Healthcare is one of the most promising and profitable fields for GenAI innovation. More than 70% of healthcare organizations are currently exploring or have already adopted generative AI capabilities, with some already experiencing a measurable impact. According to a recent survey by PYMNTS Intelligence, nine in 10 C-suite executives expect a positive return on investment (ROI), and most plan to ramp up their Generative ai investments this year.
These investments range from the most important ones like product innovation to customer interactions. But in order to get a true sense of its worth, we have to dig deeper. Let’s take a look at 6 high-impact GenAI use cases in healthcare that are making a measurable difference.
Understanding the potential of generative AI
Generative AI is distinct from traditional AI. While conventional models analyze data and make predictions, generative AI creates new content— be it text, images, audio, or even molecular structures.
Generative AI relies on complex machine learning models, known as deep learning models, that aim to mimic learning and decision-making processes comparable to those of the human brain. These models, which are a subtype of machine learning, are particularly suited to handling huge data sets and demanding tasks.
Here’s how it works in healthcare.
- Medical data training: These models are fed a variety of data sets, including medical records, clinical notes, diagnostic images, and genetic data, to help them understand complex relationships.
- Pattern recognition: AI detects patterns and correlations in multimodal data (text, photos, etc.) that are imperceptible to the naked eye.
- Content generation: Using the learned patterns, the model creates new outputs, such as a patient summary, a simulated chemical structure, or predictive diagnoses.
Let’s take a look at the 6 most impactful GenAI use cases in healthcare, starting with the most important one.
The most impactful GenAI use cases in healthcare
Executives at healthcare companies are proactively implementing GenAI for a variety of internal and external applications. However, two domains stand out as common applications of GenAI in healthcare.
The first is enhanced chatbots and customer self-help platforms that provide automated, real-time responses to customer queries. The second is a broad area of product and service innovations. This can include research and development, idea generation and brainstorming, or other uses that support new business ventures.
Let us go through them one by one.
Better patient engagement with GenAI-powered virtual assistants
Patient engagement has never been an easy task. Long wait times, fragmented communications and unanswered queries create gaps in care. The traditional AI models are not meeting the needs of the patient, who needs fast and accurate answers.
Generative AI bridges the gaps by allowing the implementation of virtual assistants and smart chatbots. They are not just keyword-triggered scripts. They utilize large language models (LLMs) that are trained on healthcare data to interpret patient questions and respond accurately in real time.
The Mayo Clinic, for instance, employs generative AI to generate responses to patient queries. The chatbots complete tasks like scheduling appointments, responding to most frequent concerns, and offering health information with ease, greatly enhancing patient engagement and satisfaction.
Reducing administrative burnout
Doctors and nurses spend nearly 28 hours a week on paperwork that could be better spent interacting with patients. These responsibilities, which range from completing insurance paperwork to recording patient histories, lead to burnout and operational inefficiencies.
GenAI can reduce this workload by automating healthcare documentation. For example, it can create progress notes, discharge summaries, and even pre-populate prior authorization documents.
According to a survey by Google Cloud and Harris Poll, 91% of providers and 97% of payers believe GenAI is a promising tool for reducing administrative costs. Companies like Nabla are already using GenAI copilots to transcribe conversations between patients and physicians and generate clinical summaries, reducing the effort associated with paperwork and increasing accuracy.
Accelerating drug discovery
Drug discovery is a long and expensive process. It can take more than a decade and cost between $1 and $2 billion to bring each drug to market, and only 10% of candidate molecules make it to clinical trials. Generative AI is changing that.
Generative AI can speed up research and save lab time by predicting how a drug will interact with human biology by modeling millions of molecular structures. For example, human DNA has a sequence of three billion letters that forms a language of its own. The building blocks of life, proteins, also have their own alphabet made up of 20 amino acids. These languages can be understood by generative AI, helping in the research and creation of new drug treatments.
The technology is already delivering results. Etcembly, an Oxford biotech company, used generative AI to create the world’s first immunotherapy drug. Meanwhile, Insilico Medicine said its novel generative AI-designed COVID-19 drug — effective against all variants — has begun Phase I clinical trials. Once accepted, the oral drug would provide “a promising alternative” to conventional antiviral therapies.
Streamlining clinical trials with synthetic data
A recurring challenge in clinical trials is finding eligible and diverse patient populations. Finding patients who fit trial criteria is time-consuming, and this often leaves out those individuals who might benefit most from novel therapies.
This is addressed by generative AI, which creates data-driven models of synthetic patient populations that closely resemble real patients. Researchers can generate and analyze virtual cohorts to simulate trial outcomes, detect potential safety concerns, and test hypotheses before enrolling a single participant.
Not only does this capability speed up stages of trials, but it also enhances inclusion by duplicating underrepresented patient groups, leading to stronger and fairer clinical results.
The potential of applying generative artificial intelligence to build synthetic data in different hematologic neoplasms is compellingly demonstrated, for example, in a research article published in the journal JCO Clinical Cancer Informatics.
Enhancing medical imaging and diagnostics
Medical imaging generates large volumes of data; an MRI can generate hundreds of images. Radiologists are time-constrained and handling an increasing number of complicated cases, increasing the risk of missing abnormalities in life-threatening diseases like cancer. According to the National Cancer Institute, screening mammograms miss about 20% of all breast cancers.
