Data Augmented Technology Assisted Medical Decision Making from Coursera

Artificial intelligence (AI) and machine learning (ML) have the potential to increase diagnostic accuracy, decrease diagnostic errors, and improve patient outcomes. The Data Augmented, Technology Assisted Medical Decision Making (DATA-MD) course will teach you how to use AI to augment your diagnostic decision-making. The National Academy of Medicine (NAM) recommends ensuring that clinicians can effectively use technology - including AI - to improve the diagnostic process. To use these technologies effectively in your clinical practice, you will need to determine when use of AI is appropriate, interpret the outputs of AI, read medical literature about AI, and explain to patients the role that AI plays in their care. In this course, you’ll explore the ethical considerations and potential biases when making medical decisions informed by AI/ML-based technologies. DATA-MD is a one of a kind curriculum designed to provide an introduction to the use of AI in the diagnostic process.

This course was created with the needs of medical students, residents, fellows, practicing physicians, advanced practice providers, and registered nurses in mind. Others, like educators, computer programmers, and data scientists, may also find value in the course.

Continuing Medical Education Information:

This activity is released for CME credit on 07/30/2024 and expires 06/31/2027.

The University of Michigan Medical School is accredited by the Accreditation Council for Continuing Medical Education (ACCME) to provide continuing medical education for physicians.

The University of Michigan Medical School designates this enduring material for a maximum of 3.5 AMA PRA Category 1 Credit(s)™. Physicians should claim only the credit commensurate with the extent of their participation in the activity.

Dr. Cornelius James and Jessica Virzi, planner and co-planner for this educational activity, have no relevant financial relationship(s) with ineligible companies to disclose.

Maggie Makar, Benjamin Li, and Nicholson Price, presenters of this educational activity, have no relevant financial relationship(s) with ineligible companies to disclose. Karandeep Singh, presenter for this educational activity, was a consultant for Flatiron Health. The relevant financial relationship listed for this individual has been mitigated. Cheri Breadon and Jessica Virzi are the coordinators for this activity.

After this activity, participants will be able to

-Use AI to augment your diagnostic clinical decision-making

-Describe the strengths and limitations of AI/ML-based technology in the diagnostic process

-Interpret statistical measures frequently used to evaluate the performance of ML models

-Critically appraise studies that include AI/ML and determine the applicability of study results in clinical practice

If you would like to earn CME credit for participating in this course, please review the information, including expected results, presenters, their disclosures, and CME credit at this website prior to beginning the activity: https://umich.cloud-cme.com/course/courseoverview?P=0&EID=61826

What's inside

Syllabus

Introduction to Artificial Intelligence and Machine Learning

In week 1, you will be introduced to artificial intelligence (AI) and machine learning (ML) and the vocabulary necessary to effectively communicate with relevant stakeholders. You will learn about some of the applications of AI/ML in healthcare and the challenges associated with using these technologies in healthcare.

Traffic lights

Read about what's good

what should give you pause

and possible dealbreakers

Explores core concepts in Artificial Intelligence (AI) and Machine Learning (ML)

Focuses on the use of AI/ML to enhance diagnostic decision-making in healthcare

Covers the ethical and legal considerations of using AI/ML in healthcare

Offers CME credit for participating physicians

Requires background knowledge in biostatistics and epidemiology

Taught by instructors with expertise in AI/ML in healthcare

Reviews summary

Ai for medical decisions: essential overview

According to learners, this course offers a largely positive and highly relevant introduction to AI/ML in medical decision-making. Students appreciate its focus on augmenting diagnostic clinical decision-making and its comprehensive coverage of ethical and legal considerations. Many found the foundational biostatistics and critical appraisal skills modules particularly helpful. The course is especially valued by medical professionals seeking CME credits and a clear overview of AI's role in healthcare. Some learners note that due to its concise nature, the pacing can feel condensed, and those seeking deep technical implementation skills may find it more of a conceptual primer.

Provides essential continuing medical education credits.

"The CME credits were a major factor in my decision, and the content fully justified the time spent on this course."

"A highly relevant course for medical professionals, especially for earning the necessary CME credits."

"The 3.5 AMA PRA Category 1 Credit(s)™ are a significant bonus for busy clinicians needing to fulfill requirements."

Develops essential skills for evaluating AI/ML studies.

"The foundational biostatistics module was a good refresher and helped me interpret ML model performance effectively."

"I found the instructors did a fantastic job explaining how to interpret study results and evaluate AI models."

"This course was very helpful for understanding the critical appraisal of AI in medical literature and its applicability in practice."

Covers vital ethical and legal implications of AI in medicine.

"The module on ethical and legal considerations was particularly insightful and practical for real-world application."

"The module on ethical and legal considerations was eye-opening and crucial for anyone implementing AI in healthcare."

"I appreciated the strong focus on the legal and ethical aspects; this section is indispensable for responsible practice."

Incredibly timely and essential for medical professionals.

"This course was exactly what I needed as a practicing physician. It provided a clear and concise overview of AI/ML's role in diagnostics."

"This course is incredibly timely and relevant, providing a much-needed foundation in AI for medical practice."

"As a practicing physician, I found the content tailored perfectly to my needs, bridging the gap between clinical practice and AI."

Offers a broad introduction, not deep technical detail.

"My only minor critique is that some sections felt a bit rushed given the depth of the material covered."

"For a data scientist like myself, it lacked the technical depth I was hoping for. It's clearly geared more towards clinicians."

"It serves its purpose as an introduction, but don't expect to become an AI expert or gain hands-on coding skills."

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Data Augmented Technology Assisted Medical Decision Making with these activities:

Connect with professionals working in the field of AI and healthcare

Show steps

Finding mentors can help you gain insights into the field, learn about career opportunities, and get valuable feedback on your progress.

Browse courses on Artificial Intelligence

Show steps

Attend industry events and conferences.
Reach out to professionals on LinkedIn.
Ask your professors or colleagues for recommendations.
Set up informational interviews.

Review MIT OpenCourseWare videos on Machine Learning

Show steps

These MIT videos will help you build a strong foundation in machine learning, which is essential for understanding the use of AI in medical decision-making.

Browse courses on Supervised Learning

Show steps

Solve practice problems on AI and machine learning

Show steps

Solving practice problems will help you develop your problem-solving skills and reinforce your understanding of AI and machine learning concepts.

Browse courses on Supervised Learning

Show steps

Find practice problems online or in textbooks.
Solve the problems on your own.
Check your solutions against the answer key.
Review your mistakes and learn from them.

One other activity

Expand to see all activities and additional details

Show all four activities

Compile a list of resources on AI and healthcare

Show steps

Compiling a list of resources will help you stay up-to-date on the latest developments in AI and healthcare.

Browse courses on Artificial Intelligence

Show steps

Search for resources on AI and healthcare.
Evaluate the resources and select the most relevant ones.
Organize the resources into a list.
Share the list with others.

Career center

Learners who complete Data Augmented Technology Assisted Medical Decision Making will develop knowledge and skills that may be useful to these careers:

Reading list

We haven't picked any books for this reading list yet.

Data Augmented Technology Assisted Medical Decision Making

Here's a deal for you

What's inside

Syllabus

Traffic lights

Save this course

Reviews summary

Ai for medical decisions: essential overview

Activities

Career center

Reading list

Share

Similar courses