Introducing the new Certificate in Medical Software & Artificial Intelligence
Contributed by Xenophon (Xenios) Papademetris, PhD | Papademetris is a Professor of Biomedical Informatics & Data Science and Professor of Radiology & Biomedical Imaging. He is the Director of Image Processing and Analysis, Bioimaging Sciences, at Yale Department of Radiology and Biomedical Imaging.
On March 13, we launched our new Certificate Program in Medical Software and Medical AI. We have an initial cohort of 16 students from four continents (Asia, Africa, Europe, North America). It has taken us a little over four months to go from final approval to launch. During this time, we have recorded close to 20 hours of video lectures, plus another six to seven hours of supplementary guest expert interview videos.
Our non-degree program builds on the foundation of the recently published textbook “Introduction to Medical Software: Foundations for Digital Health, Devices, and Diagnostics” and the popular companion Yale Coursera Course “Introduction to Medical Software,”which has enrolled over 16,000 students from around the world.
The new certificate program will be taught by a team of experienced faculty from the Section of Biomedical Informatics and Data Science at the Yale School of Medicine with expertise in AI, data science, clinical decision support, and medical software.
The program will consist of four 4-week modules as follows:
- Introduction to Medical Software – an overview of both the regulatory and software engineering aspects of medical software
- Introduction to Artificial Intelligence – a broad overview of modern machine learning, beginning with core concepts and running all the way to modern generative AI and large language models. Frequent medical examples will ensure that students already experienced in AI will be able to enrich their knowledge base with applied examples.
- Medical Software with AI – we will focus here on how medical software design, implementation, and testing are affected when incorporating AI modules and the associated regulatory processes in this area
- Current and Emerging Applications of AI in Medicine – we will cover how AI-powered medical software is used in various settings, including radiology applications, clinical decision support in emergency medicine, clinical decision support in the context of global health, and emerging applications in genomics and other areas.
Each week of the program will consist of a pre-recorded video and a live online session where students can ask questions of both the instructors and visiting guest experts from academia and industry. Visit the Yale Biomedical Informatics & Data Science YouTube Channel to see recordings of informational webinars about this program, sample lecture videos, and freely available guest expert interviews.
If you're looking to advance your career in the medical device industry, our certificate in Medical Software and Medical Artificial Intelligence is the perfect opportunity. Enroll and take the first step toward achieving your career goals.
Applications for Spring 2024 are closed. Information for the next round of applications will follow.
Where Data Meets Biology and Medicine: PhD in CBB
Information contributed by Mark Gerstein and Steven Kleinstein | Gerstein is the Albert L Williams Professor of Biomedical Informatics and a Professor of Molecular Biophysics & Biochemistry, Computer Science, and Statistics & Data Science. Kleinstein is the Anthony N Brady Professor of Pathology and, along with Gerstein, Co-Director of Graduate Studies for Computational Biology and Bioinformatics.
The rapid acquisition of data such as electronic health record (EHR) data and other types of health data, as well as data made possible by genomics, transcriptomics, and proteomics technologies, has unveiled the gap between data availability and their biological and medical interpretation. Computational and theoretical approaches must be developed to help close this gap. Computational modeling of biomedical processes, management of biomedical data and knowledge, machine and statistical learning,algorithms, human-computer interfaces, as well as statistical and mathematical analyses, are some of the topics in the CBB (Computational Biology & Bioinformatics) curriculum.
Yale has an interdepartmental CBB PhD program, which means that students complete the CBB curriculum while being able to do their dissertation research in the laboratory of a faculty member in any relevant department at Yale.
Because of the interdisciplinary nature of the field, we anticipate that students will be extremely heterogeneous in their background and training. As a result, we are willing to meet with students to help them individually tailor the curriculum to their background and interests. The emphasis will be on gaining competency in three broad “core areas”: computational biology and bioinformatics, biological sciences (e.g. genetics), and informatics (e.g. computer science and statistics). Completion of the curriculum will typically take 4 semesters, depending in part on the prior training of the student. Since students may have very different prior training in biology and computing, the courses taken may vary considerably. In addition, students will spend a significant amount of time during this period doing intensive research rotations in faculty laboratories and attending relevant lectures and seminars.
"My experience has allowed me to see the most recent research involving AI and machine learning in healthcare," says Lucy Zheng, first-year PhD student in CBB. As part of her program, she plans to explore computational methods to enhance genetic and biomedical research. First-year PhD student Kevin Jin is interested in computational psychiatry, wearable devices, and clinical natural language processing. After his program, he hopes to apply his skills in industry.
Building the New MS in CBB with Bioinformatics Track
Information contributed by Cynthia Brandt, MD, MPH | Brandt is a Professor of Biomedical Informatics & Data Science and Professor of Biostatistics at Yale School of Medicine. She is also Vice Chair for Education in the Section of Biomedical Informatics & Data Science.
