When it comes to cancer care, emerging technologies are becoming increasingly adept at carrying out life-saving work alongside researchers and healthcare professionals. The difference is that it can do it quicker and more efficiently.
For example, using traditional methods to discern suspicious results can make recognising cancer challenging. However, employing algorithms to develop computer-assisted programs with enhanced detection capabilities has become increasingly recognised in the medical industry.
These technologies identify cancer in CT scans better than most radiologists or pathologists and can have a significant impact in spotting some of the hardest cancers to diagnose, early.
According to scientist Ewelina Bębas, researchers have achieved a 97% accuracy rate in diagnosing common types of lung cancer by Machine Learning (ML) analysis.
This is revolutionary, as lung cancer is one of the leading causes of cancer deaths worldwide. Despite all efforts to stop smoking addictions — the primary cause of lung cancer — nearly 35,000 people die from the disease each year. According to Cancer Research UK, that’s around one in five (21%) of all cancer deaths in the UK.
LungLife AI — a US-based medical technology company that specialises in clinical diagnostic solutions for lung cancer —claims that around 80% of lung cancer cases in the UK are diagnosed late but it is looking to change all that.
Working with Persistent to develop AI-driven algorithms has played an instrumental role in assisting LungLife AI’s AI-enabled early lung cancer detection solution, LungAI. The technology has reduced analysis time by 70%, accelerating LungLife AI’s efforts to greatly reduce the impact of lung cancer.
Rajasekar Sukumar, Senior Vice President and Head of Europe at Persistent Systems, said: “Ultimately, with thoughtful integration emerging technologies like LungAI can play an increasing role in every step of cancer care, from diagnosis to monitoring, and even post-treatment support. AI also offers key benefits to healthcare professionals such as optimising processes and reducing wasted time — boosting productivity without wholesale automation of human roles.”