Prof. Dr. Saman Shahid
Biography: Prof. Dr. Saman Shahid deals with the teaching of Physics to BS Computer Science, Data Science, and Electrical Engineering programs. Her doctoral research work was related to brain cancer genetics and the discovery of mutations induced by environmental radiation with bioinformatics tools. She also teaches Environmental Sciences courses to MS/Ph.D. programs at the National University of Computer & Emerging Sciences, NUCES-FAST, and Lahore Campus. She is in charge of the Physics Laboratory for Engineers. She is serving the Department of Sciences & Humanities as Natural Science Coordinator & Course Moderator. She is a Director of the Environment & Health Data Science (EH-DS) Research Laboratory. The following are the main objectives of her research lab: (i) AI health-specific goals: precision medicine, disease diagnosis, treatment & prognosis, clinical decision support for physicians, public health management, and digital health platforms;(ii) data sciences for environment & sustainability: risk assessment, environmental monitoring, climate change modeling, water management, natural hazards, and air pollution. She has published 95 research articles in national & international journals. She has also published 40 abstracts at different national and international conferences. Her current impact factor is 118.9. She is also a peer reviewer and editorial board member of some local and international journals. Her areas of interest are Artificial Intelligence (AI) in Medicine & Environment, Medical/Health Physics, Medical Radiation Physics, Environmental Health, and Bioinformatics.
Talk: Medical Informatics as an Augmented Resource for Physicians.
Abstract: One of the main objectives of sustainable economies and societies is to provide high-quality healthcare to all populations. Doctors are under rising workload and time strain and often spend less time even in the most advanced healthcare systems. Big Data, Web technologies, AI, telemedicine, and Web technologies can all be crucial in boosting diagnostic accuracy and raising the standard of treatment. The COVID-19 epidemic has propelled the growing trend of telemedicine over the Internet. Hospitals and online medical systems generate a huge amount of data that may be used to construct advanced models using Artificial Intelligence (AI) approaches. There is a significant problem in leveraging actual data and images from hospitals to be used for AI-augmented tools for illness detection and other applications. Medical computing research deals with disease diagnostics, disease stage prediction, and automated medical image classifications. For patients to begin therapy as soon as possible, early identification of illness risk is advantageous. Artificial intelligence-based predictive algorithms are utilized for early illness prediction, whereas medical laboratories and physicians are needed for diagnosis and testing. The difficulty of creating a system that can identify different diseases in people is still open, and not much research has been published in this field. A significant problem is the clinical validation and real-time application of prediction algorithms. There is currently huge pressure on public sector healthcare systems, there is an exponential growth of healthcare data, a lack of integrated IoT and AI-assisted data tools for clinical information processing & patient management, and a lack of more expert clinical staff to handle and manipulate the clinical data, and also lack of intuitive tools for clinical data segregation & workflow. Clinical decision support (CDS) systems must be developed in Pakistani hospitals to help physicians and surgeons make immediate choices for early illness prediction and surgical/medical care. Big data from hospital electronic medical records (EMRs), crucial sign data from new wearable medical devices or the Internet of Medical Things (IoMT), and even patient genetics must be managed and analyzed to maximize patient treatment. The barriers to safe storage and scalable processing for big data analytics have recently been overcome by advances in cloud computing, while the development of deep learning and machine learning further advances artificial intelligence in healthcare and medicine.
Artificial intelligence (AI) technology has a lengthy history of development when it comes to applications in the medical industry. Conversely, a few persistent issues and difficulties in the medical domain have also encouraged a variety of research groups to carry out further in-depth analyses of AI. AI technology has become increasingly frequently used in the medical industry due to the development of cutting-edge technologies like the Internet of Things (IoT), cloud computing, big data, and 5G mobile networks. Furthermore, the thorough integration of AI and IoT technology permits the progressive enhancement of medical diagnostic and treatment capacities to deliver public services more efficiently.