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Workshop on Artificial Intelligence- Artificial/Augmented Intelligence Applications in Education & Research

Administrator: Biju Hameed

Use of Artificial Intelligence in Medical Education – leveraging large language models to educate students, reducing documentation burden

The symposium's first topic explores the use of artificial intelligence in medical education, specifically how large language models can be utilized to educate students and ease documentation burdens. The goal is to enhance the work of health professionals, not replace them. Given the already heavy curriculum in health professions, incorporating AI competencies for learners can be challenging and may lead to negative effects on student well-being and faculty burnout. It's crucial to determine how AI complements existing content and assess the relative impact of all content areas on future practice. The information explosion in health professions creates information overload, overwhelming students' and educators' cognitive and mental capacity. A shift in teaching methods, moving from the traditional model of information acquisition to one focused on knowledge management is likely necessary.

Another crucial aspect of utilizing AI in medical education is teaching clinicians how to evaluate its performance, including the potential for bias and generalizability of AI applications. In addition, clinicians need to understand the limitations of AI algorithms, particularly when it comes to generalizability across different populations or contexts. To ensure the ethical and effective use of AI in healthcare, it's essential to provide education and training that covers these issues and empowers clinicians to evaluate the performance of AI algorithms in their specific practice settings.

 

How Artificial Intelligence assists research – advancing biomarkers, new screening and diagnostic tools, therapeutic paradigms, and advanced prognostication/precision medicine
Sheffali Gulati

The second topic of the symposium examines how artificial intelligence (AI) can assist in medical research and advance various areas such as biomarkers, screening and diagnostic tools, therapeutic paradigms, and precision medicine. The combination of AI and precision medicine has the potential to transform healthcare, particularly by identifying patients with unique healthcare needs or uncommon responses to treatment. AI utilizes advanced computation and inference techniques to generate valuable insights, learn from data, and enhance decision-making by clinicians through augmented intelligence. Ongoing research in AI and precision medicine is paving the way for a future where medical professionals and patients alike can benefit from highly personalized diagnostic and therapeutic information. This synergistic relationship between AI and precision medicine ultimately aligns with the goal of preventing and detecting diseases early, reducing disease burden and the cost of preventable healthcare for all.

 

Ethics, bias and other issues of concern in Artificial Intelligence
Biju Hameed

The third topic of the symposium addresses important issues related to the ethics, bias, privacy, and security of artificial intelligence (AI) systems. Despite the rapid pace of growth in AI technology, concerns regarding these issues have not always been given sufficient attention. One key area of concern is the potential for bias in AI systems, which can result in output that favors certain data sets over others. To mitigate this risk, organizations must identify how bias can arise in AI systems and establish internal controls to address the issue. Another critical concern is the ethical use of AI algorithms, which requires transparency and accountability. Researchers in the field of AI argue that ethical considerations should be a primary driver in the development and adoption of AI technology. This approach would ensure that AI is used in a responsible and ethical manner that promotes the well-being of individuals and society as a whole. By prioritizing ethical considerations, we can ensure that the potential benefits of AI are realized without compromising the values and principles that underpin our society.
 

Basic techniques of applied Artificial Intelligence – EEG technical point of view as use case
Tapan Gandhi

The fourth topic of the symposium focuses on the basic techniques of applied Artificial Intelligence (AI) with a particular use case from the EEG technical point of view. Recent advancements in neuroscience and AI have facilitated the communication and interaction of the human brain with the environment, making brain-computer interface (BCI) a highly interdisciplinary field of research. By understanding brain signals and applying AI for the smart analysis and decoding of neural activity, researchers are advancing the field of BCI. During this topic, attendees will be shown how brain signals can be decoded, interpreted, and applied to a range of diagnostic and rehabilitative purposes for various neurodevelopmental problems. The session will also showcase the latest applications of BCI and brain-machine interface (BMI) in domains such as medicine and rehabilitation, further highlighting the potential of AI to revolutionize the field of neuroscience. Understanding Brain Signals and bringing Artificial intelligence for the smart analysis and decoding of neural activity, has turbocharged the field of brain-computer interfaces. We will demonstrate the brain signal decoding, interpretation and applications ranging from diagnostics to rehabilitation in typical neurodevelopmental disorders and applications in BCI/BMI domains.