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Posted: April 30th, 2022
Establishing a New Method of Diagnosing Cancer by Using Artificial Intelligence Technology
Cancer is the second leading cause of death worldwide, claiming around 10 million lives in 2020 according to the World Health Organization (WHO). Early detection of cancer is crucial for effective treatment and prevention of metastasis, the spread of cancer cells to other parts of the body. However, current methods of cancer diagnosis are often invasive, expensive, time-consuming, and prone to human errors. Therefore, there is a need for a new method of diagnosing cancer that is fast, accurate, non-invasive, and affordable.
Artificial intelligence (AI) is a branch of computer science that aims to create machines or systems that can perform tasks that normally require human intelligence, such as reasoning, learning, and decision making. AI has been revolutionizing various fields of biomedical research and health care, including cancer research. AI can help not only in cancer detection, but also in cancer therapy design, identification of new therapeutic targets, drug discovery, and cancer surveillance.
One of the most promising applications of AI in cancer diagnosis is the use of machine learning (ML), a type of AI that can learn from data without being explicitly programmed. ML algorithms can analyze large volumes of complex and heterogeneous data, such as genomic, proteomic, imaging, and clinical data, and extract patterns and relationships that are not easily discernible by humans. ML can also make predictions or decisions based on the learned data, such as classifying tumors into different types or stages, or predicting the prognosis or response to treatment.
A subset of ML is deep learning (DL), which uses artificial neural networks that are inspired by how the human brain processes information. DL can learn from huge amounts of data and perform tasks that are beyond the capabilities of conventional ML algorithms. For example, DL can process high-resolution images and detect subtle features that are indicative of cancerous lesions. DL can also integrate multiple types of data and generate novel insights that can improve the understanding of cancer biology and mechanisms.
Several studies have demonstrated the potential of AI in improving cancer screening and diagnosis across different types of cancer. For instance, scientists in the National Cancer Institute (NCI) developed a DL approach for the automated detection of precancerous cervical lesions from digital images. The algorithm achieved an accuracy of 91%, which was comparable to that of human experts. Another group of NCI researchers and their collaborators trained a computer algorithm to analyze MRI images of the prostate and detect prostate cancer with an accuracy of 93%, which was higher than that of radiologists.
AI-based methods for cancer diagnosis have several advantages over conventional methods. They can reduce the need for invasive biopsies or surgeries, which can cause complications or infections. They can also reduce the workload and costs for health care providers and patients, as well as the time required for diagnosis. Moreover, they can enhance the consistency and reliability of diagnosis, as they are less affected by human factors such as fatigue or bias.
However, there are also some challenges and limitations that need to be addressed before AI can be widely adopted in clinical practice. One of the main challenges is the availability and quality of data. AI algorithms require large amounts of annotated data to train and validate their performance. However, such data are often scarce, incomplete, or inconsistent across different sources or institutions. Therefore, there is a need for more collaboration and standardization among researchers and clinicians to share and harmonize data.
Another challenge is the interpretability and explainability of AI algorithms. AI algorithms often operate as black boxes, meaning that their internal logic or reasoning is not transparent or understandable to humans. This can raise ethical and legal issues, such as who is responsible for the outcomes or errors of AI algorithms, or how to ensure informed consent from patients. Therefore, there is a need for more research and development on methods that can make AI algorithms more interpretable and explainable to humans.
A third challenge is the integration and evaluation of AI algorithms in real-world settings. AI algorithms need to be tested and validated on diverse and representative populations and environments before they can be deployed in clinical practice. They also need to be integrated with existing workflows and systems in health care facilities, such as electronic health records or decision support tools. Moreover, they need to be evaluated on their impact on clinical outcomes and patient satisfaction.
In conclusion, AI technology offers a new method of diagnosing cancer that has the potential to improve the accuracy and speed of diagnosis, aid clinical decision making, and lead to better health outcomes. AI-guided clinical care could also play an important role in reducing health disparities, especially in low-resource settings where access to conventional methods is limited. However, there are still some challenges and limitations that need to be overcome before AI can be fully implemented in clinical practice. Therefore, more research and collaboration are needed among scientists, clinicians, patients, regulators, and policy makers to ensure the safe and effective use of AI in cancer diagnosis and therapy.
Works Cited
“Artificial Intelligence – NCI.” National Cancer Institute, https://www.cancer.gov/research/areas/diagnosis/artificial-intelligence. Accessed 15 Oct. 2023.
“Artificial Intelligence in Cancer Diagnosis and Therapy.” MDPI, https://www.mdpi.com/topics/AI_Cancer. Accessed 15 Oct. 2023.
“Exploring AI for Cancer Diagnosis.” National Institute of Dental and Craniofacial Research, https://www.nidcr.nih.gov/news-events/2020/exploring-ai-cancer-diagnosis. Accessed 15 Oct. 2023.
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