phd healthcare ai

Artificial Intelligence Enabled Healthcare MRes + MPhil/PhD

London, Bloomsbury

Artificial Intelligence (AI) has the potential to transform health and healthcare systems globally, yet few individuals have the required skills and training. To address this challenge, our Centre For Doctoral Training (CDT) in AI-Enabled Healthcare Systems will create a unique interdisciplinary environment to train the brightest and best healthcare artificial intelligence scientists and innovators of the future.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

Applications closed

The Centre for Doctoral Training recruits in at least two rounds. Applicants are advised to apply early, priority will be given to those who have applied in round one.

• Entry requirements

A minimum of an upper second class honours undergraduate degree, or a Master's degree in a relevant discipline (or equivalent international qualifications or experience). Our preferred subject areas are Physical Sciences (Computer Science, Engineering, Mathematics and Physics) or Clinical / Biomedical Science. Applicants with a clinical background or degree in Biomedical Science must be able to demonstrate strong computational skills. You must be able to demonstrate an interest in creating, developing or evaluating AI-enabled Healthcare systems.

The English language level for this programme is: Level 2

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

If you are intending to apply for a time-limited visa to complete your UCL studies (e.g., Student visa, Skilled worker visa, PBS dependant visa etc.) you may be required to obtain ATAS clearance . This will be confirmed to you if you obtain an offer of a place. Please note that ATAS processing times can take up to six months, so we recommend you consider these timelines when submitting your application to UCL.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

Every student who is accepted onto the AI-enabled Healthcare Systems Centre for Doctoral Training (CDT) must take the MRes Artificial Intelligence Enabled Healthcare in their first year. This will be followed by a 3 year PhD. Throughout this period the CDT will continue to closely monitor the need for continuing training and support, tailored to each student, and provide ongoing training in research skills. The MRes is not currently available as a stand-alone programme.

The MRes programme covers the core competencies of artificial intelligence and has a central emphasis on how healthcare organisations work. Ethical training for medical artificial intelligence will be explicitly emphasised alongside a broader approach to responsible research, innovation and entrepreneurship.

During the MRes year, students will learn the statistical underpinnings of machine learning theory, get a practical grounding in research software engineering and the principles of healthcare and medical research, as well as a thorough treatment of topics in machine learning, advanced statistics and principles of data science.

As part of the MRes, alongside the core and elective modules, you will complete a substantial Masters-level project of your choice, working with a supervisory team that will normally include a clinician and an academic. The project you work on during your MRes normally leads to the chosen PhD research topic.

The remaining years will be more like a traditional PhD, which leads to the presentation of a PhD thesis at the end of the fourth year. During your PhD you will remain involved in CDT activities and will continue to work closely with relevant health professionals and clinical teams through our NHS partners and leading academics at UCL.

As a cohort based PhD programme, students will also have the opportunity to participate in a range of seminars, training programmes, placements and other activities, including UCL's Doctoral Skills Development Programme.

Training Opportunities The CDT programme consists of a range of activities and events including:

• A Mini-MD programme where trainees undertake an immersive clinical experience within an NHS setting
• Annual CDT Conference
• Seminar series
• PPI Training
• Responsible Research & Innovation
• Communication Skills
• Entrepreneurship
• Ethical Training
• The opportunity to attend training programmes offered by the Alan Turing Institute
• Opportunities for internships and placements with industry partners

More information can be found on the CDT Website .

Who this course is for

The Centre for Doctoral Training programme is for students with an interest in creating and developing AI solutions aimed to transform and solve healthcare challenges. The CDT programme is embedded within a NHS setting, and should appeal to students keen to develop clinical knowledge and algorithmic/ programming expertise.

