Asha K.K is a Product Owner at Siemens Healthineers with over 12 years of experience. She holds a Ph.D. in Medical Image Analysis using AI. She has worked as a Data Scientist on AI-RAD projects, focusing on the regulatory clearance of deep learning algorithms for various Siemens web applications. She published her first paper on deep learning used to reconstruct MR images from k-space data at ISMRM in Paris. This is her AI journey. 

 Can you tell us about your AI journey? 

My journey in artificial intelligence (AI) began in 2016 when I first started exploring the potential of AI in healthcare. I published my first abstract at ISMRM 2017, focusing on MRI image reconstruction using deep learning. This project marked the beginning of my dedication to advancing AI in medical imaging. Over the years, I contributed to the development of multiple AI algorithms for organ and tumor segmentation, with my work being recognized through publications in various journals and conferences. Driven by my passion for innovation, I pursued a Ph.D. focused on designing AI algorithms specifically for medical image processing, further solidifying my commitment to advancing AI in healthcare. 

Following my Ph.D., I worked as a Data Scientist, where I evaluated AI algorithm performance and prepared scientific evaluation reports for regulatory clearance. This experience provided me with a comprehensive understanding of the challenges and responsibilities involved in bringing AI solutions to clinical practice. Currently, as a Product Owner at Siemens Healthineers, I lead the development of Deep Resolve Sharp, a cutting-edge deep learning interpolation algorithm that ensures our AI technologies are both innovative and effective in improving patient care. 

What is your area of expertise in AI, and what made you choose it? 

My expertise in AI primarily lies in developing advanced segmentation and localization algorithms for clinical applications within MRI and CT modalities. Over the years, I have been involved in pioneering prototypes across various oncology topics, which are critical for effective diagnosis and treatment planning. This work has provided me with the opportunity to sharpen my skills and extend my expertise in image reconstruction and interpolation algorithms. These advancements are crucial for improving the clarity and quality of medical images, ultimately supporting a better patient experience. 

How important do you think skilling and upskilling are in a country like India? 

With the rapid advancement in AI technologies and the growing presence of automated solutions across all fields, it's crucial to continually update and sharpen our skills to stay at the forefront of this evolving landscape. In a country like India, where a significant number of young professionals are entering the AI field across various sectors, upskilling in their respective domains becomes even more essential. As technology evolves every day, staying competitive requires a commitment to lifelong learning and adapting to new tools and methodologies. In India, with its vast talent pool and dynamic workforce, investing in upskilling not only ensures personal and professional growth but also contributes to the country's position as a global leader in AI innovation. By continuously enhancing their expertise, individuals can drive the development of cutting-edge solutions that address both local and global challenges. 

Describe some challenges you have faced in reaching where you are now. 

When I started my AI journey in developing deep learning models for healthcare, resources were scarce. Concepts like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) lacked comprehensive open-source materials, and access to high-performance cloud GPUs for training large medical image volumes was limited. Python libraries were fewer in number, and there wasn’t a strong AI community, so I often had to learn on my own. Compared to those days, the abundance of online materials and tools now makes it easier for new learners to dive into AI. Those early challenges taught me resilience and helped me build a solid foundation in AI, which has been crucial in the ever-evolving field. 

In what direction should companies in the AI sphere move to ensure more female participation and leadership? 

Historically, women rarely had the opportunity to study medicine or were not taken seriously. To this day, 94 percent of Nobel Prize winners in Medicine are men. This gender bias still has consequences today. Those who don’t consider the biological and medical differences between the sexes run the risk of incorrectly diagnosing or treating women. AI is a crucial innovation in medical technology. To develop its full potential, biases such as race, gender, age, and socio-economic differences must be addressed and rectified. Training data for AI naturally draws on past cases and records. If the medical data was biased—for example, favoring more cases of men than women, data from developed countries, or being limited to specific ethnic groups—there’s a risk of replicating this bias into the future. Ensuring more female participation and leadership in the AI sphere is crucial to diversify perspectives and focus on inclusive data, leading to better innovation. To achieve this, companies should focus on the following key areas: 

Focusing on Data Quality and Availability: Meticulous collection of data is important for organizations to avoid bias. Data sets should encompass a diverse range of sources, including public clinical registries, medical associations, and trusted research partners. Experts should thoroughly examine data points and enrich them with additional information such as anatomical landmarks, diagnostic indicators, and tumor characterizations. Checking the data quality in several steps and training algorithms with representative data sets can ensure that biased data does not disproportionately influence the results. Solving such system-wide challenges in healthcare can enable equal access to healthcare for everyone, everywhere. 

• Fostering an Inclusive and Supportive Culture: It is essential to create an environment where diversity is valued, and women feel welcomed and supported. Offering equal opportunities and establishing mentorship and sponsorship programs specifically for women are key steps. At Siemens Healthineers, we actively promote female leaders in AI through various initiatives that support and empower them in their careers. Diversity in gender leads to diversity in thought, and hence, diversity in ideas to solve complex healthcare challenges. 
• Addressing the Gender Gap Early and Offering Flexible Work Policies: Partnering with educational institutions to encourage young women to pursue careers in AI through internships and early exposure is vital. In addition to this, providing flexible work arrangements, such as remote work and family-friendly policies, allows women to balance professional and personal responsibilities more effectively. At Siemens Healthineers, our flexible work environment plays a crucial role in helping female employees grow in their career paths. 
• Promoting Visibility, Recognition, and Equity: Companies should highlight female role models in AI leadership, ensure equal pay and fair recognition, and provide platforms for women to showcase their achievements. This not only inspires future generations but also fosters a more inclusive and equitable environment where women can thrive and lead in AI. 

In a country like India, where women are bound by societal constraints, how do you think they should break stereotypes? What are your words of wisdom for young girls who wish to build careers in AI? 

In a country like India, where societal constraints often limit women's aspirations, breaking stereotypes requires resilience, determination, and strong support from both family and society. Women need to believe in their potential and not let traditional expectations define their careers or abilities. One of the most powerful ways to challenge these norms is by pursuing education and skill development. 

For young girls aspiring to build careers in AI, my advice is to stay curious, persistent, and open to challenges. Seek out mentors and role models who inspire you, and don’t hesitate to ask for guidance when needed. AI is a field driven by innovation, and there’s plenty of room for fresh ideas and diverse perspectives. Equip yourself with the right skills and continuously upskill as technology evolves. Above all, never let the fear of failure hold you back—embrace the opportunities and trust that you can make a significant impact.