Insurance giant Cigna is deploying artificial intelligence to identify patients with chronic diseases like cancer and kidney disease before symptoms become severe, projecting the effort will save an estimated $200 million over the next three years by connecting people with clinicians earlier. The strategy reflects a broader shift in how healthcare organizations are thinking about AI, moving beyond simply adopting new tools to strategically leading in an AI-driven healthcare landscape.

What Problem Is AI Actually Solving in Healthcare?

Katya Andresen, Cigna's chief data, digital and AI officer, explained the company's philosophy when she joined in September 2021. Rather than asking "How do I use AI?," she said the real question should be "How do I lead in an age of AI?" This reframing shifts focus from chasing countless use cases to measuring whether AI investments actually change health outcomes.

"We've been on a mission to change that question to, 'how do I lead in an age of AI?'" said Katya Andresen, chief data, digital and AI officer at Cigna Group.

Katya Andresen, Chief Data, Digital and AI Officer, Cigna Group

For Cigna, which generates $275 billion in annual revenue and ranks 14th on the Fortune 500 list, the stakes are high. National healthcare spending exceeds $5 trillion annually, driven by rising chronic conditions, an aging population, and soaring hospital and prescription drug costs. AI offers a way to bend that cost curve by catching disease earlier.

How Is Cigna Using AI to Save Money and Improve Care?

Cigna has deployed AI across multiple healthcare challenges, each with measurable financial and clinical impact:

• Early Disease Detection: Predictive analytics tools help identify patients at risk for chronic conditions including cancer, kidney disease, and high-risk pregnancy, enabling proactive clinician outreach before conditions worsen.
• Administrative Burden Reduction: A $100 million investment through 2028 aims to use AI to reduce the time clinicians spend documenting cases and speed up the prescription process, freeing physicians from hours of electronic health record data entry.
• Biosimilar Education: AI analyzed thousands of prior customer conversations about biologics and biosimilars to craft targeted digital messaging encouraging patients to switch from expensive branded drugs to cheaper alternatives, resulting in over 80 percent adoption of the lower-cost option.
• Call Center Insights: Generative AI summarizes millions of phone calls to call center agents, then creates tools that help employees quickly find answers to common questions about policy coverage.
• Clinical Documentation: AI-enabled note-taking has reduced documentation time by up to 90 percent for health practitioners at Cigna's telehealth service MDLIVE.

The biosimilar campaign illustrates how AI can drive both cost savings and patient benefit. One biologic called Humira can cost a patient $7,000 per month to treat inflammatory and autoimmune conditions. By using AI to understand patient concerns about biosimilars, Cigna created messaging that convinced more than 80 percent of patients to switch to the cheaper alternative, generating "a couple hundred million dollars of savings for patients" while also improving Cigna's margins.

Why Are Patients Turning to AI for Health Information?

The shift toward AI in healthcare extends beyond insurance companies and into patient behavior. Nearly six in ten Americans report using AI to research health information before a doctor visit, and about 14 million adults say they have skipped a provider visit after using AI, according to a Gallup survey published in April. This trend raises questions about the guardrails around AI chatbots, particularly when they handle sensitive patient information and answer complex questions about medical insurance and treatments.

Andresen noted that Cigna's status as a highly regulated company provides built-in protections. "The good news is, because we are highly regulated, we have a massive amount of controls in place to begin with," she said. The company has maintained compliance and governance frameworks for machine learning models for well over a decade, and closely controls any data accessed by third-party vendors.

Andresen

How Is Personalized Medicine Reshaping Healthcare Through AI?

Beyond insurance and administrative applications, AI is transforming how clinicians diagnose and treat individual patients. The convergence of genomics, biomarkers, and artificial intelligence is enabling clinicians to tailor treatment to each patient's unique genetic and biological profile rather than applying a one-size-fits-all approach.