What this course will give you

• Benefit from UCL's excellence both in computational science and biomedical research innovating in AI;
• Be supervised by world-leading clinicians and AI researchers in areas related to your research;
• Work within a real-world setting, embedded within hospitals, allowing you to gain a practical understanding of the value and limitations of the datasets and the translational skills required to put systems into practice;
• Have the opportunity to not only apply AI to healthcare but to apply healthcare to AI, generating novel large-scale open datasets driving methodological innovation in AI;
• Become a future leader in solving the most pressing healthcare challenges with the most innovative AI solutions;
• Study at UCL, which is rated No.1 for research power and impact in medicine, health and life sciences (REF 2021) and 9th in the world as a university (QS World Rankings 2024).

The foundation of your career

We do not yet have any graduates from the four-year programme, our first cohort of students will be graduating over the next few months. We expect them to stay within the field of AI and healthcare, and much like previous graduates from our experienced CDT supervisors, they will go onto successful careers in academia and industry. 

Employability

The distinctive characteristics of our programme allow us to produce graduates who are prepared to:

• engineer adaptive and responsive solutions that use AI to deal with complexity;
• innovate across all levels of care, from community services to specialist hospitals;
• be comfortable working with patients and professionals, and responding to their input;
• appreciate the importance of addressing health needs rather than creating new demand.

The Institute's research departments collaborate with third-sector and governmental organisations, as well as members of the media, both nationally and internationally to ensure the highest possible impact of their work beyond the academic community. Students are encouraged to do internships with relevant organisations where funding permits. Members of staff also collaborate closely with academics from leading institutions globally.

Teaching and learning

Various teaching and learning methods are employed to facilitate effective learning and cater to different learning styles. Below are some common types of teaching methods that may be used across the programme:

Interdisciplinary Teaching: Interdisciplinary teaching involves integrating knowledge and skills from multiple disciplines or subject areas to provide a comprehensive understanding of a topic, particularly AI and healthcare. This approach encourages students to make connections between different subjects and fosters critical thinking and problem-solving abilities.

Lecture-Based Teaching: Lecture-based teaching is a traditional method where the instructor presents information to students through spoken words. It involves the teacher sharing knowledge, concepts, and theories, while students take notes and listen actively. This method is effective for conveying large amounts of information and providing foundational knowledge.

Practical Coding Sessions: Practical coding sessions are hands-on learning experiences where students actively engage in coding exercises, programming tasks, and problem-solving activities. These sessions are essential for AI and programming-related subjects (machine learning, etc) as they allow students to apply theoretical knowledge to real-world scenarios.

Interactive Teaching: Interactive teaching methods encourage active participation and engagement from students. These methods can include discussions, debates, group activities, and case studies, in particular in several modules such as Journal Club. Interactive teaching fosters collaboration, communication skills, and a deeper understanding of the subject matter.

Project-Based Learning: Project-Based Learning involves assigning students long-term projects that require them to investigate and address real-world problems or challenges (such as AI & healthcare group project). It enhances critical thinking, research skills, and creativity while promoting independent learning and teamwork.

Collaborative Learning: Collaborative learning involves students working together in small groups or pairs to solve problems, discuss ideas, and complete tasks. This method promotes teamwork, communication, and the exchange of diverse perspectives.

The use of these teaching/learning methods can vary depending on the subject matter, the goals of the programme, and the preferences of the instructors in the MRes year. Our educational programme incorporates a mix of these methods to cater to the diverse needs of learners and create a well-rounded learning experience.  

Compulsory Modules:

CHME0033 Dissertation in Artificial Intelligence Enabled Healthcare

CHME0032 Healthcare Artificial Intelligence Journal Club

Optional Modules

CHME0012 Principles of Health Data Science

CHME0013 Data Methods for Health Research

CHME0015 Advanced Statistics for Records Research

CHME0016 Machine Learning in Healthcare and Biomedicine

CHME0031 Programming with Python for Health Research

CHME0034 Computational Genetics of Healthcare

CHME0035 Advanced Machine Learning for Healthcare

CHME0039 Artificial Intelligence in Healthcare Group Project

COMP0084 Information Retrieval and Data Mining

Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.

Assessment methods are crucial components of an educational programme, as they evaluate students' understanding, knowledge, skills, and application of concepts. Here are various types of assessment methods that may be used across the programme:

Exams: Traditional exams are a common assessment method that tests students' knowledge and understanding of the course material. These exams typically involve a time-bound written assessment, where students respond to questions related to the subject matter.

