From being a researcher in Computational Biology & Structural biology/ Biophysics to a Senior data scientist, Sreetama Das has gained a wide knowledge base and worked in diverse industries like manufacturing and healthcare. Analytics India Magazine caught up with Das, who is currently serving as a Senior AI ML Engineer at GSK, to understand her insights on AI-based solutions in digital health.
AIM: Given your experience in a broad career path from data science to AI and machine learning, what do you think will be the future of the tech-medical space?
Sreetama Das: To briefly mention my background, I have worked with data science and machine learning applications to biomolecules as part of academic research during my PhD, and then worked in diverse industries like manufacturing and healthcare. This experience has shaped my view of what I think the field will evolve to be.
Several areas in the tech-medical space use machine learning for improved outcomes – for example, research and development for drug discovery or repurposing, optimising clinical trials, manufacturing and quality control, digital health sensors, digital pathology patient triaging, to name a few. The data could be numeric, structured nicely in tables, or huge images or even messy text – that is what makes problem statements in healthcare exciting but challenging. Advancements in computer vision and natural language processing will improve solutions or even solve problems that were not feasible earlier. In addition, solutions focussing on explainability will find more acceptance. The overall trend is to move towards faster development, efficient manufacturing, better quality control, and easier access to health monitoring, disease detection, and patient treatment outcomes. In short, AI-based solutions will be assisting to improve lives.
AIM: There have undoubtedly been significant advancements in the field of digital health. Is there a particular situation that prompted you to conduct additional research?
Sreetama Das: Digital health has seen significant advancements in developing digital sensors for health monitoring and detection of diseases. I will cite an example from a project I was part of at my earlier organisation (Robert Bosch Engineering and Business Solutions, India) since I have been with GlaxoSmithKline for only a short time. Back there, the team developed a novel, non-invasive haemoglobin monitoring sensor based on photoplethysmography. We collected data from many participants, both healthy and frail, and trained machine learning models. As a result, our device performed better than some existing non-invasive hemoglobinometers, which tended to overestimate haemoglobin levels. The project required a lot of research, and our results have been published in several reputed scientific journals.
AIM: Enterprises began migrating away from on-premises data centres and applications toward cloud and SaaS-based solutions. How feasible is this transition to the cloud in light of the numerous security concerns and ransomware attacks occurring?
Sreetama Das: I am not an expert in this area, but I am aware of private commercial cloud and hybrid cloud offerings that combine the advantages of cloud with the security of on-premise solutions and are used to address regulatory concerns. Moreover, we are starting to see the use of blockchain in commercial cloud solutions and upcoming technologies like federated learning and its use in med-tech. So, I think the transition is feasible with well-thought-through strategies.
AIM: In what we call a “combination of AI and Digital Health”, what are some near or so groundbreaking developments that carry future potential as well?
Sreetama Das: One of the groundbreaking developments in recent …….