Farah Shamout, Assistant Professor Emerging Scholar in Computer Engineering at NYU Abu Dhabi, on her meteoric rise, and how the death of three family members inspired her to use technology to improve healthcare

 

Farah Shamout is an Assistant Professor Emerging Scholar in Computer Engineering at NYU Abu Dhabi, where she leads the Clinical Artificial Intelligence Laboratory. Her research focuses on using AI, data science, and machine learning to solve real-world medical problems. Prior to joining NYU Abu Dhabi, Shamout completed her DPhil (PhD) in Engineering Science at the University of Oxford in 2019 at the Computational Health Informatics Laboratory and was selected for the prestigious Rhodes Scholarship in 2016. Her PhD research focused on using artificial intelligence to improve the detection of patient deterioration.

 

What did you want to be when you were a child?

I have always had a passion for journalism and saw myself as a journalist. I really enjoyed writing and public speaking and so I thought it would enable me to shed light on global issues.

Could your career have taken a different direction?

Definitely because I thought a lot about studying medicine.

What initially sparked your interest in tech medicine/healthcare?

During my freshman year of college I lost three family members to cancer within a period of two months. Their struggles with the healthcare system motivated me to improve the delivery of care. Simultaneously, I got exposed to computer programming and I remember feeling in awe of the impact that technology already had on our lives. The combination of those two experiences sparked my interest in using technology to improve healthcare.

Who’s been the biggest influence on your career?

My parents – they taught me how to recognize and seize opportunities and to never underestimate my potential.

 In what areas do you see the next big advances in AI in tech medicine/healthcare?

The next advances will be related to multimodal learning for large-scale datasets. Medical data is vast and heterogenous and we will be able to gain a lot from fusing different types of data, including imaging, clinical, genetic, and wearables data. While this would better reflect how clinicians make decisions in practice, it may also unleash the potential of AI in enhancing the accuracy of medical assessment. However, this requires developing methods specifically for medical data and improving current data sharing practices.

What are you most excited about the future of AI in medicine?

I am excited about the impact of AI in improving patient diagnostics, namely breast cancer. This could directly improve patient outcomes such as by providing early treatment and interventions.

Professionally, what do you consider your greatest achievement?

Publishing my first peer-reviewed journal article as an undergraduate student when I visited Imperial College London. It was the culmination of so much hard work and the tireless effort that I put into the project. I remember spending very late nights in the wet lab during my last two weeks and my experiments were failing. On the last day, I performed an experiment that achieved the best results relative to the literature at the time. This experience triggered my passion for research and made me the researcher who I am today.

What’s been your biggest failure?

It was a group project that my team and I were unable to deliver by the deadline due to lack of communication. This made me realize that effective communication is key to success, especially when working in teams.

What’s your greatest fear?

Another pandemic crisis. We are already experiencing the global impact of the COVID-19 pandemic, despite the previous warnings of science. Healthcare systems must be adequately prepared for future public health challenges and nations should better invest in the healthcare infrastructure.

 Greatest challenge overcome?

Teaching a third-year undergraduate course for the first time, online, and during a pandemic. It paid off and I even won the campus life faculty award for the year, which I am extremely grateful for!

Best piece of advice you ever received?

It came from a previous research supervisor when I informed him that I got accepted to the University of Oxford and won the Rhodes Scholarship. In brief, he encouraged me to not get distracted, to always remember that my achievements are a result of will, hard work and perseverance, to learn and become stronger everyday, and to stay humble.

What advice would you give to someone starting out in a career in Medical AI?

I would highly recommend immersing yourself in medical literature and engaging with medical practitioners. That’s crucial to ensure your work is relevant and useful in clinical practice.

What would you tell your younger self?

Believe in yourself!

What keeps you awake at night?

It’s the same thing as what wakes me up in the morning: how I can make the world a better place.

What’s your guiltiest pleasure?

Listening to the same Coldplay songs on repeat while I am coding!

What’s the most important lesson life has taught you

The most important lesson so far is that it’s okay to say no sometimes. Professional development should also take into consideration mental health and wellbeing.

Which person do you most admire – and why

I admire my students who stormed through their courses, research projects, and internships during quite a stressful pandemic.

What’s left to conquer?

Equitable access to quality healthcare.

Does the gender inequality in AI in medicine/tech frustrate you?

It really does. We need to have more women role models to inspire, mentor, and guide the younger generation.

As a woman, do you feel you’ve had to work harder to gain recognition in this field?

In a male dominated and competitive industry, being a woman definitely has its challenges. Thankfully, through hard work I have been able to gain recognition through my professional achievements.