Open-Book Exam: In an open-book exam, students are allowed to refer to their textbooks, notes, or other resources during the assessment. The questions in these exams are often designed to test higher-order thinking and problem-solving abilities, as students have access to reference materials.

Coursework: Coursework assessments involve various assignments, essays, reports, or projects that students complete throughout the course. These assessments may cover specific topics or practical applications and help to assess students' comprehension and critical thinking skills.

Coding Exam: A coding exam is specifically designed for courses related to computer science, software development, or programming. Students are given coding challenges or programming tasks that assess their coding proficiency and problem-solving abilities.

Collaborative Project: In a collaborative project assessment, students work in groups to tackle a complex problem or complete a substantial task. This assessment measures teamwork, communication, time management, and the ability to achieve shared goals.

Presentation and Q&A: Presentations require students to deliver a talk on a given topic or project. The presentation assesses their ability to communicate effectively, organize information, and present ideas coherently. Often, a question and answer (Q&A) session follows the presentation to delve deeper into the topic.

Research Proposal: A research proposal is a preliminary plan for a research project that students submit to demonstrate their research capabilities. It outlines the research question, objectives, methodology, and potential outcomes of the study.

Dissertation Writing: Dissertation writing is typically reserved for higher education levels, such as undergraduate and postgraduate studies. It involves an extended research project on a specific subject, allowing students to demonstrate research, analytical, and academic writing skills.

Online Quizzes and Tests: Online quizzes and tests are digital assessments that may be used for formative or summative purposes. They are often employed in blended or online learning environments.

The use of assessment methods will vary based on the nature of the programme, the subject matter throughout the MRes year. A well-balanced combination of assessment types ensures that students' diverse abilities and learning styles are appropriately evaluated while providing a comprehensive understanding of their progress and achievements.

During the MRes 4 hours of a student's time is spent in tutorials per week and/or, 6-8 hours in lectures per week, and a further 20-24 hours in independent study per week.

Research areas and structure

• AI-enabled diagnostics or prognostics
• AI-enabled operations
• AI-enabled therapeutics
• Public Health Data Science
• Machine Learning in Health Care
• Public Health informatics
• Learning health systems
• Electronic health records and clinical knowledge management
• e-health and m-health
• Clinical Decision Support Systems

Research environment

Our research environment offers a unique degree programme that stands out among competitors. We provide students with the exceptional opportunity to explore the cutting-edge intersection of AI technology and healthcare applications. Our curriculum emphasizes research and innovation skills, empowering students to become independent researchers and adept problem solvers. A key difference is our close collaboration with clinicians and front-line practitioners. This interaction fosters a holistic understanding of healthcare challenges and real-world applications, ensuring that our graduates are equipped with practical knowledge and solutions. Our programme is inclusive, welcoming students from both computational and clinical backgrounds, creating a diverse and dynamic learning environment.

Students studying the programme full-time will be expected to complete 180 credits during the academic year. 

Students studying the programme part-time will be expected to complete 180 credits across two academic years. 

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing Services team .

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

All studentships include a research training support grant, which covers additional research costs throughout students' time on the programme.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

Please visit the CDT website for funding information.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

Note for applicants: When applying on UCL Select, please select MRes Artificial Intelligence enabled healthcare to apply for programme.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Got questions? Get in touch

Institute of Health Informatics

[email protected]

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Stanford Online

Artificial intelligence in healthcare.

Stanford School of Medicine , Stanford Center for Health Education

Monthly subscription: $79

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Artificial intelligence (AI) has transformed industries around the world, and has the potential to radically alter the field of healthcare. Imagine being able to analyze data on patient visits to the clinic, medications prescribed, lab tests, and procedures performed, as well as data outside the health system -- such as social media, purchases made using credit cards, census records, Internet search activity logs that contain valuable health information, and you’ll get a sense of how AI could transform patient care and diagnoses. In this specialization, we'll discuss the current and future applications of AI in healthcare with the goal of learning to bring AI technologies into the clinic safely and ethically. This specialization is designed for both healthcare providers and computer science professionals, offering insights to facilitate collaboration between the disciplines.

