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AI to Better Diagnose and Treat Renal Cancer and COVID-19 - USC Viterbi

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Kidney cancer is among the 10 most common cancers. In 2019, the American Cancer Society estimated 73,820 new cases of kidney cancer and 14,770 deaths from this disease. The five-year survival rate reduces from 93% in low-risk groups to 69% in high risk groups of patients with localized kidney cancer. However, following the spread of cancer, these rates plummet to 12%. For radiologists, a fundamental driver of diagnosing renal cancer remains visual and qualitative, meaning CT scans (images of a mass) are largely evaluated based on individual knowledge and experience.


The Scope Of Computer Vision In Nuclear Medicine

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The inclusion of technologies such as AI and computer vision in healthcare can greatly enhance high-precision applications like nuclear medicine. Nuclear medicine is a subfield of radiology that involves the use of minute amounts of radiation and radiation-based medicines, known as radiopharmaceuticals, to evaluate the composition and functioning of bones and tissue in patients. Today, nuclear medicine and radiology are prominent components of modern healthcare, especially for cancer diagnosis and treatment. X-rays and CT scans are some of the methods that involve radiation usage in healthcare. The use of powerful radiation beams to inhibit the growth of tumors in cancer patients is also a common healthcare application.


The Role of Artificial Intelligence, Machine Learning and Big Data in Oncology

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Artificial intelligence (AI) plays a vital role in oncology. Big data, artificial intelligence and machine learning excel at recognizing patterns in large volumes of data that cannot be perceived by the human brain. The integration of artificial intelligence, big data and machine learning in cancer care could drastically improve the accuracy and speed of diagnosis, help clinical decision-making, and lead to better health outcomes. Medical imaging solutions can help clinicians deliver more consistent care and tools for researchers looking to conduct efficient clinical trials. AI's applications in healthcare can improve disease diagnosis, management, and the development of effective therapies.


Automated Classification of Benign and Malignant Proliferative Breast Lesions

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Pathologists must identify precursor lesions as either benign usual ductal hyperplasia (UDH) or malignant ductal carcinoma in situ (DCIS) for diagnosis and treatment of breast biopsies. Most patients with UDH receive no treatment and have minimal or no increased risk of cancer, while patients with DCIS are more likely to be diagnosed with invasive breast cancer1, 2. Treatment to reduce DCIS recurrence and invasive carcinoma has notable risks and side effects, given the extensive methods of lumpectomy with radiation, mastectomy, and tamoxifen hormonal treatment3. Diagnostic oversights can lead to either untreated cancer or unnecessary radiation treatment and chemotherapy, both of which have detrimental consequences. Thus, accurate diagnosis is critical for patients as well as for hospitals to reduce extraneous treatment costs. However, human pathologists may not always be in concordance as there is no strict set of instructions on how to carry out a diagnosis.


Good health and well-being: summarising AI and robotics in healthcare – diagnostics, personalised care, drug discovery, and more

AIHub

In December 2020 we announced the launch of our focus series AI for Good: UN sustainable development goals (SDGs). Each month we pick a different sustainable development goal (SDG) and highlight work in that area. Following a terrific response to our first focus on "good health and well-being", we bring you the first of our monthly summary articles where we provide a brief overview of the topic and some highlights from the series. With the COVID-19 pandemic dominating our lives at the moment, research relating to the disease has rightly received considerable coverage in our focus series. In partnership with CLAIRE's COVID-19 taskforce initiative we have brought you articles covering the formation of the taskforce, and about some of the research from the participants.