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The Top 10 Use Cases for Generative AI in Healthcare

From fast-track diagnoses to identifying new drugs, generative AI is transforming healthcare.

The healthcare industry is reaping the rewards of GenAI advancements across various use cases.

Some use cases drive cutting-edge medical innovation, like expedited image processing for disease detection and identifying new drugs and treatments.

Others automate administrative tasks—like dealing with insurance and taking notes—to make busy hospitals run more effectively.

If any industry could use generative AI advances to simplify and expedite processes, it’s healthcare. Doctors, nurses, and support staff take marathon shifts to provide around-the-clock services, often without the necessary resources or human capital to function efficiently. 

Luckily, we’re already seeing substantial progress.

Major health providers, tech companies, and well-funded startups have released a proliferation of internal and patient-facing AI applications that make the jobs of health practitioners, researchers, insurance providers, and back-office support that much easier. From analyzing images to fast-tracking diagnoses to identifying new drugs and treatment options to a slew of automated administrative capabilities—GenAI is transforming the healthcare industry.

Below, we lay out 10 of our favorite use cases from companies leading the charge:

1. Clinical note-taking

Organizing vast quantities of patient notes and histories is a time-intensive, manual process—and one ripe for AI automation. Abridge bills itself as “the most trusted AI-powered clinical conversation platform, purpose-built for healthcare.” The company’s enterprise tech generates real-time clinical notes during patient visits, structured in the industry’s preferred formats (History of Present Illness, Assessment & Plan, Physical Exam, and more).

GenAI is transforming the healthcare industry.

Crucially, Abridge secured a partnership with Epic Systems to become the ubiquitous healthcare software company’s provider of choice for AI-powered medical note-taking. By integrating Abridge’s GenAI for clinical documentation into Epic’s electronic health record workflow, doctors can fully utilize Abridge Enterprise without leaving Epic. Patients can also capture and share detailed notes from visits via the Abridge app and use the tech to learn more about diagnoses and medications.

2. Scheduling appointments

Scheduling a doctor’s appointment is often a hassle, and in 2024, there’s no need to play phone tag with an overworked receptionist. Enter AgentifAI, the AI scheduling platform tailored to healthcare.

With the help of an AI assistant named Alice, providers can offer patients personalized scheduling services that integrate within existing scheduling APIs. Alice can handle thousands of simultaneous conversations and identify specific requests across appointments, exams, and health insurance policies to ensure patients get the help they need—no more “please connect me to an actual person” requests to sub-par automated bots.

3. Drug discovery and research 

Identifying new drugs can take years using conventional methods, but AI advances can greatly expedite the process. AlphaFold is an AI system developed by Google DeepMind that generates 3D protein structures from amino acid sequences—researchers have already used the tech to identify hundreds of thousands of potential new psychedelics. It can predict a protein’s shape in milliseconds down to atomic accuracy; the technology, per Google, is recognized “as a solution to the grand challenge of protein-folding by CASP (Critical Assessment of Protein Structure Prediction), a community for researchers to share progress on their predictions against real experimental data. At CASP 14 (in 2020), we presented our latest version of AlphaFold—which displayed a level of accuracy so high that the community considered the protein–folding problem solved.”

While some scientists are skeptical of the technology's potential to change the drug discovery field, Isomorphic Labs—DeepMind's drug-discovery spin-off—recently announced deals worth up to $2.9 billion to hunt for drugs using machine-learning tools such as AlphaFold.

4. Insurance claim submissions and appeals

Nobody likes dealing with insurance companies. Amperos Health is one of a number of companies putting AI to work to manage the process, building AI agents that handle healthcare claim collections and denial management.

Nobody likes dealing with insurance companies.

The company’s first AI, named Amanda, calls insurance on clinics' behalf to check claim statuses, understand why certain claims are denied, and generate cover letters that reference payor policies for claim appeals. For those who wish to handle the intricate details, Amanda can even queue up the call and let you take over when a human is on the other end. Amperos is currently raising a pre-seed but is discussing implementation with multiple providers at a $2 million contract value. With time, the company plans to expand its roster of AI coworkers and use cases.