• Identify problems healthcare providers face that machine learning can solve
• Analyze how AI affects patient care safety, quality, and research
• Relate AI to the science, practice, and business of medicine
• Apply the building blocks of AI to help you innovate and understand emerging technologies
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Introduction to Healthcare

Introduction to Clinical Data

Fundamentals of Machine Learning for Healthcare

Evaluations of AI Applications in Healthcare

AI in Healthcare Capstone

Flexible enrollment options, monthly subscription: $79.

View and complete course materials, video lectures, assignments, and exams, at your own pace with a monthly subscription to the Artificial Intelligence in Healthcare Specialization.

What Our Learners Are Saying

AI in Healthcare is an incredible program offering content related to the Healthcare System, Clinical Data, Machine Learning, & Artificial Intelligence Applications in Healthcare. After completing this program, one choose more advanced study in the aforementioned topics and/or take a deeper dive in the numerous interrelated subjects such as computational math, stats, programming/coding and algorithms. Simply outstanding!!

John S., Clinical Specialist

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• Back to PhD Program in Health Artificial Intelligence

Training & Curriculum

• Application Information
• Faculty & Administration

The curriculum of the PhD in Health AI emphasizes an active learning approach that will be used to teach six required courses, including AI, ethical AI, machine learning, natural language processing, clinical applications of AI and biomedical informatics. Students will gain healthcare experience through clinical rotations, clinical collaborations and access to clinical data from the electronic health record.

Cedars-Sinai 's PhD in Health AI is pending WSCUC accreditation.

Program Overview

This program will provide doctoral students with the knowledge and practical experience to develop, evaluate and apply cutting-edge AI algorithms and methods for improving patient care. We utilize a hands-on, active learning approach to teaching that reinforces AI concepts through short projects completed during class. Students will be exposed to hospital rotations to better understand how AI might be used in healthcare. Students will work with clinical collaborators and will have access to data from the electronic health record. Graduates of the program will be positioned to improve healthcare and patient outcomes through the rigorous development and deployment of AI algorithms and software.

View the Program Schedule (PDF)

This course provides a comprehensive exploration of the intersection between artificial intelligence and biomedical sciences, aimed at equipping AI (Artificial Intelligence) and computer science professionals with the requisite clinical knowledge to develop and apply AI algorithms in healthcare. Students will delve into the principles of clinical medicine, examine case studies of AI applications in clinical settings, and engage in the development of AI solutions to address medical challenges. Key topics include feature engineering, data preprocessing, dimensionality reduction, explainable AI, and setting up appropriate evaluation methods for domain-specific problems. The course will also address the ethical, regulatory, and practical considerations of implementing AI in healthcare, including dealing with bias and fairness, preparing students to contribute to the advancement of AI-driven clinical and translational research. 

Imaging AI seeks to advance innovative diagnostic and prognostic algorithms in Radiology and Pathology, equipping you with the competencies to develop and validate AI / deep learning workflows for biomedical image analysis and translate theoretical knowledge into clinical solutions. Through hands-on learning, you will master AI-driven image analysis for disease biomarker identification, diagnostic and prognostic modeling, and progression tracking and monitoring. The course will also emphasize appropriate statistical validation (e.g., multilevel regression modeling) and evaluation of the AI models. Special topics include graph-based methods, spatial multimodal analysis, and user interface design.  

