Johns Hopkins University teams up with Great Learning to launch AI in Healthcare program
AI is moving healthcare toward more precise, proactive, and safer care. To meet that shift head-on, Great Learning has partnered with Johns Hopkins University (JHU) to launch a practical AI in Healthcare program built for working professionals.
This 10-week online course is crafted by JHU faculty and helps healthcare teams apply AI to real operational, clinical, and strategic problems-without requiring programming or coding skills.
Who this is for
- Clinicians, nursing leaders, and care managers
- Pharma and biotech professionals
- Researchers and data-driven healthcare teams
- Healthcare consultants and policymakers
- Healthtech founders, product managers, and operations leaders
Why it matters
Global demand is rising fast: Grand View Research estimates AI in healthcare will grow at a 38.5% CAGR through 2030, spanning diagnostics, hospital management, drug discovery, and personalized medicine. Source
This collaboration aims to equip professionals to build data-driven, efficient, and patient-centered systems that improve outcomes and streamline operations.
Paul Huckett, Associate Dean, Johns Hopkins Engineering Executive and Professional Education, said, "We believe the future of healthcare will be shaped by how effectively emerging technologies like AI are adopted. The AI in Healthcare program is designed not just to teach technical skills but also to cultivate leaders who can leverage AI to transform patient care, strengthen health systems, and drive innovation responsibly. Through this collaboration with Great Learning, we are creating a bridge between academic knowledge and real-world application."
What the program covers
- AI fundamentals and the R.O.A.D. management framework applied to healthcare settings
- Core machine-learning models, accuracy evaluation, and validation
- Ethics, regulations, and human factors affecting adoption
- Predictive analytics for forecasting complications and improving care pathways
- Large language models in clinical and operational use cases
- Graph analytics for health risk networks and medication behaviors
- Epidemiological modeling (Markov, SEIR) for disease spread and pandemic planning
- Practical pitfalls in AI projects, EHR data management, and scaling pilots across hospitals
Learning experience
The program blends recorded video lectures, weekly interactive mentored sessions with industry experts, and live masterclasses with JHU faculty. You'll apply concepts through 8+ hands-on healthcare case studies focused on disease prediction, clinical workflows, and personalized patient care.
On completion, learners receive a Certificate of Completion and six Continuing Education Units (CEUs) from Johns Hopkins University.
Mohan Lakhamraju, Founder and CEO of Great Learning, said, "Artificial intelligence is propelling healthcare into a new era, one where care is deeply personalised, intelligent and safe. By collaborating with Johns Hopkins University, which is world-renowned for its medicine, nursing, and public health expertise, this program ensures that learners benefit from both academic rigor and practical industry application. The program combines rigorous AI learning with healthcare-relevant applications, thus preparing professionals to not just understand AI but to apply it effectively in clinical, operational, and strategic healthcare settings."
How healthcare teams can use this
- Stand up data-backed pilots in triage, readmission reduction, or capacity planning
- Evaluate model performance and risk before deployment
- Improve EHR workflows and data quality for AI use
- Link analytics to real outcomes: fewer complications, faster diagnostics, better throughput
Next steps
To learn more about Johns Hopkins' executive and professional programs, visit Johns Hopkins Engineering Executive Education.
If you're mapping AI learning paths for healthcare roles, explore curated options on Complete AI Training.
As AI continues to influence diagnostics, drug development, and hospital operations, programs like this help teams turn real constraints into measurable improvements-safely, ethically, and at system scale.
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