Generative AI Use Cases in Healthcare

Generative AI is accelerating transformation in healthcare by automating documentation, enhancing diagnostic imaging, improving drug discovery, and supporting clinical and administrative workflows. Below are key use cases currently being deployed by healthcare systems and life sciences firms.

1 📋 Summary of Use Cases

Use Case	Examples	Impact	Underlying Models
Clinical Documentation	Nuance DAX, AWS HealthScribe	Nuance DAX reduces physician documentation time by up to 50%	LLM, Speech-to-Text
Medical Imaging & Diagnostics	MIT CSAIL, DeepMind’s EyeDiff	MIT CSAIL reduced false positives by 37%, biopsies by 27%, and surgeries by 30%	CNN, Diffusion models
Drug Discovery & Molecule Design	Insilico, BenevolentAI	Cuts lead time from 2 years to 6 months (Insilico); improves success rate to 25% (BenevolentAI)	VAEs, GNNs, RNNs, Transformers, RL
Patient Communication	Mayo Copilot, Ada Health	Improves patient experience (Mayo); 66% feel more confident, 40% less anxious (Ada); 81% clinician adoption (UCSD)	LLM, RAG, RL
Admin Workflow Automation	MedLM, Abridge, NexusMD	Reduces admin burden by ~28–36 hrs/week; reduces cognitive load on clinicians by 78%)	LLM, IDP, Workflow Automation Agents

2 Detailed Use Cases

2.1 Clinical Documentation

• Problem: Clinicians spend significant time on documentation, contributing to burnout and reducing time available for patient care. Manual entry can also introduce delays and inconsistencies.
• Solution: Generative AI tools like ambient scribing (e.g., Nuance DAX) and real-time note generation (e.g., AWS HealthScribe) automatically generate clinical notes, transcripts, and summaries from conversations — streamlining EHR workflows.
• Impact: Nuance DAX reduces physician documentation time by up to 50%, saving an average of 7 minutes per patient encounter and enabling 3–5 additional appointments per day^[1].

2.2 Medical Imaging and Diagnostics

• Problem: Medical imaging analysis for rare conditions or low-resolution scans can be difficult, time-consuming, and resource-intensive for radiologists.
  
• Solution: Generative AI improves diagnostic workflows in two key ways. First, it creates high-quality medical images that represent a wide range of conditions, patients, and scan types—using techniques like diffusion models (e.g., DeepMind’s EyeDiff). Second, it enhances image interpretation and helps reduce false positives through advanced pattern recognition models such as convolutional neural networks (CNNs), as demonstrated by MIT CSAIL.
  
• Impact: MIT CSAIL’s AI reduced false positives by 37.3%, biopsy rates by 27.8%, and unnecessary breast surgeries by over 30%^[2][3]; DeepMind’s diffusion-based EyeDiff model improved rare disease classification accuracy on retinal OCT scans by enhancing detection of minority classes and outperforming traditional oversampling techniques^[4].

2.3 Drug Discovery and Molecular Design

• Problem: Traditional drug discovery is time-consuming and expensive, often taking over a decade and billions of dollars to bring a new therapy to market.
  
• Solution: Generative AI platforms accelerate this process by using models like graph neural networks (GNNs), variational autoencoders (VAEs), recurrent neural networks (RNNs), and transformers to generate novel compounds, optimize drug-like properties, and prioritize candidates for development.
  
• Impact: Insilico Medicine advanced a novel drug candidate for idiopathic pulmonary fibrosis (IPF) to Phase II trials in under 30 months, including less than 18 months from target identification to candidate nomination^[5]; BenevolentAI has used GenAI to generate molecules targeting resistant diseases, with multiple candidates advancing to preclinical and clinical stages^[6].

2.4 Patient Communication and Education

• Problem: Patients often find it difficult to understand complex medical language, follow care instructions, or manage their treatment after discharge—leading to confusion, low adherence, and worse health outcomes.
• Solution: GenAI-powered assistants use large language models (LLMs) with retrieval-augmented generation (RAG) to simplify medical information, answer patient questions in plain language, and deliver personalized health education at scale.
  
