Life Science Technology

Track 1

IOT | Internet of Health Things

Discover and hear from top IoT innovators. IoT has evolved with the convergence of multiple technologies including real-time analytics, machine learning, commodity sensors, and embedded systems. Healthcare is being revolutionized: IoT will also be used in healthcare systems for real time monitoring of people at risk. Internet of Health Things explores new dimensions of patient care through real-time health monitoring and access to patients’ health data. This data is a imperative for healthcare stakeholders to improve patient’s health and experiences while making revenue opportunities and improving healthcare operations.

Artificial Intelligence

See and hear about the latest AI hardware and software solutions. Artificial intelligence has changed the face of the healthcare industry. The future of health will be transformed with the radically interoperable data, available thru artificial intelligence (AI). Open, secure platforms are central to the promise of more consumer-focused, prevention-oriented care.  AI will enable major scientific breakthroughs, accelerating the creation of new therapies. AI-enabled digital therapeutics and personalized recommendations will empower consumers to prevent health issues from developing. AI-generated insights will influence diagnosis and treatment choices, leading to safer and more effective treatments.

Machine | Deep Learning

What does Machine Learning offer the Life Sciences Industry? Learn from HUExpo keynotes, sessions & panels. Deep Learning has broad and robust applications in the field of healthcare. Deep learning is a class of machine learning algorithms that uses multiple layers to progressively extract higher-level features from the raw input. For example, in x-ray image processing, lower layers may identify edges, while higher layers may identify further information relevant to a human pathology. This can be done more quickly and accurately than the most advanced technicians. This is but one transformative application of deep learning.

Blockchain

HUExpo explores how Blockchain can be implemented Life Sciences ecosystem. Within the life sciences sector, blockchain use cases are complex to implement, and the rapidly changing landscape of blockchain technology also presents a challenge for companies seeking to adopt. “Blockchain has great potential to improve upon various activities within the life sciences sector,” say Dr, Charles Wright, co-author and PreScouter Technical Director. According to Gartner, as the technology matures, experts believe that mainstream adoption will be rapid. It is predicted that the value added by blockchains will grow to $176 billion by 2025 including non-financial uses in the life sciences. “For life sciences and health care, blockchain has the potential to enhance collaboration, trust, interoperability, traceability, and auditability across a range of functions such as clinical trials, supply chain management, financial transactions, credentialing, and claims processing.”    –Deloitte

Predictive Technology

Hear from experts in their fields about Predictive Technology, dedicated to helping clinicians identify the barriers that impact lifelong health. With the use of large datasets such as a genetic library, genomic mapping and individualized diagnostics. You can build healthier families through innovations in bioscience to deliver personalized medicine. Predictive technology is a body of tools capable of discovering and analyzing patterns in data so that past behavior can be used to forecast likely future behavior.

Simulation of Human Behavior

Behavioral simulation employs a high level of abstraction to model the design. Of the three simulation methods (behavioral, structural, and timing), behavioral simulation runs the fastest but provides the least design information. Behavioral simulation allows you to verify syntax and functionality without timing information. During design development, most verification is accomplished through behavioral simulation. Errors identified early in the design cycle are inexpensive to fix compared to functional errors identified during silicon debug. After the required functionality is achieved, structural and timing simulation methods can be implemented to obtain more detailed verification data.

Robotics

Hear from and network with top thought leaders that are transforming the industry. “The three megatrends are rapidly changing the landscape of robotics and automation in the laboratory and the life science industry from both a technical and a commercial perspective.” Each of these trends stands alone in having a significant impact on the industry, but their interaction is influencing design methods and componentry, as well as the expectations those designs inspire. These trends are: The Aging Population, Personalized Medicine, Outsourced Design.

Cloud Computing

Hear from and top industry experts about the latest cloud computing and solutions helping business reopen, reinvent, and outmaneuver uncertainty. Cloud Computing is the backbone of life sciences. Most of the life sciences companies now face increasing consumer, portfolio, regulatory and operating challenges daily as they carry on their search for innovative health solutions. In order to create and sustain competitive differentiation and market dominance, the life sciences sector must meet the diverse challenges of your regulatory/life cycle strategy today while supporting innovations of tomorrow.