AI in Medicine Certificate
Artificial intelligence (AI) is poised to make a significant impact throughout healthcare systems. The AI in Medicine certificate program offered by the University of Illinois Urbana-Champaign will equip healthcare professionals with a foundational understanding of AI and its applications through real-world medical case studies using machine learning models. This course will help prepare physicians, physician assistants, medical students, nurse practitioners and veterinarians to lead in the era of digital medicine.
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This is a self-paced online course divided into six modules with built-in exercises to put what you've learned into practice. The virtual format allows for flexibility to learn during your own time. Continuing Medical Education (CME) credits are available upon the completion of this course.
Earn a Certificate from UIUC
The University of Illinois Urbana-Champaign is a leader in the field of artificial intelligence research and renowned for its teaching excellence. The AI in Medicine certificate program was designed through an interdisciplinary partnership between the department of bioengineering at The Grainger College of Engineering, the Carle Illinois College of Medicine the University of Illinois College of Veterinary Medicine to deliver exceptional course quality and empower medical professionals.
What You'll Learn
Medical professionals with a foundational knowledge of artificial intelligence will be better equipped to partner with computer science professionals, interact with vendors, advance healthcare delivery, improve patient care and, ultimately - shape the future of medicine.
By the end of this course, you will be able to read literature related to artificial intelligence in medicine, understand how data-driven decisions are made and assessed, identify and define different types of artificial intelligence tools and techniques used in medicine and actively participate in the selection, purchase and deployment of AI-based medical software.
- Data and decisions
- Concepts of machine learning
- Deep learning
- Extending deep learning
- Deploying AI in practice
- Real-world applications of AI in medicine
What Sets This AI Course Apart
Designed for medical professionals
Our course was written specifically for physicians, clinicians, medical students and other healthcare professionals. We cover practical topics of modern workflows and patient interactions, as well as legal, ethical and logistical concerns specific to the use of clinical AI—these are typically not covered in general AI courses.
Relevant case studies featuring AI in healthcare
We immerse students in only medical examples and applications that illustrate the key concepts of how machines learn and how AI-based medical technologies can be used to improve health outcomes and streamline clinical workflows. You will not need to memorize technical terms or use coding for this course.
Access to AI experts for virtual office hours
As a part of this certificate program, you will have access to ask questions and engage with artificial intelligence experts from UIUC. This is an exclusive opportunity to connect with an expert about the course material and get hands-on support with exercises ranging from machine learning, deep learning and other concepts.
Improve Disease Diagnostics
AI technologies can help reduce the burden of clinicians during the diagnosis of diseases so they devote more time to providing patient care. Many advancements in the clinical application of AI are seen in image recognition-related fields through the use of machine learning and deep learning. These AI tools can facilitate the rapid detection of symptoms or physical characteristics based on image features and learn from patterns to apply in future diagnosis.
The University of Illinois has a robust history of medical imaging research tracing back to pioneers of the MRI machine. Researchers at Illinois are developing new diagnostic tools to expand the applications of AI in healthcare. These innovations include using AI to assemble digital biopsies that can identify important molecular characteristics of breast cancer biopsy samples without dyes or labels. Another team of researchers at Illinois have developed an AI-based approach to read EEG data to assist with the identification and treatment of epilepsy.
Reduce Medical Error to Ensure Patient Care Safety
Using artificial intelligence in healthcare can enable better care and improve outcomes by reducing the likelihood of medical errors. These errors are detrimental to patients and a burden on healthcare systems. An example use case could be during the prescription process. Medication error commonly results from human error and a lack of a backup system to detect mistakes. Using AI in electronic medical records software can improve patient safety by flagging a prescription if it deviates from the typical treatment for a particular condition.
Provide Predictive Risk Assessment and Health Monitoring
AI can be used to identify lifestyle and environmental risk factors in individuals and predict health outcomes. An example could be predicting whether an expectant mother might give birth preterm based on a set of risk factors. Another application could be to predict who might be at risk for heart disease. Using artificial intelligence with wearable technologies that can monitor a range of lifestyle data points could lead to more opportunities for precision medicine and personalized care. This use of AI in healthcare could even simplify patient visits by helping them to get to the right place at the right time and then streamline their interactions with their healthcare provider.