Designing and inventing new biomedical devices and wearables in any area of healthcare requires a comprehensive clinical and physics-based understanding of the human body integrated with the art of engineering design. This course focuses on developing devices and wearables for the neuromuscular system. We will start with a brief introduction to the human anatomy, the neuromuscular system, and the behavior of different types of signals, such as electrical, acoustic, and optical waves, that can be used to understand human tissue condition and behavior, along with examples of the current state of the art. We will then delve deep into 2 to 3 clinical problems medical providers face in musculoskeletal medicine, where biomedical devices could improve screening, diagnosis, or assessment and, therefore, improve clinical care.  We will focus on pathophysiology, the clinical workflow, and constraints inherent in human subject studies, and the engineering limitations, before exploring potential pathways to develop a biomedical device or wearable. The second part of the class will focus on developing a working prototype of a wearable device. You will gain hands-on experience with prototyping tools and devices such as high-end 3D printers, Computer-Aided Design (3D design), programming microcontrollers and sensors, and transducers. 

AI algorithms for personalized medicine require multi-modal data to capture the interactions between our genes and the environment in order to understand disease conditions. This course will cover algorithms and methods used to analyze complex biomedical data, including DNA sequences, genetics, epigenetics, proteomics, single-cell genomics, and molecular image data. A mentored term project will provide you with hands-on experience for carrying out independent research, highlighting the importance of interdisciplinary collaborations and the value of incorporating diverse perspectives in research.

Computational Biomedicine, a rapidly growing discipline at the intersection of biology, medicine, statistics, and computer science, offers exciting opportunities for real-world impact in healthcare. In the dynamic landscape of biomedical research, where data plays an increasingly crucial role, understanding scientific inquiries and developing quantitative skills for data analysis and interpretation are essential. This course, serving as an introduction to Computational Biomedicine, will focus on modeling health and disease systems. We will cover computational modeling principles, apply modeling techniques, analyze model performance and limitations, and explore innovative computational frameworks, algorithms, and architectures. These tools are not just theoretical concepts, but practical solutions to address unmet needs and open problems in biomedical research and clinical practice. The course will use project-based and hands-on learning experiences to enhance students’ understanding and application of the subject matter, preparing them for the exciting challenges of the field. 

This course explores the ethical challenges and considerations involved in developing and deploying artificial intelligence (AI) systems in healthcare and public health contexts, including responsible use, patient consent, bias of AI algorithms, and fairness in models. You will critically examine predictive models and AI applications used for making important health decisions, addressing factors that lead to trustworthy AI. Through a reverse classroom approach, students will engage in active learning activities to analyze the potential for bias, risk, and social inequity in AI systems. The course will emphasize project-based learning, allowing students to learn and apply ethical AI principles and practices to real-world healthcare scenarios. 

This course provides comprehensive coverage in machine learning, covering both theoretical foundations and practical applications. Students will learn concepts, algorithms, and techniques used in machine learning. Emphasis will be placed on real-world applications, particularly in biological and clinical sciences. Students will gain hands-on experience through practical exercises and projects and learn the theory and practice of machine learning from a variety of perspectives. Topics include supervised learning (classification, regression); unsupervised learning (clustering, dimensionality reduction); reinforcement learning; and computational learning theory. 

The significant advance of natural language processing (NLP) approaches in the last few years, with the advent of chatbots that seem to hold conversations and even express ‘chain-of-thought’ reasoning behind their answers, sets the bar high for what these systems can accomplish within the healthcare setting, facilitating patient-physician interaction and improving diagnostic accuracy. This course will take a hands-on approach to explore the boundaries of NLP and Artificial Intelligence, enabling deep understanding of cutting-edge technologies that could help address the hardest problems currently faced by clinicians and patients.

Clinical Rotations

All students are required to fulfill a minimum of 20 hours of clinical rotations across one or more specialties. During these rotations, students will shadow doctors during patient encounters and observe interactions, utilizing electronic health records and decision-support tools.

Research Rotations

All students will complete three rotations during the first year in candidate dissertation research labs. This process will culminate in identifying a willing research mentor to supervise a dissertation research project.

Dissertation Research

Students are expected to conduct a dissertation research project that generates new knowledge at the intersection of AI and healthcare. The project will facilitate collaboration between AI experts and clinicians, culminating in several peer-reviewed publications.

Have Questions or Need Help?

If you have questions or wish to learn more about the PhD program in Health AI, call us or send a message.

Health Sciences Informatics, PhD

School of medicine.