Generative AI improves medical imaging by processing images at unprecedented speeds and accuracy. At Massachusetts General Hospital, generative AI models are more accurately detecting minor abnormalities in mammograms, enabling early diagnosis of diseases such as breast cancer.
Beyond identification, generative AI improves image quality by reducing noise and producing better visualizations, allowing clinicians to prioritize complex cases and minimize the burden of routine analysis.
Simplifying complex workflows with RAG
Healthcare workflows often rely on information spread across multiple systems, leading to errors, delays, and administrative inefficiencies. Traditional process automation helps, but it struggles to perform context-dependent tasks.
Generative AI, when combined with retrieval-augmented generation (RAG), bridges this gap. AI systems can dynamically assist with activities such as prior authorizations, patient eligibility checks, and compliance audits by accessing real-time information and producing context-sensitive results.
This technique simplifies information-heavy workflows, eliminates manual involvement, and speeds up decision-making without compromising on accuracy.
Also Read: How to Implement RAG Pipeline Using Spring AI
GenAI challenges in healthcare (and how to solve them)
While current-generation AI models are amazing, everyone who works with them knows they come with their fair share of problems.
Hallucinations, biases, data privacy, and data availability issues are not just technical problems, but real problems that have the potential to sabotage even the most promising GenAI use cases in healthcare. Let’s examine these challenges and their solutions.
Hallucinations
Hallucinations are one of the most discussed (and annoying) problems with generative AI. Sometimes the model produces a result that seems convincing but is completely wrong. Think of medical advice that could be harmful.
What causes this to happen? Generative AI makes predictions based on the facts it has been trained on; it doesn’t “know” anything. The model starts generating predictions to fill in the blanks if that data is unclear or missing.
Solutions
- Use RAG to anchor responses to current, verifiable data. This reduces hallucinations by allowing your model immediate access to true facts.
- Allow human reviewers to examine AI-generated results in high-risk industries such as healthcare. This hybrid strategy strikes a balance between speed and accuracy.
- Continuously fine tune models using edge cases and mistakes to help them understand where they failed.
Data privacy
Data is essential for generative AI, but not all data should be shared. Considering laws like GDPR and HIPAA, improper handling of personal data is not only dangerous, but also prohibited. Another source of concern is the emergence of agent AI, or models that make decisions without human intervention.
Solutions
- Create realistic but anonymous training data to preserve model performance while protecting user identities.
- Use privacy-preserving strategies to protect sensitive data while it is being processed, such as homomorphic encryption and differential privacy.
- Ensure strict control over who has access to and uses data, particularly when incorporating third-party AI models.
Data availability
AI models consume a lot of data, but as Elon Musk has stated, we are approaching the point where we have “exhausted” human-generated data. Without diverse and high-quality data, models become less accurate and biased.
Solutions
- Create AI-generated training data that resembles real-world trends without revealing sensitive or proprietary information.
- Collaborate with other companies to develop common data pools, especially in industries like healthcare, where data silos impede growth.
- To minimize stagnation, use automated processes to update models with the most up-to-date and relevant data.
Bias
Bias in AI is not a theoretical problem; it is a lived reality for those who face prejudice from biased algorithms. Not only does it harm people, but it also exposes your company to legal and reputational issues.
Solution
- Ensure that databases represent a diverse range of populations, experiences, and languages.
- Regularly assess your AI models for biased results and modify training processes as needed.
- Establish ethical rules that emphasize openness, accountability, and inclusion at all stages of development.
Sure, hallucinations, data privacy concerns, and bias seem like problems that can be solved with better algorithms or more data. But if there’s one reality about implementing generative AI, it’s that the toughest challenges aren’t technological; they’re human.
The talent crunch is real. Companies are struggling to hire people with practical experience in areas like:
- Foundation model fine tuning
- Data governance and privacy by design
- Bias detection and mitigation
And the further you go—on issues like confidential computing or responsible AI—the fewer experts there are.
In this case, outsourcing to the right AI development services provider is a wise move. Forward-thinking companies are bringing on board specialist partners who have already tackled these difficulties at scale.
Why choose outsourcing?
Outsourcing AI development isn’t a shortcut—it’s a strategy. Done right, it offers three major benefits:
Access to expertise. External teams, particularly those working on cutting-edge AI, bring field-tested frameworks to address challenges like hallucinations and data protection.
Faster deployment. While others are still hiring (or training) their internal teams, you’ve already started building, testing, and deploying.
Operational flexibility. Generative AI needs continuous oversight and adaptability. With the right partner, you don’t just build models—you also establish capabilities that grow along with your business.
The goal isn’t to delegate your responsibilities, but to increase your potential. And in a situation where the AI talent gap is only growing, that’s a competitive advantage you can’t afford to overlook.
Conclusion
Generative AI use cases in healthcare are focused on new possibilities, not small, incremental advances. It is changing the way healthcare organizations operate and deliver care, from drug discovery to patient engagement.
However, deploying models alone is not enough to harness its full potential. A methodical approach to AI workflow integration is necessary, maintaining transparency and prioritizing human insight.
Working smarter, not just faster, is the way of the future in the healthcare industry. And generative AI is already setting the standard.
Interested in learning how generative AI could fit into your healthcare workflows? Let’s discuss how to make it work for you, without compromising safety, ethics, or quality of care.