Enhance Electronic Healthcare Records
One of the biggest problems healthcare providers face is managing health data. Clinicians are burdened with administrative tasks in addition to their primary medical responsibilities. AI in healthcare systems can automate the creation and analysis of electronic health records (EHR) through natural language processing. For example, when physicians input notes into the EHR during patient visits, they are documenting unstructured data. Although there might be potential to glean key patterns or information from these notes, these insights are hard to extract. Natural language processing of medical records using machine learning algorithms can help process a large volume of data and help to flag diseases and deliver more personalized medicine.
Improve Administrative Workflow
AI tools can offer significant improvements to the day-to-day administrative workflow of healthcare systems. A use case might be employing AI to predict patient no-shows. On average in the United States, an unused time slot costs $200 per physician. The cost of no-shows amounts to billions of dollars a year.
Another application might be using AI to anticipate a shortage of resources for a particular specialization and plan ahead for those gaps. During the COVID-19 pandemic, for example, Illinois professors led the governor's modeling task force to predict shortages in ICU capacity which ultimately helped to shape the state's mitigation policy. AI can help to alleviate problems plaguing entire healthcare systems.
Offer Clinical Decision Support
One of the biggest benefits of using AI in healthcare is to support clinical care decisions. Healthcare practitioners have to make thousands of decisions everyday which can lead to clinical burnout. Ophthalmology, for example, is a field with the potential to capitalize on the use of AI. Retina specialists process a large number of images, through routine exams, to detect retinal diseases. AI can support ophthalmologists' clinical decision-making during the screening process and help to identify signs of diseases and predict patient outcomes. The use of AI to assist with clinical screening can be particularly impactful in high-risk populations or low-resource communities.
“My general field is biomedical imaging, and here, AI with machine- and deep-learning are already making a significant impact.”
Grainger Distinguished Chair in Engineering and executive associate dean at the Carle Illinois College of Medicine
FAQs about AI in Healthcare
Artificial intelligence is a vast and rapidly growing field. You may have some questions regarding AI-innovations and how it is impacting healthcare professionals.
Artificial intelligence is a broader idea that includes some deep philosophical concepts about what intelligence and learning really are. Machine learning can be thought of as a subfield of artificial intelligence. Some computer scientists think of machine learning as a means of achieving artificial intelligence. We can think of a machine learning as a method where computer algorithms automatically improve performance at a task by analyzing input data. In healthcare, those sort of tasks include: decision support, natural language processing, image interpretation, anomaly detection, treatment plan optimization, automated survey and referral systems, automated lab tests and analysis, medical data collection and data mining, health monitoring via wearables and many other clinical and research applications.
The artificial intelligence (AI) currently available for clinical practice is not a general intelligence capable of learning the same spectrum of tasks which humans can learn. Because these machine learning methods only work at restricted task sets in constrained environments, they are referred to as a kind of narrow AI. Precisely because it is not a wide, generally applicable AI, it is unlikely to completely replace human physicians at this time. It’s more practical to think of the potential of human experts such as physicians and other healthcare professionals, using AI rather than being replaced by it.
Artificial intelligence in healthcare can be thought of as a semi-automated data analysis method that can provide clinical decision support as well as improved medical logistics. It’s not yet capable of replacing healthcare providers outright, but, instead, has the potential to streamline healthcare providers’ workflow, schedule and recommend patient visits, and improve patient outcomes while reducing healthcare costs.
Yes! Healthcare providers are already using AI-based technologies to help with numerous tasks such as the automated collection and analysis of healthcare data. The entire healthcare industry is undergoing a change in how medical care is delivered to patients. AI in healthcare will only become more common as AI technologies become more capable. Tech companies big and small already are spending billions of dollars on developing AI and expanding the applications of AI in healthcare.
The artificial intelligence currently available for healthcare and medical research is not a general intelligence and, as such, is not adversarial and does not have feelings about patients. In effect, the AI is a device under human control. All medical devices intended for patient use are regulated in the United States by the Food and Drug Administration (FDA). For the near future, artificial intelligence mostly likely will be software for computer systems. The FDA already regulates software as a medical device (SaMD) and is continually expanding and updating regulations for a new era of AI-based devices. Furthermore, artificial intelligence in healthcare, like any other medical device, is to be deployed only according to strict usage protocols designed by an interdisciplinary team of medical and computer science professionals. Thus, artificial intelligence in healthcare is well-regulated and reasonable steps are taken to ensure patient safety.
AI technologies are already in use for healthcare from assisting with disease diagnostics, personalizing treatment, healthcare monitoring and even drug development. Learning about AI as a health practitioner is critical because of its broad applications in medicine and its project growth through many aspects of healthcare and health systems.