DS&AI has already been integrated into the medical field, helping to provide better healthcare.
How do you see the role of data science and AI in the medical field?
DS&AI is increasingly being incorporated into medicine. For example, in diagnostic imaging, systems that detects lesion areas, such as tumors, in real-time from medical endoscopic and radiological images are already in clinical use. In terms of disease treatment, these technologies are being used to assist in determining the extent of resection needed during surgery and to ensure no remnants are left behind. AI is also making inroads into drug discovery. We are entering an era where DS&AI technologies are being integrated into clinical practice, enabling the delivery of enhanced medical care.
I think the term DS&AI itself is still new, but when did you become aware of it?
I have loved programming since high school and have wanted to be a researcher since I entered Tohoku University School of Medicine in 2006. Then, when I was a first-year undergraduate student, a biology professor introduced me to a paper on iPS cells (induced pluripotent stem cells). The paper described how introducing just four specific genes into differentiated skin cells could revert them into a pluripotent state. What struck me was how those four genes were identified: through a data science approach, the authors narrowed down from about 20,000 genes to 24, and then experimentally tested combinations of these genes. I was really impressed by this. Around my second year, next-generation sequencing technology was emerging. This technology allowed for comprehensive data acquisition of a patient’s entire genome, rather than examining each gene individually. However, the sheer volume of data generated was overwhelming for human analysis. That’s where data science comes into estimate disease-related genes through data mining. The technology was still in its infancy, but the laboratory I subsequently joined adopted it. I was able to participate in the data analysis, which was my first real engagement with DS&AI.
Could you tell us a little more about your original research field?
Our goal is to create the future of medicine through data science. My research spans from basic studies to elucidate the causes of diseases to establishing methods for disease prevention and early detection for prompt treatment. Specifically, we comprehensively analyze genetic alterations in both healthy individuals and those with the diseases. By analyzing this data, we aim to identify genes that could serve as therapeutic targets or to discover drugs that modulate gene function. We also emphasize validating the phenomena we identify through data science with laboratory experiments using cells and other models. It’s a cyclical process of data acquisition, data science-driven analysis, and experimental validation.
I heard that you are also a doctor. What is your doctor's department?
Generally speaking, it falls under the category of internal medicine. I graduated from medical school about 12 years ago, and medicine is changing ever-increasingly. For example, new drugs for diabetes, which did not exist at all when I took the national board exam, are now in routine use. It’s crucial to stay updated, and that is part of the reason I continue clinical practice. By having an outpatient clinic, I am able to catch up with new guidelines and medical treatment.
Expectations for DS&AI technology to support the unique DS&AI education innovations unique to medical and dental schools.
I understand that you are teaching DS&AI to medical and dental students. Is it challenging to teach a large number of students?
The most important factor is motivation. Medical and dental students enter the university with their sights set on obtaining national certification in their respective medical and dental fields, so there is a difference in atmosphere among students in terms of their enthusiasm for DS&AI. In fact, after the programming exercises, we initially collected feedback from students, and some responses indicated a lack of understanding about the relevance of the material. To address this, the first third of the class talking about the importance of DS&AI in the clinical field. This serves as motivation, showing students what they can achieve with DS&AI. The professors contribute by sharing examples from their areas of expertise, like how DS&AI is used in nursing or dentistry. Then, we emphasize the necessity of learning DS&AI before moving on to the basics of Python. This approach has fostered greater student interest and engagement.
Are there any other difficulties or barriers that you feel are unique to the medical field?
Students in the medical and dental sciences ultimately aim to obtain national medical certifications upon graduation. The national government has established a model core curriculum outlining the specific units and the depth of coverage required for each. As a result, the schedule is tightly packed with mandatory courses from the first morning class on Monday to the evening hours on Friday, resembling extension of high school. This makes it difficult to allocate time for DS&AI classes. So, the first hurdle was negotiating with other faculty members to condense their units or make some time available for DS&AI education. We managed to get some time slots and finally launched the course in 2021. The second hurdle is that, unlike students in science and engineering, medical and dental students often have a weaker background in mathematics. While mathematics is a crucial subject in the entrance exam of the former Tokyo Institute of Technology, that was not always the case at the former Tokyo Medical and Dental University. Some students may have only studied mathematics up to the high school level. Our approach involves a series of small, incremental steps to ensure that students with varying mathematical backgrounds can keep up without dropping out.
More students and researchers with different backgrounds will lead to invasion.
Convergence science is a term that has been coined, but what are your expectations for the Institute of Science Tokyo, which was born from the very fusion of medical and dental sciences and science and engineering?
I’m very excited about the prospect of leveraging the specialized expertise of the science and engineering fields to advance impactful research in the medical and dental fields. The medical field faces numerous challenges, that demand multidisciplinary approaches, and many of these issues cannot be resolved solely by medical professionals alone. I am hopeful that the technology and knowledge of our colleagues in the science and engineering fields will help us overcome these challenges. I also anticipate significant benefits for student education. For example, science and engineering students can study medical and dental sciences, while medical and dental students can gain insights into not only DS&AI but also the broader mindset of science and engineering. Many of the challenges facing society today are complex, so I believe this merger will foster the development of researchers and specialists who are versatile, multidisciplinary, and possess both breadth and depth of knowledge.
It is not simply a matter of the size of the university becoming larger and having a medical school and an engineering school, but more of a fusion of the two.
There are other universities with faculties of medicine, science, and engineering, but I suspect that inter-faculty interactions are often limited, with departments operating in silos. In our case, the medical and dental schools and the science and engineering schools have only recently merged, there is initial momentum and enthusiasm for collaboration. I’ve also heard there will be opportunities to study on each other’s campuses. This will facilitate increased interaction between students and researchers from diverse backgrounds and with various specialties. I hope that this will lead to innovation.
We hope they will be proactive and will not be afraid to touch different areas of the field.
Finally, do you have a message for those aiming for the Institute of Science Tokyo or for students currently at the Institute of Science Tokyo?
I’m thrilled to be a part of the Institute of Science Tokyo and to have the opportunity to collaborate with all of you in research and education. DS&AI is intimately connected to all academic disciplines. Regardless of your major, I believe that data analysis and the insights derived from it will contribute to new discoveries and advancements across various fields. While it’s essential to deepen your expertise in your current area of specialization, I also encourage you to be proactive and unafraid to explore different fields, especially now that the two universities have merged. Similar to participating in club activities, there will be significantly more opportunities for interaction. I hope that you will inspire and learn from each other, fostering mutual growth.
Off-record talk
What I always say to medical and dental students, and what I also encourage science and engineering students to do, is to first thoroughly learn their respective areas of specialization. Then, I urge them to leverage the power of DS&AI, which are tools of modern civilization, to solve specialized problems in their fields. When I began my studies over 20 years ago, learning programming meant painstakingly copying code from books and writing it yourself. Today, however, you can quickly generate practical code that accomplishes your goals by utilizing AI coding assistants. It would be a missed opportunity not to acquire DS&AI skills, as you are the ones who will be leading the way in the future.