Even just a year ago, we couldn’t fathom a cure for cancer. Now, we can.
Artificial intelligence (AI) is rapidly transforming the healthcare industry, with its ability to process and analyze vast amounts of data. Cancer treatment is one area that could benefit significantly from AI, as it can aid in early detection, precise diagnosis, and personalized treatment plans. With the rise of AI technologies, we could see significant advancements in cancer treatment, making it more efficient and potentially leading to a cure.
Below, we’ll explore where we see the greatest opportunity for AI to make a meaningful impact on the fight against cancer along with important challenges and limitations to keep in mind.
The great promise
- Early detection: Detection is naturally key in the treatment of cancer and AI can assist in identifying the disease at its earliest stages. By catching the disease early, patients have a higher chance of receiving successful treatment, and the cost of treatment can be significantly reduced. AI algorithms can analyze medical images and identify any abnormalities that may indicate cancer. For example, Google’s DeepMind Health, a subsidiary of Alphabet Inc., has developed an AI algorithm that can accurately detect breast cancer on mammograms. In a study conducted by Korean academic hospitals and Lunit, a medical AI company specializing in developing AI solutions for radiology and oncology, the algorithm outperformed human radiologists in identifying breast cancer. The AI system could identify cancer with an accuracy of 94.5%, compared to 88.4% for human radiologists.
- Precision diagnosis: Cancer diagnosis is a complex process but AI can aid in identifying the specific type of cancer and its stage. By analyzing vast amounts of data from medical records, laboratory tests and imaging scans, AI algorithms can provide a more precise diagnosis, which can lead to better treatment outcomes. One area where AI is already making significant strides is in the diagnosis of lung cancer. Researchers at MIT have developed an AI algorithm that can detect lung cancer with an upto 94% accuracy rate. The algorithm was trained on over 42,000 CT scans and can identify the specific type of lung cancer.
- Personalized treatment plans: Treatment plans can vary widely depending on the type of cancer, the stage and the patient’s overall health. AI can help by identifying the most effective treatment options and personalizing plans based on a patient’s unique characteristics and disease profile — potentially improving patient outcomes and reducing the risk of side effects. One example of how AI is already being used to personalize cancer treatment is in the field of precision medicine. Precision medicine involves tailoring treatments to a patient’s unique genetic makeup, disease profile and lifestyle. AI algorithms can analyze large amounts of genomic data and identify genetic mutations that are unique to an individual’s cancer. This analysis can then be used to develop personalized treatment plans tailored to the patient’s specific needs. For example, researchers at the University of Pittsburgh Medical Center (UPMC) have developed an AI algorithm that can predict how individual patients will respond to immunotherapy — a cancer treatment that boosts the immune system to fight cancer cells. However, not all patients respond well to immunotherapy and it can have significant side effects. The UPMC algorithm uses machine learning to analyze patient data and identify the specific markers that indicate how the patient will respond.
- Efficient drug development: AI can also assist in the drug development and identifying potential new treatments — significantly reducing the time and cost of developing new, more effective, cancer drugs. By analyzing data from clinical trials, genetic research and other sources, AI algorithms can identify drug candidates that have the potential to be more effective in treating cancer. For example, researchers at MIT have developed an AI system that can analyze millions of chemical compounds and identify those that have the potential to be effective cancer drugs. The system uses deep learning to analyze molecular structures and predict the effectiveness of a drug candidate.
- Genomics on steroids: One of the most significant contributions that AI could make to cancer research is its ability to analyze and process vast amounts of complex genomic data. Genomics has become an essential tool in cancer research and the field is rapidly expanding as more data becomes available. However, the sheer volume and complexity of genomic data make it challenging for researchers to analyze and interpret effectively.AI algorithms can analyze this data and identify patterns that are too complex for humans to discern. This analysis could potentially lead to the identification of new biomarkers for cancer diagnosis, personalized treatments, and improved patient outcomes. AI can also help predict the efficacy of different treatment options and identify potential side effects, enabling clinicians to make more informed decisions about patient care.
- Medical imaging: Another area where AI could significantly impact cancer research is in the field of medical imaging. Medical imaging is a critical tool in cancer diagnosis, staging, and monitoring. However, interpreting imaging data is complex and requires significant expertise. AI algorithms can analyze medical images and identify patterns and anomalies that human clinicians might miss — improving the accuracy and efficiency by reducing the need for manual interpretation.
- Clinical trials: Clinical trials are essential for evaluating the safety and efficacy of new cancer treatments. However, conducting clinical trials can be time-consuming, expensive, and resource-intensive. AI can reduce the time and resources required by identifying patient populations that are most likely to respond positively to new treatments. It can also help identify potential side effects, enabling researchers to develop more targeted and effective treatments.
- Research collaboration: One of the most significant benefits of AI in cancer research is its ability to accelerate the pace of discovery. AI algorithms can analyze data quickly and accurately, enabling researchers to identify new insights and patterns that might be missed using traditional research methods. AI can also help researchers collaborate more effectively, enabling them to share data and insights more easily. This collaboration could lead to faster and more effective cancer research, potentially accelerating the pace of discovery and improving patient outcomes.
Challenges & limitations
- AI has the potential to revolutionize cancer diagnosis, treatment and management. However, while AI offers many exciting possibilities, it also faces significant challenges that must be addressed.
- Datasets — both large & diverse: AI algorithms require data to train and develop models accurately. However, cancer datasets can be challenging to obtain, as they often contain sensitive patient information and require rigorous ethical and privacy considerations. Further, cancer datasets can be biased, as they may not accurately represent the diversity of the population. This bias can impact the accuracy of AI algorithms and lead to less effective treatments for underrepresented patient groups.
- Complexity of cancer: Cancer is a complex, heterogenous disease with widely varying treatment plans. Developing AI algorithms that can accurately predict the effectiveness of different treatment options is a significant challenge, as the disease’s complexity can make it difficult to identify consistent patterns or biomarkers.
- Complexity of algorithms: AI algorithms can be challenging to interpret and understand, making it difficult for clinicians to incorporate them into decision-making. It is critical to develop AI models that are transparent and interpretable, allowing clinicians to understand the algorithms’ outputs and make informed decisions about patient care.
- Over-reliance on technology: AI algorithms are not a substitute for human expertise, and it is crucial to remember that technology is only one tool in the healthcare toolkit. Clinicians must continue to rely on their expertise and judgment when interpreting AI-generated insights, and patients must understand that technology is not infallible.
- Regulatory oversight: As AI becomes more prevalent in cancer research and treatment, there is a growing need for regulatory oversight to ensure that the technology is safe, effective, and ethical. Regulators must establish standards for data privacy, transparency, and accountability to ensure that AI is used responsibly and ethically in cancer research.
- Cost and accessibility: Developing AI algorithms can be expensive, and the costs of implementing AI technology in healthcare can be prohibitive. Furthermore, AI algorithms may not be accessible to all patients, particularly those in low-income or underserved communities, exacerbating existing healthcare disparities.
Parting thoughts
While AI offers many exciting possibilities in cancer research, it also faces significant challenges that must be addressed. Researchers, clinicians, and policymakers must work together to ensure that AI is used responsibly, ethically, and transparently in cancer research and treatment. By addressing these challenges, AI has the potential to accelerate the pace of discovery and improve patient outcomes, bringing us closer to a world where cancer is no longer a significant public health challenge.
Photo: Main_sail, Getty Images