5. AI-powered echocardiograms

Those suffering from heart conditions may not even be aware a problem exists—and some of those who do may still eschew in-person scans for financial reasons. Caption Care claims to be the first company to integrate AI and ultrasound technologies to drive early disease detection and management, focusing on heart health. The company’s AI-powered software for cardiovascular imaging analysis allows doctors to perform echocardiograms (a type of ultrasound) more cost-effectively when and where patients need it—even from home.

The platform also uses deep learning algorithms to help providers understand the results of echocardiograms and detect cardiac conditions before they become a problem. This saves time, money, and, potentially, even the lives of at-risk patients who may otherwise not make a hospital visit for a scan.

6. Health-centric chatbots

Right now, ChatGPT and other GPT-4-powered chatbots are the go-to source for patients who want to leverage GenAI to learn more about medical conditions, symptoms, and treatments. However, these models are only as smart as their training data, which may include incorrect or misleading information, a huge liability for healthcare advice. Hippocratic AI, the “first safety-focused LLM for healthcare,” invests heavily in acquiring evidence-based healthcare content that standard LLMs lack. It outperformed GPT-4 on 105 of 114 tests and certifications—by a margin of 5% or more on 74 and 10% or more on 43.  The company has already raised $50 million in a seed round led by General Catalyst and Andreessen Horowitz.

Hippocratic AI invests heavily in acquiring evidence-based healthcare content that standard LLMs lack.

7. Data analysis and insight tools

Healthcare organizations tend to collect massive amounts of data but only have so much bandwidth to interpret and implement it. Iodine’s proprietary AI engine, CognitiveML, works to change that by analyzing raw clinical data at scale and providing actionable insights and predictions providers can use to improve care delivery, boost financial stability, automate administrative tasks, and allocate resources more efficiently.

According to the 2020 and 2022 Iodine Cohort Studies, health organizations using Iodine (including more than 900 hospitals) generate $1.5 billion in additional appropriate reimbursements annually.

8. Career marketplace for nurses

The nursing industry currently faces a shortage of workers due to high turnover and inequitable working conditions, among other issues. Incredible Health, the “first career marketplace to deploy generative AI,” expedites the hiring process for nurses by instantly processing resumes and applications to match job openings across specialties with qualified candidates. This has driven a 20% boost in interview acceptance by nurses, which speeds up hires and reduces “days to fill” at hospitals from the national average of 82 days to less than 20.    

The company offers a "resume wizard" in its mobile app that nurses can use for free, automated resume creation in less than five minutes. Meanwhile, providers can utilize Incredible Health to generate customized messages that detail hospital benefits, perks, and other job elements candidates wish to know. More than 800,000 nurses and 750 hospitals are already using the platform nationwide.

9. AI co-pilot for caregiving

Busy doctors may only spend 10-15 minutes with a patient, making it especially difficult to provide in-depth, individually tailored care. Rhythm AI has built “the world’s first generative AI precision care platform for doctors to deliver hyper-personalized interventions and compassionate care,” easing the burden. The system essentially functions as an AI co-pilot—RhythmX AI’s algorithms generate patient-specific recommendations for holistic care, lab tests, imaging, social care, follow-ups, and medications that can guide doctor actions. 

Busy doctors may only spend 10-15 minutes with a patient.

The platform was launched and funded by an initial $50 million investment by SAIGroup, a leading private investment firm focused on businesses with the potential to grow into enterprise AI leaders. This allows RhythmX AI to tap into SAIGroup’s data on 300 million patients, 4.4 billion annual claims, and 1.8 million healthcare professionals across 300,000 facilities.

10. Medical imaging analysis 

Through its Siemens Healthineers subsidiary, Siemens is leveraging generative AI to assist doctors in analyzing medical scans to detect potential abnormalities. This speeds up diagnoses and treatment plans across a variety of conditions. The AI-RAD Companion provides automatic post-processing of medical imaging datasets while reducing the burden of repetitive tasks. So not only can doctors diagnose more precisely, but they also have more time to focus on critical issues rather than routine workflows.

The company’s flagship supercomputer, Sherlock, is one of the healthcare industry’s most powerful AI development platforms—allowing Siemens Healthineers experts to conduct more than 1,400 daily experiments to drive continuous progress.

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