Overview / CareersAI Careers in Healthcare
AI is poised to transform healthcare. AI and machine learning are being used for enhanced diagnostic imaging, early disease detection, predictive patient monitoring, and treatment optimization. Natural language processing and generative AI techniques are used for efficient management of electronic medical records, effective summarization of doctor-patient conversations, and significant acceleration of drug discovery.
AI has the potential to make healthcare more accessible and affordable with highly personalized and optimized treatments, proactive preventive care, and significant improvements to diagnosis. AI could automate routine clinical tasks, optimize healthcare resource and data management, and improve patient outcomes on a global scale, elevating the standards of care and leading to more effective healthcare systems.
What would an AI professional contribute to the healthcare industry?
An AI professional in healthcare would typically develop, train, and evaluate AI systems designed to support medical diagnostics, treatment plans and recommendations, patient monitoring data, and health data management. No matter what they do within healthcare, AI professionals must pay particular attention to ensuring patient privacy, safety, and ethical practices.
What are the biggest opportunities and challenges for AI in the industry?
AI holds great promise for improving diagnostic accuracy, enabling early disease detection, and personalizing treatments. It can enhance patient outcomes, accelerate drug discovery, and address management aspects of delivering quality accessible and affordable care.
The primary challenges involve patient safety, data privacy, and ethical compliance. AI solutions must handle sensitive patient information securely, comply strictly with healthcare regulations, and provide transparency around medical decision-making processes to build patient trust and doctor confidence.
How does MSAI prepare students to lead in the healthcare space?
In addition to learning core techniques and skills in AI and machine learning that are central to healthcare technology, MSAI students have the opportunity to take elective courses that center on healthcare or related subjects. Additionally, they can partake in research projects in collaboration with doctors and scientists from Northwestern University’s Feinberg School of Medicine, working on real-world, impactful medical projects that can prepare them for the healthcare industry.
Where have MSAI students and alumni interned or worked
- CentraCare
- Direct Supply
- Genmab
- GSK
- HonorHealth
- Mayo Clinic
- Medical University of South Carolina
- MedMitra AI
- Mendel.ai
- Nationwide Children's Hospital
- Northwestern Medicine
- Teladoc Health
- The Catholic University of Korea
- UC San Diego Health
- University of Wisconsin-Madison
- Vector Space Biosciences
Featured Alumni
Hawkins Gay, MD
MSAI '21, Cardiac Electrophysiologist for Northwestern MedicineHawkins Gay saw the potential for AI in the medical community while working as a cardiology fellow at Northwestern Memorial Hospital.
He took advantage of the MSAI+X program and applied lessons learned in the program to several research projects focused on using machine learning algorithms to make medical diagnoses. He also discovered how AI can help analyze electronic health records and be an asset to medical teams.
"Electronic systems collect clinical, laboratory, and demographic information on patients in both the inpatient and outpatient settings," Gay said. "This results in massive amounts of catalogued data that can be used efficiently or inefficiently. If we could access all of the clinical data at our fingertips, we can use these systems to help us make decisions and to care for patients."
Alex Leidner, MD
MSAI '21, Assistant Professor of Medicine (nephrology and hypertension) for Northwestern MedicineAlex Leidner was a resident physician and nephrology fellow before joining MSAI through the MSAI+X program. He realized that machine learning algorithms could help assess decades of information collected through electronic health records and aid doctors in decision-making processes, particularly around identifying patients for new treatments.
Leidner also thinks AI can be a valuable tool for patients with hypertension, which can lead to heart disease — the leading cause of death in the US.
"Hypertension does not result in negative outcomes for everyone, but it remains a leading risk in developing heart disease," he said. "Using machine learning, we can identify latent characteristics to make it clear whose hypertension will cause issues later on. It can also identify those people with subtle abnormalities and assist public health attempts to identify and treat these diseases."
Read more of Leidner's view on AI supporting the medical community
Eric Yang
MSAI '19, Senior AI Scientist for GenmabWhen Eric Yang first looked into Northwestern Engineering's Master of Science in Artificial Intelligence (MSAI) program, it was the wide range of topics within the curriculum that most appealed to him. Being able to understand complex concepts like knowledge representation and neural networks intrigued him, and he figured the varied skill set he would develop would be a valuable commodity.
He was right.
Yang spent nearly four years as a senior manager and data scientist at Sumitomo Pharma America (previously Sumitovant Biopharma), where he helped speed up the development of innovative medicines. In 2024, Yang joined Genmab, an international biotechnology company looking to improve patient lives with antibody therapeutics.
"The breadth of techniques and concepts I was exposed to in MSAI allowed me to have a good grasp on high-level techniques and capabilities that exist within machine learning and data science," Yang said. "Though I didn't get deep experience in each, knowing they existed gave me enough direction for where to start researching for a given topic at hand."
