Cancer diagnosis on your laptop? New artificial intelligence model makes it possible!

Imagine diagnosing cancer not with a supercomputer but on an ordinary laptop instead. Sounds like science fiction? Thanks to a revolutionary artificial intelligence (AI) model developed by Professor Kenji Suzuki and his research team from Institute of Science Tokyo (Science Tokyo), this far-fetched scenario is now a reality.

Unveiled at the prestigious Radiological Society of North America (RSNA) 2024 Annual Meeting, the team introduced an ultra-lightweight deep learning model that assists with lung cancer diagnosis without relying on costly graphics processing unit (GPU) servers or massive datasets. Developed using a unique deep learning approach based on massive-training artificial neural network (MTANN), the model was trained and tested on nothing more than a standard laptop computer, achieving what once required entire data centers.

AI, trained by deep learning models, has gained significant attention in recent years, leading to innovations in multiple fields of research. It has also been reported that if a deep learning model is trained on a large amount of data, such as a million images, it can acquire a performance that can surpass that of conventional technologies and even humans.

Where most models rely on big data, the AI model developed by Suzuki’s team is unique—unlike conventional large-scale AI models, it does not require entire medical image sets. Instead, it learns directly from individual pixels extracted from computed tomography (CT) scan images. This strategy significantly reduced the number of required cases from thousands to just 68!

Despite being trained only on a small set of data, the model outperformed state-of-the-art large-scale AI systems, such as Vision Transformer and 3D ResNet, attaining a discrimination performance corresponding to an area under the curve (AUC) value of 0.92 (against AUC values of 0.53 and 0.59 for the traditional state-of-the-art (SOTA) models, respectively). Once trained, with the full training process only taking 8 minutes and 20 seconds on a standard laptop, it could generate diagnostic predictions at an unprecedented rate of 47 milliseconds per case.

“This technology isn’t just about making AI cheaper or faster,” says Suzuki. “It’s about making powerful diagnostic tools accessible, especially for rare diseases where training data is hard to obtain. Furthermore, it will cut down the power demands for developing and using AI at data centers substantially, and can solve the global power shortage problem we may face due to the rapid growth in AI use.”

In recognition of its significance, the team’s research was conferred the coveted Cum Laude Award at RSNA 2024, an honor received by only 1.45% of the 1,312 presentations. While this innovation is sure to have a transformative impact on cancer diagnosis, it stands as a testament to Suzuki’s deep knowledge and unwavering dedication.

With profound expertise in the field of biomedical AI, Suzuki was the first to invent the MTANN technology (used in the current research) in the early 2000s. It was one of the earliest deep learning models that he had developed and improved on. In his 25 years of research experience, Suzuki has made significant contributions to his field, with more than 400 publications and over 40 patents, most of which have been licensed and commercialized.

Beyond this, his recent achievements include serving as a session chair at the 39^th Annual AAAI Conference on Artificial Intelligence. He has received two of RSNA’s highest distinctions for his research in 2024. Moreover, he is recognized among the top 2% scientists worldwide.

Suzuki continues to lead groundbreaking research at the intersection of AI and medical imaging, actively fostering interdisciplinary collaboration that pushes the boundaries of what AI can achieve in clinical practice. His team’s work on compact, high-performance diagnostic models exemplifies how innovative thinking—combined with practical implementation—can bridge gaps between engineering and medicine. With a dynamic research environment and a strong network of collaborators, Suzuki is not only advancing the field of biomedical AI but also helping shape the next generation of translational medical technologies.

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About Institute of Science Tokyo (Science Tokyo) 
Institute of Science Tokyo (Science Tokyo) was established on October 1, 2024, following the merger between Tokyo Medical and Dental University (TMDU) and Tokyo Institute of Technology (Tokyo Tech), with the mission of “Advancing science and human wellbeing to create value for and with society.”