5 ways to use generative AI in healthcare

Generating high-quality medical images. Hospitals can employ generative AI tools to enhance the traditional AI’s diagnostic abilities. This technology can convert poor-quality scans into high-resolution medical images with great detail, apply anomaly detection AI algorithms, and present the results to radiologists.

Diagnosing diseases. Researchers can train generative AI models on medical images, lab tests, and other patient data to detect and diagnose early onsets of different health conditions. These algorithms can spot skin cancer, lung cancer, hidden fractures, early signs of Alzheimer’s, diabetic retinopathy, and more. Additionally, AI models can unveil biomarkers that can cause particular disorders and predict disease progression.

Answering medical questions. Diagnosticians can turn to generative AI in healthcare if they have questions instead of looking for an answer in medical books. AI algorithms can process large amounts of data and generate answers fast, saving doctors’ precious time.

Designing and generating new molecules with desired properties that researchers can later evaluate in lab settings

Predicting properties of novel drug candidates and proteins

Generating virtual compounds with high binding affinity to the target that can be tested in computer simulations to reduce costs

Forecasting side effects of novel drugs by analyzing their molecular structure

Extract data from patients’ medical records and populate the corresponding health registries. Microsoft is planning to integrate generative AI into Epic’s EHR. This tool will perform various administrative tasks, such as replying to patient messages.

Transcribe and summarize patient consultations, fill this information into the corresponding EHR fields, and produce clinical documentation. Microsoft’s Nuance integrated generative AI tech GPT-4 into its clinical transcription software. Doctors can already test the beta version.

Generate structured health reports by analyzing patient information, such as medical history, lab results, scans, etc.

Answer doctors’ queries

Find optimal time slots for appointment scheduling based on patients’ needs and doctors’ availability

Generate personalized appointment reminders and follow-up emails

Review medical insurance claims and predict which ones are likely to be rejected

Compose surveys to gather patient feedback on different procedures and visits, analyze it, and produce actionable insights to improve care delivery

Bias. AI models’ performance is as good as the dataset they were trained on. If the data does not fairly represent the target population, this will leave room for bias against less represented groups. As generative AI tools train on vast amounts of patient records data, they will inherit any bias present there, and it will be a challenge to detect, let alone eradicate it.

Regulations. Are there any regulations governing the use of generative AI in healthcare? There are several laws that protect patient data, such as GDPR in Europe and HIPAA in the US. They enforce privacy, security, and compliance.

The more recent and AI-specific regulation—the EU AI Act—came into force on the 1st of August, 2024. It establishes a risk-based regulatory framework for the use, development, and deployment of AI across all sectors, including healthcare. This act classifies AI systems by risk: unacceptable, high, limited, and minimal. Healthcare-related applications, especially those involved in diagnosis, treatment, or emergency triage, fall into the high-risk category. Healthcare facilities operating in the EU have to implement rigorous validation processes to comply.

There is no equivalent regulation in the US. The Biden Administration’s Executive Order on Safe, Secure, and Trustworthy Development and Use of AI introduces some broad AI governance principles, but not on the same scale

Accuracy concerns. AI does make mistakes, and in healthcare, the price of such mistakes is rather high. For instance, LLMs can hallucinate. Meaning they can produce syntactically probable outcomes that are factually incorrect. Healthcare organizations will need to decide when to tolerate errors and when to require the AI model to explain its output. For instance, if generative AI is used to assist in cancer diagnosis, doctors are unlikely to adopt such a tool if it can’t justify its recommendations.

Accountability. Who is responsible for the final health outcome? Is it the doctor, the AI vendor, the AI developers, or yet another party? Lack of accountability can have a negative impact on motivation and performance.

Prepare your data. Even if you decide to opt for a pre-trained, ready-made Gen AI model, you might still want to retrain the LLM on your proprietary dataset, which needs to be of high quality and representative of the target population. Keep medical data secure at all times and safeguard patient privacy. It would be useful to disclose which dataset an algorithm was trained on, as it helps to understand where it will perform well and where it might fail.

Take control of your AI models. Cultivate the concept of responsible AI in your organization. Make sure people know when and how to use the tools and who assumes responsibility for the final outcome. Test the generative AI models on use cases with limited impact before scaling to more sensitive applications. As mentioned earlier, generative AI can make mistakes. Decide where a small failure rate is acceptable and where you can’t afford it. For instance, 98% accuracy can suffice in administrative applications, but it’s unacceptable in diagnostics and patient-facing practices. Devise a framework that will govern the use of generative AI in healthcare at your hospital.

To reduce hallucination in LLMs, you can deploy retrieval-augmented generation (RAG). This AI architecture dynamically extracts relevant, recent information from different knowledge sources to augment a standard LLM’s response.

Help your employees accept the technology and use it. AI still needs human guidance, especially in the heavily regulated healthcare sector. Human-in-the-loop remains an essential ingredient for the technology to succeed. The medical and administrative staff will be expected to supervise AI models, so hospitals need to focus on training people for this task. Employees, on the other hand, should be able to reinvent their daily routine, now that AI is a part of it, to use the freed-up time to produce value.

Find a reliable Gen AI development partner who will assist you every step of the way. At ITRex, we offer an AI/Gen AI readiness assessment to make sure your company can take full advantage of the technology. We also offer an AI proof of concept (PoC) service that allows you to experiment with AI on a small scale before committing your resources to a full-blown project.

Generative AI in healthcare can analyze large volumes of medical data, such as imaging scans, lab results, and patient histories, and highlight patterns that may indicate early signs of diseases like cancer or Alzheimer’s. It doesn’t replace doctors but acts as a powerful support tool, helping clinicians detect conditions earlier, make more accurate diagnoses, and personalize care plans.

Gen AI in healthcare can help draft responses and suggest treatment options, but it should never operate without human oversight. Clinicians must review and approve all AI-generated outputs to ensure accuracy, safety, and alignment with medical guidelines. Used this way, AI can save time without putting patients at risk.

If the data used to train AI systems is incomplete or not diverse enough, the models may produce biased results. This could mean underdiagnosing or misdiagnosing certain patient groups. To reduce this risk, healthcare organizations must use high-quality, representative data and regularly test AI systems for fairness.

Hospitals should establish clear governance frameworks that include regular audits, data quality checks, and compliance with regulations such as GDPR, HIPAA, and the EU AI Act. Medical facilities should also dedicate time to training staff on how to use AI responsibly and be transparent with patients about how AI contributes to their care.

Yes. AI can support clinical decisions, but it can’t replace human judgment. Oversight ensures that recommendations are interpreted correctly, ethical considerations are applied, and patient safety is protected. In healthcare, AI works best as a partner—not a substitute—for skilled professionals.