Speakers - 2027

Title: Smart diagnostistics :Digital tools for precision health

Abstract

Introduction:
Healthcare worldwide faces a heavier workload due to ageing populations, rising multimorbidity, increased complexity of healthcare, and personnel shortages. Primary care bears the full brunt of this pressure because it’s usually the first port of call Also, the number of laboratory tests is unnecessarily rising because of this pressure Healthcare needs transformation, ensuring access and continuity even in limited access to traditional face-to-face services. Advocates of digital health propose that digital technologies can facilitate this transformation, improving care from a quadruple-aim perspective (better patient experiences, health outcomes and professional satisfaction at lower costs

Digital Health: health services and information delivered or enhanced through the Internet and related technologies’

Its complexity can be brought down to three domains u consumer-driven and consumer-controlled technologies (e.g. wearables and apps), u digital tools for health stakeholders to interact with each other (e.g. telemedicine and messaging systems), u technologies that improve health and health services through data (e.g. data management systems and repositories).

Smart Digital Diagnostic Tools Definition: A smart digital diagnostic tool for precision healthcare refers to advanced medical technologies that use digital tools to deliver highly accurate, personalized, and realtime diagnosis. The core principle of digital diagnostics is that the traditional role of healthcare professionals (HCPs) is partially or wholly replaced or facilitated by digital systems

Precision healthcare aims to deliver personalized medical treatment based on an individual’s genetic makeup, lifestyle, and environment. A Smart Digital Diagnostic Tool (SDDT) integrates advanced technologies to enhance diagnostic accuracy, speed, and accessibility.

Objectives:

• Enable early and accurate disease detection
• Provide personalized diagnostic insights
• Reduce healthcare costs and diagnostic delays
• Improve patient outcomes through data-driven decisions

Technologies Used

• Artificial Intelligence & Machine Learning
• Internet of medical Things (IoT)
• Big Data Analytics u Cloud Computing
• Blockchain (for data security)
• AI AND MACHINE LEARNING (ML)
• Transforming patient care by accelerating diagnostics, enabling personalized medicine, and streamlining administrative tasks

Key Applications of AI/ML in Healthcare:

Medical Imaging & Diagnostics: AI algorithms, specifically deep learning, can analyze medical images (X-rays, CT scans, MRIs) with accuracy comparable to specialists in detecting diseases such as skin cancer and pneumonia.

Predictive Analytics:

AI models (e.g., in radiology) often outperform humans in diagnosing conditions from images, aiding early detection