The Benefits
AI is poised to help solve healthcare’s greatest challenges — the growing need for serious or complex care, increased rates of chronic disease, a shortage of healthcare workers, and the explosion of data and technology tools.
Augmenting Human Knowledge
AI technologies can process and synthesize data far more quickly than humans. This rapid analysis allows clinicians to gain actionable, personalized and predictive insights for individual patients. For instance, AI can analyze a patient's medical history, pathology reports and imaging scans in minutes to identify patterns and recommend tailored treatments.
Reducing Inefficiencies
By managing repetitive tasks, AI lets clinicians focus on patient care rather than administrative work. For example, ambient listening technology in exam rooms can generate clinical notes automatically, reducing the burden of paperwork. This shift allows healthcare professionals to spend more time engaging with patients and less time on documentation.
Improving Patient Outcomes
Incorporating AI into clinical workflows can enhance patient outcomes by detecting disease early and expediting drug discovery. AI's ability to analyze complex datasets allows for the identification of subtle indicators of disease, facilitating timely intervention. Additionally, AI-driven drug discovery processes can accelerate the development of new treatments, making them available to patients more quickly.
Healthcare’s
Under the broad umbrella of AI, there are several subfields. Machine learning enables systems to learn, adapt and make inferences by identifying patterns in data. Deep learning, a powerful subset of machine learning, mimics the way the human brain processes information to generate accurate insights and predictions. Natural language processing teaches machines to understand and produce human language and text. Cloud computing refers to the use of remote servers on the internet to store, manage and process data efficiently.
These subtypes work in concert to create medical algorithms — sets of rules or sequences designed to solve problems or inform decisions in patient care. Some of the most common algorithm types are:
Predictive AI Models
Predictive AI models analyze vast amounts of data to make predictions based on patterns and trends identified within that data. In healthcare, predictive AI can forecast patient outcomes, disease progression and potential complications by examining historical health records and current patient data. This allows healthcare professionals to make more informed decisions, tailor treatments to individual needs, and intervene proactively to prevent adverse events. Predictive AI leverages machine learning algorithms to continually improve its accuracy, adapting to new data and refining its predictions over time.
Generative AI Algorithms
Generative AI refers to a type of AI that can create new content based on patterns identified from the data on which it is trained. Using deep learning and machine learning, generative AI models understand and mimic the structure of training data to generate new content — from text and photos to technical content like computer code or individualized treatment plans for patients.
Agentic AI Algorithms
This is a type of AI that allows machines to work autonomously toward goals, adapting and learning as they go. This autonomy means the AI system can operate without constant human intervention, making its own decisions and taking actions based on its understanding of its environment and objectives.
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Deploying AI Solutions
Any AI solution that Mayo Clinic considers deploying into its practice must meet a highly rigorous standard of safety and ethics. As a founding member of the
Coalition for Health AI, Mayo Clinic has committed itself to responsibly developing and deploying AI solutions in healthcare. Additionally, Mayo Clinic’s
Digital Hippocratic Oath requires that within our walls, data, artificial intelligence and clinical knowledge are used for the sole purpose of improving healthcare for all.
The AI models Mayo Clinic staff develop are built and trained on vast and diverse datasets. The goal is to mitigate inherent bias, ensure algorithms are useful to as many patient populations as possible, and make sure algorithms do what they say they will.
Once an AI solution is developed and meets Mayo Clinic’s ethical and safety standards, the solution moves into the deployment stage. In order to be deployed, a solution must be able to do two things:
1. improve care delivery and the patient experience; and 2. seamlessly fit into clinicians’ daily workflows and enhance team efficiency, satisfaction and overall quality of care. Once a solution has proved it can do both things, it is deployed into clinical practice and patient care.
AI’s Future
As AI continues to evolve, its potential applications in healthcare continue to expand. From predictive analytics to personalized medicine, AI's transformative power is just starting to be realized. However, the successful integration of AI into healthcare will require ongoing collaboration between technology developers, healthcare professionals and policymakers.
Guided by Mayo Clinic’s primary value to put the needs of all patients first, the organization is leading in the discovery, validation and deployment of safe and ethical AI to transform medicine. As Mayo Clinic continues to innovate and refine these technologies, the promise of AI in healthcare becomes increasingly attainable, paving the way for a healthier, more efficient and more personalized medical landscape.
