Key Considerations for Integrating AI-Driven Personalized Medicine and Early Detection in Clinical Settings

By Shaji Nair

Caring for people with chronic conditions consumes 90% of the more than $4 trillion spent annually on healthcare in the United States.

The percentage of Americans ages 35 and above who have received all their recommended high-priority screenings dropped from 8.5% in 2015 to 5.3% in 2020. As clinicians know, identifying people at risk for chronic conditions early and helping patients understand and manage their conditions can promote longevity, reduce healthcare spending, and minimize emergency room visits and extraordinary treatments that can be both painful and costly.

However, clinician time is more valuable than ever, with shortened appointment slots and greater administrative burdens causing burnout rates to remain stubbornly high, making it difficult to provide patient-centered care.

Emerging AI solutions can play a transformative role in the early identification and management of chronic conditions by automating the creation of baseline diagnostic and treatment plans while providing patients the tools to manage their conditions based on their diagnosis, family history, environmental factors, and personal preferences.

In clinical settings, we see that AI can help in following ways:

• Early detection
• Clinical decision-making
• Patient education
• Prescription management
• Personalized medicine

Enabling a lifestyle management approach

AI’s role in early detection, monitoring, and management can impact outcomes of many chronic conditions. This includes diabetes, which consumes 25% of U.S. healthcare spending and will benefit greatly from emerging AI solutions that promote preventive care, and lifestyle approaches to care.

Diabetes impacts nearly half of all Americans. This includes nearly 12% of the U.S. population with diabetes and 38% with pre-prediabetes—blood sugar levels that are higher than normal but not high enough for a diabetes diagnosis. Diabetes ranks as the most expensive chronic condition in the nation, accounting for $307 billion in direct medical costs and another $106 billion on reduced productivity.

While genetic or age-related risk factors can’t be changed, studies show that lifestyle changes characterized by focusing on healthy eating and physical activity can reduce the risk of Type 2 diabetes by more than 50% among people identified as high risk. These individuals can benefit from AI-enabled solutions across the care continuum, from early detection and education to ongoing diabetes management:

• Early detection and intervention: Endocrinologists manage specific aspects of diabetes, but specialty medicine is not only more expensive, it is not available in many parts of the country that lack comprehensive healthcare services. By providing primary care clinicians with access to enhanced tools facilitated by AI, healthcare professionals can take a more proactive role in the long-term management of their patients with diabetes. By analyzing data on lifestyle, activity, and health history, they can forecast outcomes such as, “If current habits persist, diabetes may develop in X years,” which in turn empowers patients with actionable insights that foster education and motivate lifestyle changes. Early detection also enables preventive measures, improving patient health outcomes and reducing costs for complex treatments. Payers also benefit by prioritizing prevention over costly interventions like weight-loss medications, which often address complications rather than root causes.
• Informed clinical decision-making: AI can digest information across years of visits and identify issues humans may miss, such as patients who have been borderline prediabetic for years. One emerging use case involves integrating generative AI into EHR systems to streamline diagnostics and treatment planning. When patient data is entered into the system, generative AI could automatically suggest a baseline diagnostic or treatment plan that physicians would then refine, optimizing their time and expertise rather than starting from scratch. Generative AI can also accelerate clinical notes and treatment plans, saving as much as 100 minutes daily that can instead be spent seeing patients. While still in the early stages, generative AI-powered tools represent a transformative step toward automating aspects of decision-making, reducing administrative burdens, and improving care delivery.
• Personalized patient education: Patient education often fails because patients rarely revisit or fully engage with materials provided during appointments. AI can solve this by delivering personalized, actionable updates regularly, ensuring the information remains accessible and relevant over time. For example, instead of overwhelming patients with all details at once, AI systems could provide reminders tailored to their condition, such as drinking more water, taking vitamins, or maintaining activity levels. These updates could occur daily or weekly, reinforcing healthy habits and fostering engagement. AI could also inform patients about the latest research, treatments, or clinical trials related to diabetes or other conditions. By personalizing the patient education process, AI supports better adherence to treatment plans which improves health outcomes.
• Optimizing scheduling: By analyzing patient needs and a doctor’s availability, AI could not only automatically recommend when a patient should be seen by their physician based on real-time data gathered from wellness and other connect apps but also suggest real-time appointment slots and prioritize visits for high-risk patients. This approach personalizes care, reduces administrative burdens, and enhances overall efficiency.
• Automating prescription management: Half of all diabetes patients don’t take their medications as prescribed, which carries an estimated cost burden of for $16 billion annually. Automating prescription management through AI can significantly enhance patient adherence and convenience. Patients could use integrated systems like smartphone apps to receive personalized medication reminders. These systems can also notify patients when prescriptions are running low, prompting refill requests. While basic reminder services exist through some pharmacies, integrating these features into a unified platform could make the process more seamless. Such systems could enable patients to request refills directly through an app, bypassing the need to call their physician’s office. Alerts would trigger automatic communication with the prescribing doctor, streamlining the process and saving time, and make it easier for patients to maintain their schedules and avoid lapses in treatment, ultimately supporting better health outcomes and convenience.

Tailoring treatment to the individual

Every patient requires a unique treatment plan, yet doctors often lack the tools to fully integrate all necessary components. Currently, treatment often involves recommending lifestyle changes, suggesting a nutritionist, and prescribing medications. However, these elements can conflict—for instance, steroid-based medications may cause weight gain, undermining dietary goals or activity plans. Similarly, advice from specialists or online sources may inadvertently clash with prescribed treatments.

These challenges stem from fragmented data: medication effects, nutrition plans, and lifestyle changes are typically siloed, making it difficult for doctors to identify interdependencies. The goal is to create truly personalized healthcare plans that account for a patient’s unique health conditions, medications, diet, activity level, and family history.

By using AI to integrate data and address these interdependencies, healthcare providers can deliver customized treatment strategies. This approach ensures better outcomes, minimizes adverse interactions, and supports the patient’s overall health journey.

Conclusion

AI-driven tools are transforming chronic disease management by streamlining care, enhancing efficiency, and improving outcomes. For conditions like diabetes, which heavily burden the healthcare system, AI offers early detection, personalized education, and streamlined prescription management, fostering better adherence and reducing costs.

By automating administrative tasks and integrating data, AI allows clinicians to focus more on patient care, creating tailored treatment plans that address unique needs and minimize adverse interactions. These innovations not only empower patients to manage their health effectively but also support clinicians in delivering proactive, patient-centered care.

As healthcare continues to evolve, AI stands out as a critical solution for addressing rising demand, improving experiences, and promoting sustainable, cost-effective care delivery.

Shaji Nair is CEO of Friska.AI, which offers an innovative healthcare platform that leverages advanced AI and mobile technology to enhance patient care by empowering individuals to partner with their physicians to take control of their health journey.