Problem-Based Learning (PBL) has significantly impacted biomedical engineering (BME) education since its introduction in the early 2000s, effectively enhancing critical thinking and real-world knowledge application among students. With biomedical engineering rapidly converging with artificial intelligence (AI), integrating effective AI education into established curricula has become challenging yet increasingly necessary. Recent advancements, including AI's recognition by the 2024 Nobel Prize, have highlighted the importance of training students comprehensively in biomedical AI. However, effective biomedical AI education faces substantial obstacles, such as diverse student backgrounds, limited personalized mentoring, constrained computational resources, and difficulties in safely scaling hands-on practical experiments due to privacy and ethical concerns associated with biomedical data. To overcome these issues, we conducted a three-year (2021-2023) case study implementing an advanced PBL framework tailored specifically for biomedical AI education, involving 92 undergraduate and 156 graduate students from the joint Biomedical Engineering program of Georgia Institute of Technology and Emory University. Our approach emphasizes collaborative, interdisciplinary problem-solving through authentic biomedical AI challenges. The implementation led to measurable improvements in learning outcomes, evidenced by high research productivity (16 student-authored publications), consistently positive peer evaluations, and successful development of innovative computational methods addressing real biomedical challenges. Additionally, we examined the role of generative AI both as a teaching subject and an educational support tool within the PBL framework. Our study presents a practical and scalable roadmap for biomedical engineering departments aiming to integrate robust AI education into their curricula.

Abstract-- Understanding the relationship between muscle activation and thickness deformation is critical for diagnosing muscle-related diseases and monitoring muscle health. Although ultrasound technique can measure muscle thickness change during muscle movement, its application in portable devices is limited by wiring and data collection challenges. Experimental results with six healthy subjects showed that the approach could accurately predict muscle excursion with an average precision of 0.923 0.900mm, which shows that this method can facilitate real-time portable muscle health monitoring, Our proposed method employs a novel dual-attention framework to correlate muscle activation with thickness I. INTRODUCTION This framework included hierarchical selfattention Quantifying the relationship between muscle activation [12] and cross-attention [13] mechanisms. Selfattention and thickness deformation is essential for understanding captured long-range signal dependencies and dynamically muscle dynamics and health [1], [2], particularly in conditions adjusted the importance of different signal components such as Facioscapulohumeral Dystrophy [3]. Traditional [14], while cross-attention merged and synthesized ultrasound imaging can visualize muscle thickness these features to provide comprehensive MTD information.

Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions. Once pretrained, large AI models demonstrate impressive performance in various downstream tasks. A prime example is ChatGPT, whose capability has compelled people's imagination about the far-reaching influence that large AI models can have and their potential to transform different domains of our lives. In health informatics, the advent of large AI models has brought new paradigms for the design of methodologies. The scale of multi-modal data in the biomedical and health domain has been ever-expanding especially since the community embraced the era of deep learning, which provides the ground to develop, validate, and advance large AI models for breakthroughs in health-related areas. This article presents a comprehensive review of large AI models, from background to their applications. We identify seven key sectors in which large AI models are applicable and might have substantial influence, including 1) bioinformatics; 2) medical diagnosis; 3) medical imaging; 4) medical informatics; 5) medical education; 6) public health; and 7) medical robotics. We examine their challenges, followed by a critical discussion about potential future directions and pitfalls of large AI models in transforming the field of health informatics.

The healthcare sector is an important pillar of every community, numerous research studies have been carried out in this context to optimize medical processes and improve care quality and facilitate patient management. In this article we have analyzed and examined different works concerning the exploitation of the most recent technologies such as big data, artificial intelligence, machine learning, and deep learning for the improvement of health care, which enabled us to propose our general approach concentrating on the collection, preprocessing and clustering of medical data to facilitate access, after analysis, to the patients and health professionals to predict the most frequent pathologies with better precision within a notable timeframe. keywords: Healthcare, big data, artificial intelligence, automatic language processing, data mining, predictive models.