The Ph.D. in Health Sciences Informatics offers the opportunity to participate in ground-breaking research projects in clinical informatics and data science at one of the world’s finest biomedical research institutions. In keeping with the traditions of the Johns Hopkins University and the Johns Hopkins Hospital, the Ph.D. program seeks excellence and commitment in its students to further the prevention and management of disease through the continued exploration and development of health informatics, health IT, and data science. Resources include a highly collaborative clinical faculty committed to research at the patient, provider, and system levels. The admissions process will be highly selective and finely calibrated to complement the expertise of faculty mentors.    

Areas of research:

• Standard Terminologies
• Precision Medicine Analytics
• Population Health Analytics
• Clinical Decision Support
• Translational Bioinformatics
• Health Information Exchange (HIE)
• Multi-Center Real World Data
• Telemedicine

Individuals wishing to prepare themselves for careers as independent researchers in health sciences informatics, with applications experience in informatics across the entire health/healthcare life cycle, should apply for admission to the doctoral program.

Admission Criteria

Applicants with the following types of degrees and qualifications will be considered:

• MA, MS, MPH, MLIS, MD, PhD, or other terminal degree, with relevant technical and quantitative competencies and evidence of scholarly accomplishment; or
• In exceptional circumstances, BA or BS, with relevant technical and quantitative competencies, with some combination of scholarly accomplishment and/or professional experience in a relevant field (e.g., biomedical research, data science, public health, etc.)

Relevant fields include: medicine, dentistry, veterinary science, nursing, ancillary clinical sciences, public health, librarianship, biomedical science, bioengineering and pharmaceutical sciences, and computer and information science. An undergraduate minor or major in information or computer science is highly desirable. Professional work experience in one of these fields is also highly desirable. 

The application is made available online through Johns Hopkins School of Medicine's website . Please note that paper applications are no longer accepted. The supporting documents listed below must be received by the SOM admissions office by December 15 of the following year. Applications will not be reviewed until they are complete and we have all supporting letters and documentation.

• Curriculum Vitae (including list of peer-reviewed publications and scientific presentations)
• Three Letters of Recommendation
• Statement of Purpose
• Official Transcripts from undergraduate and any graduate studies
• Certification of terminal degree
• You are also encouraged to submit a portfolio of published research, writing samples, and/or samples of website or system development

Please track submission of supporting documentation through the SLATE admissions portal.

If you have questions about your qualifications for this program, please contact [email protected]

Program Requirements

The PhD curriculum will be highly customized based on the student's background and needs. Specific courses and milestones will be developed in partnership with the student's advisor and the PhD Program Director.

The proposed curriculum is founded on four high-level principles:

• Achieving a balance between theory and research, and between breadth and depth of knowledge
• Creating a curriculum around student needs, background, and goals
• Teaching and research excellence
• Modeling professional behavior locally and nationally.

Individualized curriculum plans will be developed to build proficiencies in the following areas:

• Foundations of biomedical informatics: e.g., lifecycle of information systems, decision support
• Information and computer science: e.g., software engineering, programming languages, design and analysis of algorithms, data structures.
• Research methodology: research design, epidemiology, and systems evaluation; mathematics for computer science (discrete mathematics, probability theory), mathematical statistics, applied statistics, mathematics for statistics (linear algebra, sampling theory, statistical inference theory, probability); ethnographic methods.
• Implementation sciences: methods from the social sciences (e.g., organizational behavior and management, evaluation, ethics, health policy, communication, cognitive learning sciences, psychology, and sociological knowledge and methods), health economics, evidence-based practice, safety, quality.
• Specific informatics domains: clinical informatics, public health informatics, analytics
• Practical experience: experience in informatics research, experience with health information technology.

Basic Requirements

• "Core" courses
• Student Seminar & Grand Rounds
• Selective and Elective courses
• Mentored Research (in Year 1)
• Qualifying Exam (in Year 2)
• Proposal Defense (in Year 2 or 3)
• Dissertation (Years 2-4)
• Final Dissertation Defense (Year 4)
• Research Ethics