• Impact: Mayo Clinic reports improved patient experience and more efficient discharge workflows through AI-assisted communication^[7]; Ada Health users showed significant engagement benefits—66% felt more certain about the care they needed, 40% reported reduced anxiety, and 80% felt better prepared for doctor consultations^[8]; At UC San Diego Health, physicians using AI-generated replies wrote longer, more empathetic messages with less cognitive effort ^[9].

2.5 Administrative Workflow Automation

• Problem: Healthcare organizations face rising administrative workloads—from prior authorizations to claims processing and medical coding—that consume staff time, and drive up costs.
• Solution: Generative AI streamlines these tasks by combining LLMs and document intelligence platforms to automate medical coding, summarize prior authorization requests, extract insurance policy details, and generate routine correspondence.
• Impact: Google’s MedLM, used in tools like Augmedix, automates clinical documentation to deliver faster, more accurate notes and reduce administrative burden. Google Cloud estimates that staff spend 28–36 hours per week on administrative tasks—time that GenAI can help reclaim^[10]; Abridge reports up to 78% reduction in clinician cognitive load and 86% drop in after-hours documentation ^[11].

3 Ethical and Regulatory Considerations

The use of Generative AI in healthcare offers immense potential, but it also raises complex ethical and regulatory challenges that must be addressed to ensure safety, trust, and fairness. Below are the key pillars guiding responsible deployment:

3.1 Transparency and Explainability

Generative models like large language models (LLMs) often operate as black boxes, making it difficult to trace how decisions are made. In healthcare, this lack of explainability is a critical barrier to adoption. The U.S. FDA’s AI/ML Software as a Medical Device (SaMD) Action Plan calls for real-world performance monitoring, clear labeling, and transparency in algorithm logic, especially for high-risk clinical applications ^[12]. Similarly, the European Commission’s ethics guidelines designate transparency as a foundational requirement for AI systems in medicine ^[13].

Best practice: Use explainable models or complement black-box systems with post-hoc interpretability tools (e.g., SHAP, LIME), and communicate limitations clearly to clinicians.

3.2 Bias Mitigation and Fairness

AI systems trained on healthcare data can inherit or amplify existing disparities. A landmark study published in Science revealed that a commercial algorithm systematically underestimated the needs of Black patients, leading to reduced care referrals ^[14]. The WHO urges developers to adopt inclusive data sourcing, perform subgroup analysis, and use fairness-aware learning techniques to ensure equitable performance ^[15].

Best practice: Conduct bias audits, stratify model evaluation by race, gender, and age, and iteratively improve fairness during retraining.

3.3 Privacy and Consent

Generative AI models may memorize and regurgitate sensitive patient information, posing serious risks under HIPAA and GDPR. Experts emphasize the need for differential privacy, federated learning, and strong access controls to protect PHI ^[16][17]. In addition, explicit informed consent should be obtained when AI is used in patient-facing workflows or influences clinical decisions ^[15].

Best practice: Train models on de-identified data, disclose AI involvement to patients, and obtain consent when appropriate.

3.4 Governance and Accountability

As AI becomes embedded in clinical workflows, clear accountability mechanisms are essential. Who is responsible if an AI-driven recommendation is wrong? The FDA’s Predetermined Change Control Plans and Total Product Lifecycle (TPLC) framework recommend ongoing oversight, version control, and human review ^[18].

Best practice: Maintain audit trails, define roles and responsibilities, and require human-in-the-loop verification for all high-impact decisions.

3.5 Model Drift and Continuous Validation

AI systems may become less reliable over time as clinical practices or populations evolve—a phenomenon known as model drift. Without regular validation, GenAI tools risk producing inaccurate or even harmful outputs.

Best practice: Set up monitoring pipelines to detect performance degradation and retrain models periodically using current data ^[18].

3.6 Human-AI Collaboration

Generative AI should augment, not replace, clinical judgment. Over-automation can erode clinician trust and lead to overreliance on AI outputs. Leading health AI researchers emphasize the importance of human-in-the-loop design to ensure clinicians remain central to the decision-making process ^[17][19].

Best practice: Embed AI into workflows in a way that supports clinician expertise, rather than bypassing it.