While a robot doctor at the bedside is not on the horizon, data-driven digital health is transforming how we receive care - and society is still playing catch-up on the ramifications. In 2012, Professor Enrico Coiera, Founding Director of the Centre for Health Informatics (CHI) at the Australian Institute for Health Innovation, published a paper titled The Dangerous Decade. In it, he warned that more information and communication technology (ICT) would be deployed into healthcare in the 10 years to 2022 than in the health system's entire history to date. "Systems will be larger in scope, more complex, and move from regional to national and supranational scale," he wrote. "Yet we are at roughly the same place the aviation industry was in the 1950s with respect to system safety."

In this era of Big Data, supercomputing, advanced technology, extensive research, and seemingly non-ending pandemics like COVID-19, Health Informatics (HI) has the potential to minimize the data gap in public health between doctors, scientists, governments, and people. But the question is, "Are we making good use of these large untailored piles of data in the right way? Or, are the traditional computing tools and research procedures sufficient to analyze these data accurately?" These questions have only one answer: Artificial Intelligence (AI), an outstanding combination of computing power with human cognition capable of revolutionizing the healthcare industry[1]. HI is defined as an interdisciplinary study that uses Information Technology (IT) and Data Sciences (DS) in health science studies and practices[2]. But, in the real world, the applications of HI are just not limited to procurement, storage, and inspection of electronic health records (EHRs) only; it has more to offer.

With digitalization disrupting every industry, including healthcare, the ability to capture, share and deliver data is becoming a high priority. Machine learning, big data and artificial intelligence (AI) can help address the challenges that vast amounts of data pose. Machine learning can also help healthcare organizations meet growing medical demands, improve operations and lower costs. At the bedside, machine learning innovation can help healthcare practitioners detect and treat disease more efficiently and with more precision and personalized care. An examination of machine learning in healthcare reveals how technology innovation can lead to more effective, holistic care strategies that could improve patient outcomes.

Prec ision Medicine (PM) is an emerging approach that appears with the impression of changing the existing paradigm of medical practice. Recent advances in technological innovations and genetics, and the growing availability of health data have set a new pace o f the research and imposes a set of new requirements on different stakeholders. To date, some studies are available that discuss about different aspects of PM. Nevertheless, a holistic representation of those aspects deemed to confer the technological pers pective, in relation to applications and challenges, is mostly ignored. In this context, this paper surveys advances in PM from informatics viewpoint and reviews the enabling tools and techniques in a categorized manner. In addition, the study discusses ho w other technological paradigms including big data, artificial intelligence, and internet of things can be exploited to advance the potentials of PM. Furthermore, the paper provides some guidelines for future research for seamless implementation and wide - s cale deployment of PM based on identified open issues and associated challenges. To this end, the paper proposes an integrated holistic framework for PM motivating informatics researchers to design their relevant research works in an appropriate context.

One of the formidable challenges healthcare providers face is putting medical data to maximum use. Somewhere between the quest to unlock the mysteries of medicine and design better treatments, therapies, and procedures, lies the real world of applying data and protecting patient privacy. "Today, there are many barriers to putting data to work in the most effective way possible," observes Drew Harris, director of health policy and population health at Thomas Jefferson University's College of Population Health in Philadelphia, PA. "The goals of protecting patients and finding answers are frequently at odds." It is a critical issue and one that will define the future of medicine. Medical advances are increasingly dependent on the analysis of enormous datasets--as well as data that extends beyond any one agency or enterprise.

While there are some sectors of the tech-driven economy that thrive on rapid adoption on new innovations, other areas become rooted in traditional approaches due to regulatory and other constraints. Despite great advances toward precision medicine goals, the healthcare industry, like other important segments of the economy, is tied by several specific bounds that make it slower to adapt to potentially higher performing tools and techniques. Although deep learning is nothing new, its application set is expanding. There is promise for the more mature variants of traditional deep learning (convolutional and recurrent neural networks are the prime example) to morph into domain-specific tools to bolster healthcare capabilities in new ways. Of course, this is not without a set of challenges, which we will get to in a moment.