Benefits of applying AI to cancer detection and treatment
AI has been used in healthcare since the 1970s, evolving over time with new advancements. The latest type, generative AI (Gen AI), is gaining attention for its ability to handle large data and create original content like reports, clinical notes, and realistic fake data for learning or testing.
AI offers many benefits in healthcare. Here are five keyways it helps with cancer detection and treatment:
- Personalizing therapies.: AI helps personalize cancer treatment by analyzing patient and cancer cell data to create targeted therapies. This approach attacks cancer cells while protecting healthy ones. For example, the National Institute of Health created an AI tool that predicts how tumor cells will respond to specific drugs.
- Reducing false positives and negatives: AI improves cancer detection by reducing false positives and negatives. For example, doctors often misidentify 1 in 10 mammograms as positive. Google's AI software has cut false positives by 6% and false negatives by 9%.
- Classifying tumors without invasive procedures.: AI can classify tumors without surgery. For example, doctors sometimes perform surgery only to find a tumor is harmless. Researchers from Harvard and the University of Pennsylvania developed an AI tool that uses MRI scans to detect specific tumor mutations, like IDH in gliomas, avoiding invasive procedures.
- Detecting diseases in early stages.: AI can detect diseases early by spotting changes in medical images that humans might miss. Early detection can save lives. For example, pancreatic cancer is usually found late, with a 20% survival rate, but catching it early can raise that to 50%.
- Accelerating diagnosis.: AI speeds up diagnosis by analyzing tissue in seconds, compared to days with traditional methods.
Challenges in using AI for cancer detection and treatment, or why some pathologists hesitate to adopt it.
- Biased training data: AI models can be biased, favoring certain ethnic groups or hospitals, which affects accuracy. For example, a study showed an AI tool considered the hospital, not just the scan, when detecting cancer. Also, many skin cancer models are trained mostly on white skin images, making them less accurate for people of color.
- Fear of being replaced: Some pathologists fear AI might replace them, especially when they hear that AI can learn from millions of images. However, AI is good at specific tasks, but it can’t replace all the work pathologists do. AI won’t replace doctors, but doctors who use AI might have an edge over those who don’t.
- Difficulties associated with gathering and managing data: Healthcare data is stored in different ways, making it hard to standardize. Tools like patient-reported outcome measures (PROMs) help collect data early, but asking doctors to gather more can lead to burnout. PotentiaMetrics and ITRex created an AI-powered portal to track patient data from diagnosis to recover, helping doctors use the information to make personalized treatment plans.
- Insufficient training data: In healthcare, more training data improves accuracy, but medical image datasets are smaller than those for natural images. For example, the LUNA CT dataset has only 888 images. Using generative AI can create more data, but it needs a lot of computing power, and doctors must still review the results.
- Ethical concerns and considerations: AI in cancer treatment is often a "black box," so doctors can't explain how it makes decisions. Using "explainable AI" can help but may reduce accuracy. There are also concerns about patient data privacy, like when the University of Chicago shared records with Google, leading to a lawsuit.
The Future for AI in Cancer
The future of AI in cancer involves better diagnosis, personalized treatments, and quicker, more accurate results. AI will help doctors make smarter decisions, improve patient care, and find new ways to treat cancer more effectively.
Conclusion:
AI has the potential to revolutionize cancer care by improving early detection, personalizing treatments, and enhancing overall patient outcomes. As technology advances, AI will play a key role in making cancer treatment more precise, efficient, and accessible, bringing us closer to better management and potentially more cures for the disease.
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