Technology enables researchers to collect data in a standardized way, normalize it and access it faster than before. With technologies like ours (www.medicai.io), access to medical imaging data for oncology clinical studies is almost real-time and anonymized. Data is also collected from multiple clinical trial locations. Thanks to technology, medical research can now be done at a larger scale than previously. for example, Apple Research Kit enables researchers to conduct studies with large numbers of participants from almost every geography of the world.
In my work, technology plays a central role in advancing medical research, particularly in developing AI-driven diagnostic tools for personalized healthcare. Over the years, technology has evolved from simple data collection systems to sophisticated platforms that analyze and interpret complex medical data in real-time. Initially, we relied on traditional software and hardware for basic data tracking and patient management. However, with the emergence of machine learning and AI, we've shifted to more advanced solutions capable of detecting subtle patterns in patient data, which can inform earlier diagnoses and targeted treatment strategies. A notable evolution is in the area of Software as a Medical Device (SaMD) solutions. Earlier technologies primarily focused on monitoring basic vitals, while today's systems, powered by AI and machine learning, can assess patient risk profiles, predict disease progression, and even recommend treatment adjustments. In my work on diabetes and neurological disorders, we use digital biomarkers derived from AI-driven data analytics to gain real-time insights into patient health, which supports more responsive and individualized care. The pace of technological advancement has also improved the scalability of research. Cloud computing, for instance, enables us to analyze vast datasets from global trials, allowing for faster validation and deployment of new solutions. As technology continues to advance, I anticipate even greater opportunities for automation, precision, and integration across the medical field, ultimately leading to more accessible and effective healthcare.