"Deep Learning in Cancer Detection: Challenges and Solutions"

🚨 Delighted to share our latest publication in Mayo Clinic Proceedings: Digital Health on “Deep Learning Applications in Clinical Cancer Detection: A Review of Implementation Challenges and Solutions” https://lnkd.in/gj2jPZtq In this comprehensive review, my co-authors and I analyzed over 1,400 studies from 2018–2024, spanning imaging, genomics, pathology, and multimodal diagnostics. We highlight: ✅ The tremendous promise of deep learning (DL) in improving early cancer detection ✅ Persistent challenges in clinical adoption—including interpretability, bias, and regulatory hurdles ✅ Cutting-edge solutions, such as federated learning, explainable AI, synthetic data generation, and real-world trials ✅ A call for interdisciplinary collaboration to ensure AI tools are ethically, equitably, and effectively deployed in diverse healthcare settings I have always supported the integration of AI into oncology especially when I served as MD/CEO of the National Cancer Centre Singapore from 2017-2023 and even took a course with the MIT Sloan School of Management on of AI in Healthcare back in 2020. So when I had two junior college students Isaiah Yao and Dong Min approach me for an elective posting I set them a task with some directions on getting this paper done. They surprised me with excellent work done on this and this paper is a culmination of many months of work together on this. Deep learning is not a distant promise—it’s already transforming diagnostics. The question now is: How do we scale it safely and fairly for all? Grateful to my co-authors and to REMEDIS for supporting this work. Also to Yinxia Chao of Duke-NUS Medical School for referring the students to me. #AIinHealthcare #CancerDetection #DeepLearning #MayoClinic #DigitalHealth #OncologyInnovation #ExplainableAI #NCCS #REMEDIS #artificialintelligence Mayo Clinic SingHealth