Welcome Address by Prof. Roberto J. Lavarello, IEEE EMBS VP Member & Student Activities, 2024

Remarks Delivered by Dr. NYI NYI TUN, IEEE EMBS SAC PDP Chair 2024

Remarks Delivered by Dr. Polat Goktas, IEEE EMBS Summer Camp Chair 2024

Healthcare Innovation, Entrepreneurship and AI, by Prof. Metin Akay, IEEE EMBS Chief Ambassador, 2024 & Past President, IEEE EMBS 2023-2024

Summary: Engineering and technological innovations have rapidly improved healthcare, enhancing quality and reducing costs. This shift requires a new generation of professionals with expertise in both medical and engineering fields, supported by interdisciplinary collaboration.

Data science plays a crucial role in addressing healthcare challenges, offering insights into health and disease while driving advancements in care.

This talk will explore AI's impact on healthcare innovation, challenges, opportunities, and its integration into biomedical engineering education.

Ad-Hoc Members: Kathan Vyas (Moderator), Gabriela Abigail Lazo de la Cruz (Supporter)

Multimodal Generative Models, by Prof. Cesar Beltran, Principal Professor and Senior Researcher, Pontificia Universidad Católica del Perú & President, IEEE Peru Section 

Summary: Multimodal generative models are revolutionising AI by creating original content in diverse modalities such as text, images and music. These models learn from multiple types of data, enabling them to generate surprising and realistic results. Their applications range from art and entertainment to communication and science, offering enormous potential to transform the way we interact with technology and the world around us.

Ad-Hoc Members: Kathan Vyas (Moderator), Gabriela Abigail Lazo de la Cruz (Supporter)

AI-Driven Diagnosis and Treatment in Telemedicine by Mr. Muraleedharan P. G., CEO, Super Intel Innovation

Summary: In this tutorial, it will cover: Transforming Healthcare with AI: From Diagnosis to Treatment, Focusing on Cardiovascular Risk Prediction.

Ad-Hoc Members: MennatAllah Zain El-Deen (Moderator), Gabriela Abigail Lazo de la Cruz (Supporter)

Hands-on AI Workshop: Imputing Missing Data with GANs & GAIN! by Mr. Antoni Grzanka, Biomedical Engineering, Medical University of Warsaw, Chair of Polish Section of IEEE EMBS

Summary: This workshop is perfect for anyone interested in learning how AI can be used to tackle the real-world challenge of incomplete datasets in healthcare.

Starting with GANs, participants will explore how these powerful networks generate synthetic data by training a generator and discriminator to produce realistic data samples.

You’ll gain experience setting up a GAN model, training it on nutritional data, and understanding how it learns to handle missing values.

Next, we’ll delve into GAIN, a specialized form of GAN designed specifically for data imputation. You’ll work through practical exercises on imputing missing dietary information, learning how GAIN captures patterns in incomplete data and generates accurate, high- quality imputations.

Finally, we’ll consolidate our learning in the Imputation.ipynb notebook, where you’ll compare and validate the effectiveness of different imputation techniques using real-world datasets. You’ll leave the session with hands-on experience and a solid understanding of how GANs and GAIN can be applied to address missing data problems.

Ad-Hoc Members: Gabriela Abigail Lazo de la Cruz (Moderator), MennatAllah Zain El-Deen (Supporter)

Ultrasound Simulation with Physics-Conditioned GAN

Speakers: Dr. Debdoot Sheet, Department of Electrical Engineering, Indian Institute of Technology Kharagpur

Raj Krishan Ghosh, Department of Artificial Intelligence,  Indian Institute of Technology Kharagpur

Abhishek Kumar, Department of Artificial Intelligence,  Indian Institute of Technology Kharagpur

Niharika A Raj, Department of Electrical Engineering, Indian Institute of Technology Kharagpur

Summary: Medical imaging simulators help healthcare professionals refine their skills. However, physics-based simulators are computationally expensive and inflexible, while signal processing-based simulators, though faster, lack realism. Deep learning (DL) simulators, trained on real data, improve accuracy but struggle with limited datasets, especially in ultrasound. This tutorial combines ultrasound physics with DL, using signal processing simulations to condition a Generative Adversarial Network (GAN) for generating realistic ultrasound images.

Ad-Hoc Members: Md Sajid (Moderator), Kathan Vyas (Supporter)

Advancing Brain-Computer Interfaces (BCI): Overcoming Challenges in Transfer Learning by Ozgur Ege Aydogan, Research Assistant,  Osaka University

Summary: This workshop will explore the application of transfer learning in brain-computer interfaces (BCIs) to improve motor function for individuals with motor impairments. Focusing on Electrocorticography (ECoG) data, participants will learn how machine learning models, such as Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks, can be trained on real motor execution data and applied to motor imagery tasks.

