While traditional algorithms can predict when a patient is likely to miss a dose or discontinue their treatment, they do not provide effective interventions that keep patients on track.

Combining generative AI with predictive algorithms enables the creation of customized interventions for individual patients. Generative AI can bring together multiple data streams like biometrics and genetics to tailor the treatment plans for individual patients. Mayo Clinic is using the patient’s genetic profile and developing generative AI models to predict which treatment might work best for rheumatoid arthritis (RA). These models act as translators, reading the genetic code, which is unique to each person. By understanding these genetic clues, AI can suggest tailored treatments that are more likely to be effective.

Generative AI can be the most profound technological breakthrough in healthcare settings. When combined with predictive AI, it has the potential to make scientific breakthroughs more accessible and beneficial for individuals worldwide. Aiding in healthcare education assistance, patient care navigation, early disease detection, and disease management improve the healthcare system, which is fair and easily accessible across different communities.