AI in healthcare to improve disease diagnosis, management, and the development of effective therapies. Given the large number of patients diagnosed with cancer and significant amount of data generated during cancer treatment, there is a specific interest in the application of AI to improve oncologic care. In this review, we introduce the fundamentals of AI and provide an overview of its current applications, pitfalls, and future directions in oncology.
Oncologists have been trying for decades to define small subsets of cancer patients that can benefit from a specific treatment. However, the success of targeted therapies has so far been limited.
- At the moment, medical doctors are overcrowded with data from imaging, genomics, co-morbidities and previous treatments.
- This is where AI comes into play. The technology has the potential to crunch the data to predict the prognosis of the patient and advise doctors with different options available, including personalized medicine and clinical trials with experimental therapies.
AI in Cancer Detection & Diagnosis
AI components in imaging machines would reduce this workload and drive greater efficiency in the radiology field. Machines also have access to a greater wealth of data than their human counterparts do, which can mean that an AI machine can detect cancer with more accuracy than a human.
- Deep learning algorithms developed by our data scientists for Research in Computer Vision, was very successful in accurate detection of lung cancer.
- Application was fed 1,000 CT scans to the AI to teach it how to analyze lung tissue for abnormalities. This study found that AI machines could identify lung cancer from a scan 30 percent more accurately than humans. For some cancers, survival rates are incredibly bleak, so AI could be the catch-all solution many patients hope for.
- Mesothelioma cancer, which has a 5-year survival rate of 12 percent and a 10-year of less than 5 percent, is particularly deadly.
How it works?
Artificial intelligence and machine learning are new technologies that have been recently boosted thanks to hardware improvements.
- Our algorithms, they can learn, predict and advise based on vast amounts of data. In the health services industry, AI has a wide range of uses and applications, from helping with clerical work to turning the tide in the race against cancer.
- Any oncologist knows that early detection of a cancer is necessary for the successful treatment of malignant tumors.
- Tumors inside of a patient’s body are most commonly detected through medical imaging techniques, such as radiology.
- Radiologists today receive more data than they can humanly work through in one shift. On an average radiologist must interpret one image every 3-4 seconds to keep pace with their daily workload
- DL algorithms in our AI program are able to learn the optimal features that best fit the data through the training process, avoiding the need to use pre-engineering, unstructured data. This ability has allowed DL algorithms to outperform traditional ML algorithms in many common AI problems, including image classification, natural language processing, and sequence prediction.