Ad-Hoc Members: Eashita Chowdhury (Moderator), Moqaddaseh Afzali (Supporter)

Innovations in Telemedicine by

Speakers: Mr. M.G Bijoy, Managing Director - Unarv Telemedicine and Healthcare Services Ltd

Mr. Vimal Wakhlu, Former Chairman and MD – Tele Communications India Ltd (Govt. India), Board Member - International Society for Telemedicine & e Health, Vice President Digital Communication India Forum

Summary: In this tutorial, we will dive into key topics such as:

• History and Evolution of Telemedicine: A look at the journey of telemedicine, from its origins to its modern-day applications.

• AI in Telemedicine: Exploring cutting-edge technologies and how AI is revolutionizing telemedicine with smarter diagnostics and patient care.

• Impact of AI-Enabled Telemedicine: How AI is transforming healthcare delivery, improving decision-making, and enhancing efficiency.

• Challenges: Discussing the technical and regulatory barriers to AI-driven telemedicine adoption.

  
  

Ad-Hoc Members: Rishi Kant Thakur (Moderator), Eashita Chowdhury (Supporter)

Advancing Neural Engineering with AI-Driven Innovations
by Xiaoxiao Sun, Columbia University

Summary: This workshop session will provide a comprehensive overview of the integration of Artificial Intelligence (AI) in neuroengineering, focusing on key areas such as Brain-Computer Interfaces (BCI), neuroimaging analysis, and the role of AI in disease prediction and biomarker identification. The session will conclude with a hands-on workshop, where participants will have the opportunity to engage with live code demonstrations.

Ad-Hoc Members: Mereum Fernando (Moderator), Eashita Chowdhury (Supporter)

AI-Driven Diagnosis and Treatment in Telemedicine by Dr. Vivek Nambiar, Vice President, Telemedicine society of India, Kerala & Head-Tele Neurology Amrita Institute of Medical Science and Research Center

Summary: In this tutorial, I will cover:

• Overview of Teleneurology: Its evolution and applications in remote care.

• Key Use Cases: Demonstrating improved patient access and outcomes.

• AI in Neurology: Advancements in diagnostics and treatment.

• Intersection of AI and Teleneurology: Enhancing remote assessments.

• Case Studies: Real-world applications of AI in teleneurology.

• Challenges and Ethical Considerations: Data privacy and access issues.

• Future Trends: Innovations shaping AI-enabled teleneurology.

  
  

Ad-Hoc Members: Md Sajid (Moderator), Mereum Fernando (Supporter)

Generative AI in Healthcare: Practical Imputation of Dietary Data Using GAN by Mr. Antoni Grzanka, Biomedical Engineering, Medical University of Warsaw, Chair of Polish Section of IEEE EMBS

Summary: The session will dive deep into the critical issue of missing data in dietary records, a common challenge in medical research. We’ll introduce you to the concept of Generative Adversarial Networks (GANs), an advanced AI technique that can accurately impute missing data and enhance the quality of healthcare datasets.

Our speaker, Antoni Grzanka, Chair of the Polish Section of IEEE, will guide you through the practical aspects of working with GANs, using TensorFlow and PyTorch frameworks. You will learn how to handle missing data in healthcare, understand the role of GANs, and discover the challenges and opportunities in applying AI to dietary data.

This session offers a unique blend of theoretical insights and practical knowledge, making it an excellent opportunity to gain hands-on experience with cutting-edge AI tools. You’ll leave with valuable skills in healthcare data analysis and a clear understanding of how AI can transform healthcare practices.

Ad-Hoc Members: Md Sajid (Moderator), José Quintero (Supporter)

Innovations in Telemedicine by Mr. Gauba, CEO, MD, Country Head IoT, Digital Health, Semiconductors, Power Sectors

Summary: In this tutorial, I will explore:

• Understanding of LoRa Technology

• LoRa and LoRaWAN in Telemedicine

• Applications of LoRa in Remote Healthcare

• Advantages and Future Prospects of LoRa

• Integration with 5G, AI, and Edge Computing

• Case Studies of Telemedicine Innovations

Ad-Hoc Members: Rishi Kant Thakur (Moderator), Eashita Chowdhury (Supporter)

Artificial Intelligence in Fetal and Newborn Health Monitoring by Dr. Ethan Grooby, Postdoctoral Researcher, McGill University & Lead, IEEE EMBS SAC  Student Activities

Summary: This webinar is an overview of artificial intelligence applied to fetuses and newborns. It will discuss the future direction of monitoring technology within fetal and newborn health monitoring suitable for artificial intelligence. Then go over the key software processing steps for analysing digital stethoscope and video recording data of newborns, and clinical/CTG data from fetuses and the mother. Finally conclude with future directions for access to data and code.