A study estimated that nearly 80 percent of the variation in drug response can be attributed to differences in an individual's DNA, accelerating the integration of genomic technologies into clinical practice. At Karolinska University Hospital, whole genome sequencing has become a standard diagnostic tool for patients with suspected rare genetic disorders. More than 15,000 patients have undergone testing, with approximately 23 percent receiving a genetic diagnosis, demonstrating how genomic analysis can shorten diagnostic journeys and enable earlier, more targeted interventions.

In cancer care, researchers from the University of Kansas Cancer Center, Emory University School of Medicine, and the Southwest Oncology Group recently identified blood-based biomarkers that can predict which women with hormone receptor-positive breast cancer are unlikely to benefit from chemotherapy. By identifying patients who are unlikely to respond, clinicians can avoid unnecessary treatment and its associated side effects while pursuing more effective therapeutic options.

AI is also enabling real-time clinical decision support. Researchers at Johns Hopkins University developed the Targeted Real-Time Early Warning System, now commercialized by Bayesian Health, which can detect sepsis between two and 48 hours earlier than conventional methods. The system has reduced sepsis mortality by 18 percent across several U.S. hospitals. Similarly, Aidoc's FDA-cleared CARE Foundation Model uses AI to identify multiple critical conditions from medical imaging within a single workflow, helping emergency departments prioritize patients who require immediate care.

What Challenges Do Healthcare Technology Professionals Face With AI Adoption?

For clinical engineers and healthcare technology management professionals who oversee medical devices and systems, AI adoption presents both opportunities and risks. The integration of AI into healthcare technology represents a fundamental shift in how hospitals diagnose, treat, and manage patient care, but it also introduces challenges related to transparency, bias, security, and accountability.

One significant risk is "automation bias," where clinicians become overly trusting of AI recommendations and fail to apply their own critical judgment. Over time, this could lead to a de-skilling of the workforce, where essential clinical expertise atrophies. Healthcare delivery organizations must balance the efficiency gains of AI with the need to maintain clinician oversight and decision-making authority.

Despite these challenges, the strategic value is substantial. Analyses from Morgan Stanley and McKinsey project AI could create up to $110 billion in annual value for the U.S. healthcare industry alone. AI drastically shortens drug development timelines by simulating interactions with biological targets, accelerating the journey from lab to bedside. Additionally, AI is democratizing healthcare by expanding access beyond physical hospitals, making high-level specialized expertise available in rural and underserved areas.

Steps for Healthcare Organizations to Implement AI Safely

• Establish Clear Governance: Define decision-making authority, oversight mechanisms, and accountability structures before deploying AI tools to ensure clinical teams maintain appropriate oversight and can intervene when necessary.
• Address Bias and Fairness: Audit AI models for demographic bias, validate performance across diverse patient populations, and ensure that algorithmic recommendations do not perpetuate healthcare disparities.
• Strengthen Cybersecurity: Implement robust security protocols to protect sensitive patient data accessed by AI systems, particularly when working with third-party vendors or cloud-based AI platforms.
• Plan for Legacy Systems: Develop integration strategies for aging medical devices and electronic health records that may not be designed to work with modern AI tools, ensuring a smooth transition without disrupting clinical workflows.
• Measure Clinical Impact: Focus AI investments on use cases with measurable outcomes rather than adopting tools for their own sake, tracking whether AI actually improves patient outcomes and reduces costs.

Andresen emphasized that the principles behind Cigna's AI strategy are straightforward: "What problem are we trying to solve, and how can AI help? That's always the starting point." This problem-first approach, combined with robust governance and measurement, appears to be the foundation for successful AI adoption in healthcare.

Andresen

As healthcare organizations continue to invest in AI, the focus is shifting from simply adopting new technology to strategically deploying it in ways that improve patient outcomes, reduce costs, and maintain clinician oversight. For Cigna and other healthcare leaders, the next phase of AI adoption will depend on their ability to balance innovation with responsibility, efficiency with oversight, and technological capability with human judgment.