Without the workforce and the individuals who understand how data is created, how it's captured, how it's stored, and how different computational methods are necessary to analyze it, it causes a limitation that slows down what you can learn from the data that scientists are creating. Then it makes it more difficult to translate that data, which could be used for clinical trials and for medical advances.
The MS degree in CBB is a full-time 2-year program that provides students with broad training in information sciences, data science, clinical informatics, biological science, and consumer informatics. Students explore innovative ways to use data, information, and knowledge to improve the care and well-being of patients and populations, and biomedical science research. Graduates will be ready to tackle problems spanning medicine, computing, biology, data science, and more.
Applicants should typically have an undergraduate degree with a focus in health, computer science or mathematics/statistics. For the experienced clinician looking to gain a problem- solving edge or technical aficionado looking to understand clinical practice, the MS focuses on developing research skills through both coursework and structured research opportunities. Students will be expected to produce real-world solutions of publishable quality to problems in concert with faculty and practicing clinicians.
Read a feature article about this new program here.
A MS in computational biology and bioinformatics with a biomedical informatics track is expected to prepare a student for a career in biology at scientific research institutes, in clinical or health systems in data science roles, in STEM industry (beyond iust the biomedical sector), or further academic research in graduate school or beyond.
Explore the MS in Health Informatics at Yale School of Public Health
Contributed by Cynthia Brandt, MD, MPH
The Master’s in Health Informatics began in 2019 at the Yale School of Public Health within the Health Informatics Division in the Department of Biostatistics. The MS degree provides well-rounded training in Health Informatics, with a balance of core courses from such areas as information sciences, clinical informatics, clinical research informatics, consumer health and population health informatics, data science and more broadly health policy, social and behavioral science, biostatistics, and epidemiology. The program’s faculty cross-list courses and students take relevant courses in other schools and divisions at Yale. There are currently 15 ladder track faculty leading the program and the HI track in the executive MPH.
Graduates of this program will be equipped to develop, introduce, and evaluate new biomedically motivated methods in areas as diverse as data mining, natural language or text processing, cognitive science, human-computer interaction, decision support, databases, and algorithms for analyzing large amounts of data generated in public health, clinical research, and genomics/proteomics.
The length of study for the MS in HI is two academic years. First-year courses survey the field; the typical second-year courses are more technical and put greater emphasis on mastering the skills in health informatics. The degree also requires a year-long capstone project in the second year. There have been a total of 15 graduates from the program. There are currently 45 matriculated students. Applicants will typically have an undergraduate degree with a focus in health, computer science, and mathematics/statistics.
Physicians Wanted! For a Master of Health Science (MHS) with a Clinical Informatics & Data Science Focus
Contributed by Cynthia Brandt, MD, MPH
The Clinical Informatics and Data Science MHS is designed for graduates with clinical backgrounds who wish to gain competency in informatics and data science through core required courses and research activities. The science of informatics drives innovation that is defining future approaches to information and knowledge management in biomedical research and healthcare. Biomedical data science includes the design, implementation, and evaluation of statistical learning/machine learning models for pattern recognition, diagnosis, and prognosis, as well as other artificial intelligence (AI) models.
Required courses cover basics of clinical informatics and data science; other courses and topics cover clinical decision support, computer system architectures, networks, security, data management, human factors engineering, clinical data standards, analytical methods and data science, and medical AI.
Also, the curriculum includes other courses and electives including leadership models, processes and practices, effective interdisciplinary team management, effective communications, project management, strategic and financial planning for clinical information systems, and change management.
Executive MPH: Online and On-Campus at Yale
Directed by Hamada Hamid Altalib, DO, MPH, FAES | Associate Professor of Neurology and of Psychiatry; Track Director, Health Informatics, Executive MPH
The Executive Master of Public Health is an innovative, hybrid program that blends comprehensive online education with in-person management and leadership training on the Yale campus, creating a unique and powerful educational experience. Taught by top faculty from the Yale School of Public Health, the Yale School of Medicine, and additional experts in their fields and employing state-of-the-art tools and technology, the program aims to train professionals who seek to acquire a strong public health education, advanced training in their area of interest, and hands-on public health and leadership training.
Designed from the ground up for working health professionals, the hybrid online program provides extensive training in leadership and management, a broad foundation in public health, specialized knowledge in areas critical to health promotion and disease prevention, and a year-long integrative experience that enables students to apply what they have learned to a real-world public health problem.
The two-year, part-time program is open to students with:
- A bachelor’s degree and at least four years of relevant work experience (need not be in the health field), OR
- A master’s degree and at least two years of relevant work experience (need not be in the health field), OR
- A doctoral (or international equivalent) degree in a field related to public health (e.g., physicians, dentists, podiatrists, pharmacists, veterinarians, attorneys, and those with a doctorate in the biological, behavioral, or social sciences)
The Health Informatics track is hosted by Yale School of Medicine's Section of Biomedical Informatics, and the track director is Professor Altalib.