AI and the Future of Precision Medicine

Healthcare today still largely operates using a “one-size-fits-all” model that assumes all patients will respond similarly to the same interventions. Examples of this “one-size-fits-all” approach are found everywhere in healthcare. They include medications prescribed in the same dosage for all patients despite differences in metabolism, weight, genetics, etc., as well as health screening guidelines (like mammograms at a certain age) that are based on population-wide averages rather than personalized risk assessment. 

By definition, standard treatment guidelines recommend the same clinical protocols for all patients rather than tailored plans based on each patient's unique characteristics. And public health recommendations, such as vaccination schedules, dietary guidelines, and exercise recommendations, are typically designed to apply to the majority of the population without customization.

Until recently, this approach to healthcare was preferred, as medicine lacked the capability to determine the genetic and other “omic” differences that exist among individuals, to understand how those differences might affect patient responses to different treatment approaches, or to develop therapies that could be designed to target patients’ genetic variations.

So, the emergence of precision medicine represented a paradigm shift from traditional medical practices. By leveraging genetic, environmental, and lifestyle data, precision medicine aims to identify the most effective treatment strategies for various patient subgroups—and ultimately for every individual patient. This personalized approach not only helps enhance the efficacy of treatments but also supports minimizing the potential for adverse side effects. 

Precision medicine is most advanced in oncology and the treatment of rare diseases, areas where our Oracle research services division is a leader in data intelligence and developing oncology decision support tools. For some time now, the genomic profiling of tumors has enabled oncologists to identify specific mutations and select what they believe to be the most effective treatments to target those mutations, which can lead to enhanced outcomes and improved survival rates. Today, there are more than 100 targeted therapeutics being used to treat different types of cancer, with more in development. [1]

However, the vast and complex data involved in precision medicine pose significant analytical challenges. This challenge is where AI can become a game changer, offering advanced tools and algorithms to analyze and understand data more efficiently. By rapidly and accurately analyzing vast amounts of data—genetic information, patient history, lifestyle factors, and even real-time biometrics—AI can help support clinicians develop and tailor medical care options to individuals rather than relying on “one-size-fits-all” approaches.

At Oracle, we are using AI to help enable clinicians and researchers to enhance diagnostic capabilities that can help develop and tailor therapeutic intervention options with unprecedented granularity. AI technologies can identify patterns and correlations that may be beyond human capability, thus enabling clinicians to improve their predictions and diagnostic recommendations.

In precision medicine, AI can assist in various ways:

• Genomic analysis: AI algorithms can analyze genomic data to identify mutations and genetic markers associated with specific diseases. This information can guide the development of targeted therapies and help clinicians choose the most effective treatment plans.
• Predictive analytics: AI can facilitate clinicians evaluating disease progression and potential patient outcomes based on historical data. This information can enable clinicians to intervene earlier and help them develop personalized treatment plans, supporting the chances of more successful outcomes.
• Clinical intelligence: AI-powered clinical decision support systems, such as the new Oracle Health EHR, can provide near real-time recommendations to healthcare clinicians, helping them make evidence-based decisions about patient care. The Oracle Health EHR can be enhanced with Oracle Health Data Intelligence, which can integrate patient data from various sources, including wearable devices, medical imaging and lab tests, and patient histories from other health systems.
• Drug discovery: AI can help accelerate the drug discovery process by identifying potential drug candidates and better predicting their efficacy. This approach can help reduce the time and cost involved in bringing new drugs to market. 

Oracle is also using AI to bring enhanced precision to clinical trials by identifying optimal sites and matching patients to available trials for which they may qualify. This process helps accelerate recruitment, improve the likelihood of positive trial outcomes, and deliver more optimal experiences for patients, sites, and sponsors alike.

Predictive modeling tools, such as clustered regularly interspaced short palindromic repeats (CRISPR) AI, help enhance gene-editing precision, enabling targeted therapeutic strategies. In drug discovery, AI-driven molecular docking simulations and in silico modeling help reduce reliance on labor-intensive experimental screening.

AI can also be instrumental in the identification of biomarkers, which are biological molecules found in blood, other body fluids, or tissues that signify a disease process or condition. Machine learning (ML) algorithms can be used to analyze vast datasets to uncover patterns and correlations that might be missed by human researchers, potentially leading to the discovery of new biomarkers and the improvement of existing ones.

For example, AI-based liquid biopsy platforms may soon use ML algorithms to detect circulating tumor DNA (ctDNA) and epigenetic modifications, enabling earlier cancer detection and near real-time monitoring of tumor evolution. 

Integrating AI with EHRs like the new Oracle Health EHR, proteomic profiling, and real-world evidence can refine treatment selection and therapeutic stratification. Reinforcement learning models support optimized chemotherapy regimens, adapting dosages based on dynamic tumor response.

AI-driven digital pathology platforms aid in facilitating automated tumor grading, lymph node metastasis detection, and immunophenotyping, guiding precision immunotherapy. Pharmacogenomic AI models help predict adverse drug reactions based on single nucleotide polymorphisms (SNPs), supporting medication alignment with an individual’s metabolic profile, which can help reduce the likelihood of toxicity and therapeutic failure.

AI’s predictive power can extend into epidemiology and chronic disease management. Advanced models can analyze longitudinal health data to support clinicians identifying at-risk populations and guide them to develop preemptive interventions. AI-powered digital twins—virtual physiological representations of patients—can help understand disease progression and treatment response options, enabling clinicians to personalize preventive strategies.

We are rapidly moving toward a new era of medicine where AI can help clinicians:

• Identify treatment options to a person’s unique genetic and biological profile
• Identify potential diseases or conditions before the likelihood of certain symptoms, helping enable early intervention
• Optimize medication dosages based on a person’s metabolism, health record, and drug labeling or established guidelines
• Create personalized health plans based on near real-time data

Healthcare is the most important and most broken industry in the world. At Oracle, we're on a mission to fix it using the most advanced technologies available. We’re building a healthcare ecosystem unlike anything that has existed before, powered by cloud-scale infrastructure and AI, layered onto unified health data, including EHR data, genomics, financial data, and more.  This combined data enables clinical trials matching and population disease modeling transitioning from the current reactive health response to a proactive approach. We’re not just automating health systems; we are reimagining the entire landscape of health by leveraging truly longitudinal data to improve patient care and improve efficiency of researchers and health systems.

AI is already helping clinicians understand cancer biopsy slides, discover novel therapeutic targets, and comb through medical literature to better deliver insights directly to doctors at the point of care.  We’re integrating next-generation sequencing data and precision medicine tools to connect this complex and historically isolated data across the most critical points in a person's health journey. We can elevate the patient and provider experience using vast datasets in multiomics to give clinical teams understandable, actionable, data-backed support for the best options for each individual patient, such as helping identify the most effective medication or guiding cancer therapy options by providing outcomes data for therapies based on results for a cohort of similar cases. 

We expect that this powerful and intelligent platform will scale to gather data in new ways, capturing not just static genetic data, but continuously capturing complex multiomic data over time and measuring minute changes that can identify patterns, flag anomalies that busy doctors may miss, and make connections across vast datasets that humans are not capable of processing.

The potential of AI in precision medicine is revolutionizing healthcare, making the “one-size-fits-all" approach obsolete and leading to a new era of truly personalized medicine. Oracle is creating a remarkable ecosystem with the potential to change the future of medicine to be more precise and personalized with the help of AI.  And we’re not just imagining it, we’re building it.

[1] Targeted Therapy Drug List by Cancer Type - NCI