3.7 Conclusion

Taken together, these ethical pillars—transparency, fairness, privacy, governance, drift monitoring, interoperability, and human-AI collaboration—form the foundation for trustworthy and effective use of GenAI in healthcare. Responsible design and deployment not only reduce risks but also build the confidence needed for widespread adoption.

4 🧾 Key Takeaways

• Generative AI is transitioning from pilots to production across healthcare, with mature applications in clinical documentation, medical imaging, and drug discovery.
  
• Providers, insurers, and biotech firms are leveraging GenAI to automate back-office tasks, accelerate research, and improve decision-making—delivering measurable time and cost savings.
  
• Patient-facing tools for education, triage, and communication are enabling more personalized, accessible, and scalable care experiences.
• As adoption increases, organizations must address critical concerns around transparency, bias, privacy, and accountability to ensure responsible and equitable deployment of GenAI in clinical settings.

5 📚 References

[1] Microsoft Nuance. Move beyond scribes to automatically document care https://www.nuance.com/content/dam/nuance/en_us/collateral/healthcare/infographic/ig-move-beyond-scribes-to-automatically-document-care-en-us.pdf

[2] MIT CSAIL News. Using AI to improve early breast cancer detection. https://www.csail.mit.edu/news/using-artificial-intelligence-improve-early-breast-cancer-detection

[3] Liu et al. (2021). Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. https://pmc.ncbi.nlm.nih.gov/articles/PMC8463596/pdf/41467_2021_Article_26023.pdf

[4] De Fauw et al. (2024). EyeDiff: text-to-image diffusion model improves rare eye disease diagnosis. https://arxiv.org/abs/2411.10004

[5] Insilico Medicine. First Generative AI Drug Begins Phase II Trials with Patients. https://insilico.com/blog/first_phase2

[6] The Pharmaceutical Journal. How AI is transforming drug discovery. https://pharmaceutical-journal.com/article/feature/how-ai-is-transforming-drug-discovery

[7] Mayo Clinic. AI Improves Patient Experience. https://mayomagazine.mayoclinic.org/2025/04/ai-improves-patient-experience/

[8] Ada Health. Improving patient pathways with AI. https://about.ada.com/improving-patient-pathways-with-ada-digital-triage/

[9] UC San Diego Health. Study Reveals AI Enhances Physician-Patient Communication. https://health.ucsd.edu/news/press-releases/2024-04-15-study-reveals-ai-enhances-physician-patient-communication/

[10] Google Cloud. MedLM: Foundation Models for Healthcare. https://cloud.google.com/blog/topics/healthcare-life-sciences/introducing-medlm-for-the-healthcare-industry

[11] Christus Health reduces cognitive load on clinicians by 78% with Abridge. https://www.abridge.com/press-release/christus-health-announcement

[12] U.S. FDA. Artificial Intelligence and Machine Learning Software as a Medical Device Action Plan. https://www.fda.gov/media/145022/download

[13] European Commission. Ethics Guidelines for Trustworthy AI. https://digital-strategy.ec.europa.eu/en/library/ethics-guidelines-trustworthy-ai

[14] Obermeyer Z, Powers B, Vogeli C, Mullainathan S. (2019). Dissecting racial bias in an algorithm used to manage the health of populations. Science. https://www.science.org/doi/10.1126/science.aax2342

[15] World Health Organization. Ethics and Governance of Artificial Intelligence for Health. https://www.who.int/publications/i/item/9789240029200

[16] Momani A. Implications of Artificial Intelligence on Health Data Privacy and Confidentiality. arXiv, January 2025. https://arxiv.org/abs/2501.01639

[17] Stanford HAI. On the Opportunities and Risks of Foundation Models. https://crfm.stanford.edu/report.html

[18] U.S. FDA. Artificial Intelligence and Machine Learning Discussion Paper. https://www.fda.gov/files/medical%20devices/published/US-FDA-Artificial-Intelligence-and-Machine-Learning-Discussion-Paper.pdf

[19] JAMA. Artificial Intelligence in Health Care: Anticipating Challenges to Ethics, Privacy, and Bias. https://jamanetwork.com/journals/jama/fullarticle/2765681