Ad-Hoc Members: José Quintero (Moderator), MennatAllah Zain El-Deen (Supporter)

• Remarks for SAC by Nicole Caballero, Chair, IEEE EMBS SAC 2024

• Remarks (Thanksgiving speech) by Dr. NYI NYI TUN,  IEEE EMBS SAC PDP Chair 2024

• Remarks (Future SC/W perspective) by Dr. Polat Goktas, IEEE EMBS Summer Camp Chair 2024

• Photo session and Adjournment - Closing!

Integrating Generative AI into BCI: Transforming Healthcare with Intelligent Systems by Associate Prof. Athanasios Koutras, Electrical & Computer Engineering Dept., University of Peloponnese, Greece

Summary: This presentation explores the exciting intersection of Brain-Computer Interfaces (BCIs) and Generative AI, showcasing how these advanced
technologies are reshaping the future of healthcare. BCIs, which enable direct communication between the brain and external devices, have become invaluable for assisting individuals with motor disabilities and enhancing neural rehabilitation. The presentation will provide an
overview of how brain signals are recorded using electroencephalography (EEG), processed, and translated into commands for various applications,
from communication devices to neuroprosthetics.

Generative AI plays a pivotal role in enhancing BCI systems by improving signal processing, reducing noise, and boosting the accuracy of neural decoding. We will explore different models of Generative AI, such as
GANs and VAEs, and their potential to create more natural and responsive interactions between users and technology. Real-world case studies will demonstrate how these innovations are being applied in fields like neuroprosthetics and communication devices, enabling more seamless control and enhancing user experience.

Looking to the future, the presentation will address emerging trends, including the integration of BCIs with technologies like augmented reality (AR) and virtual reality (VR). These developments hold significant promise for transforming healthcare, offering new possibilities for personalized treatments and improved quality of life.
Ethical considerations and the need for interdisciplinary research will also be emphasized, highlighting the importance of responsible development in this evolving field.

Ad-Hoc Members: Eashita Chowdhury (Moderator), Moqaddaseh Afzali (Supporter)

Generative Models for Healthcare by Prof. May Dongmei Wang, Wallace H. Coulter Distinguished Faculty Fellow and full professor of BME, ECE, CSE at Georgia Institute of Technology and Emory University

Summary: Despite their success in general-domain tasks, LLMs have encountered significant challenges in medical domains before April 2023, particularly with real-world clinical tasks. Medical data is typically private, while LLMs are primarily pre-trained on web-searched data. Clinical applications of LLMs usually require additional information and expertise beyond what is included in their pre-trained data. Moreover, generative models are susceptible to producing hallucinated information and often struggle with logical reasoning in the context of complex inferences. In addition, other concerns, such as computational costs and model transparency, further impede the broad adoption of LLMs in real-world clinical settings. To solve the lack of domain knowledge in LLMs for healthcare, we developed a retrieval-augmented framework to augment LLMs with the most recent domain-specific medical knowledge. This is one of the first retrieval-augmented solutions in clinical settings, and has received the Best Paper Award in ACM SIGBio 2023. After enhancing the knowledge of LLMs for clinical tasks, we used toward EHRs, which are typically relational databases with heterogeneous patient data of both administrative and clinical information in the unique circumstances of individual patients or specific groups. Thus, we developed  EHRAgent, an LLM agent augmented with external tools and medical knowledge to solve few-shot multi-tabular reasoning derived from EHRs, and enabled the LLM agent to formulate a clinical problem-solving process as an executable code plan of action sequences along with a code executor. By introducing interactive coding between the LLM agent and code executor, we leveraged environmental feedback to improve code generation for tabular reasoning tasks in EHR by up to 36% in comparison with the state-of-the-art agent AutoGen. All the current progresses have been accepted for top tier AI conferences such as Annual Meeting of the Association for Computational Linguistics (ACL 2024) and the Empirical Methods in Natural Language Processing (EMNLP).

Ad-Hoc Members: José Quintero (Moderator), MennatAllah Zain El-Deen (Supporter)