Overview

Why use AI for healthcare?

Artificial intelligence (AI) and machine learning solutions are transforming the way healthcare is being delivered. Health organizations have accumulated vast data sets in the form of health records and images, population data, claims data and clinical trial data. AI technologies are well suited to analyze this data and uncover patterns and insights that humans could not find on their own. With deep learning from AI, healthcare organizations can use algorithms to help them make better business and clinical decisions and improve the quality of the experiences they provide.

Benefits of AI in healthcare

Providing user-centric experiences

Using large datasets and machine learning, healthcare organizations can find insights faster and more accurately with AI, enabling improved satisfaction both internally and with those they serve.

Improving efficiency in operations

By examining data patterns, AI technologies can help healthcare organizations make the most of their data, assets and resources, increasing efficiency and improving performance of clinical and operational workflows, processes, and financial operations.

Connecting disparate healthcare data

Healthcare data is often fragmented and in various formats. By using AI and machine learning technologies, organizations can connect disparate data to get a more unified picture of the individuals behind the data.

Use cases

AI in healthcare use case: Natural language processing

When subject matter experts help train AI algorithms to detect and categorize certain data patterns that reflect how language is actually used in their part of the health industry, this natural language processing (NLP) enables the algorithm to isolate meaningful data. This helps decision makers find the information they need to make informed care or business decisions quickly.

Healthcare payers

For healthcare payers, this NLP capability can take the form of a virtual agent using conversational AI to help connect health plan members with personalized answers at scale.

Government health and human service professionals

For government health and human service professionals, a case worker can use AI solutions to quickly mine case notes for key concepts and concerns to support an individual's care.

Clinical operations and data managers

Clinical operations and data managers executing clinical trials can use AI functionality to accelerate searches and validation of medical coding, which can help reduce the cycle time to start, amend, and manage clinical studies.

See how medical coding with AI works (video, 00:48)

isometric of a cube in shades of purple

AI for healthcare payers

Answer questions at scale for members, providers and call center agents.

a woman looking at the camera
Play Icon

Sonoma County and IBM Working Together to Change Lives

See how AI helped one California county serve their citizens

Clinical decision support

How AI in healthcare accelerates clinical decisions

Inundated with massive volumes of health data and growing responsibilities, clinicians are struggling to find the time to keep up with the latest medical evidence and still provide patient-centered care. By applying machine learning technologies to the latest biomedical data and electronic health records, healthcare professionals can quickly mine accurate, relevant, evidence-based information that has been curated by medical professionals. Some AI-powered clinical decision support tools feature natural language processing and domain-based training – enabling users to type questions as if they were asking a medical colleague in everyday conversation and receive fast, reliable answers.

Medical imaging

How AI in healthcare supports medical imaging

By supplementing labor-intensive image scanning and case triage, AI solutions used in medical imaging enable cardiologists and radiologists by surfacing relevant insights that can help them identify critical cases first, make more accurate diagnoses and potentially avoid errors while taking advantage of the breadth and complexity of electronic health records. A typical clinical study can produce vast datasets containing thousands of images, leading to incredible amounts of data in need of review. Using AI algorithms, studies from across the healthcare industry can be analyzed for patterns and hidden relationships, which can help imaging professionals find critical information fast.

Health equity

How AI in healthcare can support health equity

The healthcare IT industry has a responsibility to create systems that help ensure fairness and equality in data science and clinical studies, which leads to optimal health outcomes for everyone. AI and machine learning algorithms can be trained to help reduce or eliminate bias by promoting data diversity and transparency to help address health inequities. For example, minimizing bias in healthcare research can help combat health outcome disparities based on gender, race, ethnicity or income level.

AI adoption

The challenges of adopting AI in healthcare

There are challenges to adopting AI in healthcare, including having to meet regulatory requirements and overcoming trust issues with machine learning results. Despite these challenges, bringing AI and machine learning to the healthcare industry has brought numerous benefits to healthcare organizations and those they serve alike. AI improves operations by streamlining workflows and helping with mundane tasks, as well as by helping users to quickly find answers to their pressing questions, leading to better experiences for patients, members, citizens and consumers.

Case studies

Deeper insights with AI

Hardin Memorial Health adopted an AI solution to help radiologists make faster, more informed care decisions and maximize their EHR investments.

Improving efficiency and care with AI

TidalHealth Peninsula Regional improved efficiency, care and overall adoption of clinical decision support by integrating AI-powered search into its EHR.

Beneficial integration

Sonoma County worked with IBM to find ways to address the needs of the most vulnerable individuals and families by better coordinating case workers and counselors.

Resources

AI to drive operational efficiency

Read how artificial intelligence solutions are helping medical professionals solve healthcare problems.

AI to support patient care

From diagnosis to treatment, AI technology helps clinicians make better-informed decisions through data.

Pharma's shift to broad data and AI

See how life science companies can scale their use of AI to employ more varied data types and take advantage of broad data.

Healthcare technology

See how technologies such as AI, blockchain and cloud are changing how healthcare is delivered.

Interoperability in healthcare

Interoperability solutions for the healthcare industry let organizations manage, view, analyze and share vital health data.

Clinical decision support and AI

Read the results of two studies that show how AI-infused clinical decision support is helping to benefit care experiences.

Solutions

Clinical decision support

Explore solutions that can help healthcare providers keep up with the latest clinical knowledge and deliver personalized, evidence-based care with efficiency.

Life sciences technology

Overcome new treatment challenges by making trials more efficient, using better data sets and showing evidence-based value.

Employer benefits management

Using AI to make better decisions when designing employment benefits plans leads to healthier and more productive people.

Enterprise imaging

Transforming imaging solutions through AI lets you manage more images, collaborate more efficiently and deploy the right imaging applications.

Payer solutions

Healthcare payers need a data and analytics strategy to drive competition, build offerings and engage customers.

DynaMed and Micromedex with Watson

Evidence-based drug and disease content, AI-powered search and cloud-based tools – with the convenience of a single, point-of-care solution suite.

Population health management

Unlock the power of your data to help improve quality, safety and population health management.