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Medical AI is an exciting frontier where technology and healthcare converge, and as a PhD student at King’s College London, I’m fortunate to be at the heart of this transformative field. My work focuses on addressing inefficiencies in radiology workflows through the smart orchestration of radiology data using artificial intelligence (AI). Every day presents a new challenge and an opportunity to innovate, collaborate, and make a difference.
Morning: Kicking Off with Purpose
The morning is my time to get focused and map out the day’s tasks. My research involves tackling a long-standing challenge in radiology departments: ensuring data flows seamlessly and reliably. Radiology data is vast, complex, and often fragmented across multiple systems. Currently, much of the orchestration relies on DICOM tags, metadata embedded within medical images that indicate critical details like modality, anatomy, and scan parameters.
The problem? These tags are sometimes incomplete or incorrect, creating bottlenecks that slow down the processing of images. In urgent situations, these delays can lead to wasted time, increased costs, and potential risks for patients. My research seeks to address this by combining AI with multimodal data inputs, including images, DICOM tags, and contextual information, to create a smarter, more resilient system.
Mornings are often spent reviewing recent experiments, writing code, or diving into new datasets. Data exploration is like detective work, sifting through patterns and anomalies to uncover insights that can make AI models more robust.
Mid-Morning: Building Intelligent Systems
Once the groundwork is set, the real fun begins: training AI models to address these challenges. My approach integrates foundational models and advanced neural networks to make data orchestration more intelligent. Foundational models, known for their ability to generalize across tasks, are particularly exciting as they allow us to leverage pre-learned knowledge while adapting to the specific needs of radiology workflows.
This isn’t just about getting the models to work; it’s about ensuring they’re scalable, interpretable, and adaptable to real-world hospital settings. For example, I might test how well a model can identify key patterns in data when certain tags are missing or incorrect. Each experiment is a stepping stone toward creating a system that not only works in theory but excels in practice.
Collaboration: Where Innovation Meets Real-World Needs
AI research doesn’t happen in isolation. Collaboration is a cornerstone of my work. Engaging with clinicians, radiologists, and other researchers provides invaluable insights into the practical challenges faced in hospitals. These conversations ensure that the solutions we design are grounded in reality and can make a meaningful impact.
For instance, a radiologist might highlight specific scenarios where incorrect metadata caused delays, giving me the context needed to refine the model. This iterative process, learning from healthcare professionals and integrating their feedback into the AI system, is what makes the work so rewarding. With the Researcher Suite, this collaboration becomes more structured. Radiologists can review model outputs directly, record feedback in standardized formats, and provide context that feeds back into the research process.
Afternoon: Teaching and Mentoring
As part of my PhD program, I also engage in teaching assistantship roles. Supporting undergraduate and master’s students in their journey through AI and machine learning is one of the most fulfilling parts of my day.
Teaching is not just about imparting knowledge; it’s about inspiring curiosity and fostering critical thinking. I might guide students through debugging their code, explain concepts like convolutional neural networks, or discuss the ethical considerations of deploying AI in healthcare. These interactions are a two-way street; they often challenge me to think differently and help refine my own understanding of the field.
Late Afternoon: Fine-Tuning and Analysis
The latter part of the day is dedicated to refining models, analyzing results, and troubleshooting challenges. AI research is an iterative process, where setbacks and successes are both part of the journey. Fine-tuning parameters, testing alternative architectures, and examining why a particular model didn’t perform as expected are all part of the daily routine.
This phase requires patience and creativity. It’s about asking the right questions: Why didn’t this model generalize well? What can be done to reduce artifacts in the reconstructed data? How can we balance computational efficiency with accuracy? deepcOS® Researcher Suite supports this process with monitoring dashboards that track performance trends and highlight where models need improvement.
Connecting Research to Impact
The ultimate goal of my research is to create solutions that translate into real-world impact. Smarter radiology workflows mean faster processing times, reduced costs, and most importantly, better patient outcomes. By reducing reliance on imperfect metadata and incorporating multimodal data inputs, we’re paving the way for a more resilient healthcare system.
This vision aligns closely with deepc, a company I had the privilege of working with. Their Researcher Suite provides researchers with a unique opportunity: the ability to test AI models in a hospital-like environment without disrupting clinical operations. This bridges the gap between academic research and practical application, enabling validation under realistic conditions.
For a project like mine, this is invaluable. The ability to safely test AI models in a controlled yet realistic setting ensures that they are not only theoretically sound but also practically viable. The Researcher Suite helps accelerate the transition from innovation to implementation, bringing AI solutions closer to benefiting patients and healthcare providers.
Evening: Reflecting on Progress and Looking Ahead
As the day comes to a close, I take time to reflect on the progress made and the challenges ahead. Research is rarely a straight path; it’s a journey of exploration, discovery, and continuous learning.
Some evenings are spent brainstorming new ideas, reading papers to stay updated on the latest advancements, or simply jotting down thoughts for the next steps in the project. The dynamic nature of AI research keeps every day exciting and full of possibilities.
Closing Thoughts
Life as a medical AI researcher is a blend of technical rigor, creative problem-solving, and meaningful collaboration. It’s about pushing the boundaries of what’s possible while staying grounded in the practical needs of healthcare.
For those curious about the field, it’s a journey filled with opportunities to make a real difference. It is built on the same secure, enterprise-grade foundation that hospitals use for commercial AI, which makes the step from prototype to patient care both safer and faster. And for researchers eager to translate their work into clinical impact, the deepcOS® Researcher Suite provides an unparalleled opportunity to bridge the gap between lab and clinic, ensuring that innovations do not just remain ideas but become solutions that improve lives.
