AI Cancer Detection & Treatment in 2024: The Ultimate Oncology Solution?

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AI cancer treatment is a concept complicated by the fact that "cancer" is actually a complex set of diseases, not just one illness. However, AI can help with diagnozing cancer, personalizing treatments, and helping monitor cancer survivors. Yet, there are limitations and ethical issues to consider, such as data quality, privacy, accountability, access, consent, and the need for ethical oversight.

Artificial intelligence (AI) cancer treatment is a topic that is gaining traction, particularly due to the complexity of the disease itself.

AI is revolutionizing cancer detection and treatment, with academic studies and innovative projects backed by the European Union helping to inform the future.

However, the use of AI for cancer treatment isn’t without challenges, including data accuracy, privacy, and the need for ethical oversight, amongst others.

So, if you’re wondering whether AI cures cancer or simply enhances current treatment methodologies, read on to explore the role of AI in oncology.

AI Cancer Detection: Unraveling the Complexity of Different Cancer Types

Cancer is often spoken of as if it were one illness. However, the reality is far more complex. Cancer is actually a collection of many related conditions, which happen when cells start to grow too fast. Often, this leads to tumors, which can spread.

The term “cancer” covers over 100 conditions,  each with its own risks, tests for diagnosis, and treatment options. Knowing about this range is vital for treatment and care.

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Take breast cancer, for instance. This is the most common type of cancer (National Cancer Institute, 2023), but it’s not just one type — it has many different kinds, determined by the specific cells in the breast that become cancer. And each type has its own treatment needs (American Cancer Society, 2023).

– Basic Types

  • Most breast cancers are called “carcinomas.” These start in the lining of the breast’s milk ducts or milk-making glands.
  • Some breast cancers stay in the milk ducts and don’t spread. These are called “in situ” cancers.
  • Others spread into the surrounding breast area and are known as “invasive” cancers.

– Common Invasive Types

  • The most common type that spreads is “invasive ductal carcinoma.” It makes up 70-80% of all breast cancers.
  • Another type that spreads is called “invasive lobular carcinoma.”

– Special Types

  • “Triple-negative” is a harder-to-treat type that doesn’t have certain hormone receptors or proteins. It makes up about 15% of breast cancers.
  • “Inflammatory” breast cancer is rare but aggressive. It makes the breast look red and swollen.

– Rare Types

  • “Paget disease” is rare and affects the nipple and the dark circle around it.
  • “Angiosarcoma” starts in blood or lymph vessels and is very rare.
  • “Phyllodes tumors” are also rare and grow in the breast’s connective tissue.

The Scope of Cancer Types: More Complex Than You’d Think

Ultimately, even breast cancer, the most common type, isn’t just one disease.

This level of complexity when it comes to just one ‘type’ of cancer underlines just how much cancer isn’t a one-size-fits-all situation.

If breast cancer alone can be broken down into such diverse subtypes, it raises the question: How diverse are other cancers like lung, skin, or prostate cancer? The answer is “very”.

How Can AI Be Used in Cancer Research?

AI is being used in cancer research by addressing the complex challenges posed by various forms of cancer.

This technology analyzes a multitude of variables, continually learning and adapting to the nuances of each cancer type and its subtypes.

As a result, AI develops cancer treatment plans that are as unique as the diseases themselves. This section explores cutting-edge AI technologies and initiatives that are setting new standards in cancer care.

– How AI is Improving Cancer Diagnostics

AI algorithms are changing how we approach medical imaging. They can analyze images like mammograms and MRI scans faster and sometimes more accurately than human experts.  Convolutional Neural Networks (CNNs) are a specific kind of deep learning model making this possible.

For instance, EfficientNet architectures developed in 2019 have been applied to AI for breast cancer, showing remarkable capabilities in diagnosing the various types of this cancer (Suh Y.J et al., 2020). These architectures have also been used to detect lung cancer (Astaraki M. et al., 2021) and brain cancer (Guan Y. et al., 2021).

However, AI cancer detection is not just limited to imaging. It’s also making strides in early cancer detection through blood tests and other non-invasive methods.

For example, machine learning has helped identify markers in blood samples that could indicate cancer (Liu B. et al., 2019; Kawakami E. et al., 2019).

5 Innovative AI Cancer Detectors/Models in 2024

  • iStar (Inferring Super-Resolution Tissue Architecture): Created by Penn Medicine, iStar is an AI tool that revolutionizes medical image analysis. It offers detailed insights into gene activities and cells, aiding in precise disease diagnosis since it can quickly identify cancer cells. It also plays a crucial role in checking the area around where a tumor was removed to ensure all cancer cells are gone. Additionally, iStar identifies specific immune responses in the body that fight cancer, which is helpful in deciding if a patient should receive immunotherapy.
  • Virchow: Virchow, developed by Paige, is capable of identifying cancer in over 17 different types of tissue, such as skin, lung, and gastrointestinal tissues. It can also spot rare tumors and identify the spread of cancer to other parts of the body. It’s built with the largest collection of digital pathology images and cutting-edge computing resources from Microsoft Research. Unlike older AI models, Virchow can quickly and efficiently detect multiple types of cancer at once, making it a versatile tool for diagnosis and treatment planning. Virchow is currently working towards FDA approval, aiming to meet high safety and regulatory standards.
  • Prism: Prism, created by researchers at MIT and the Beth Israel Deaconess Medical Center, is an innovative tool aimed at enhancing the early detection of pancreatic cancer. By analyzing extensive patient data, including 6 million records, PRISM predicts the likelihood of developing pancreatic cancer within 6 to 18 months. The system employs two AI models to process patient information like age, medical history, and lab results. This system significantly outperforms current screening methods, identifying 35% of high-risk cases well before diagnosis. Prism’s broad dataset makes it more accurate and versatile, offering a potential breakthrough in detecting and treating pancreatic cancer effectively.
  • DermaSensor: DermaSensor is a breakthrough device that has just been approved by the FDA in January 2024. It is designed to help primary care doctors detect skin cancer. It uses advanced AI to analyze skin lesions for signs of the three most common types of skin cancer. The device works by shining light on the skin and analyzing how it scatters, which reveals changes in cells that could indicate cancer. DermaSensor gives quick results, categorizing lesions as either needing further investigation or just monitoring. This tool makes it easier for doctors to spot skin cancers early, which is crucial for effective treatment.
  • Sybil AI: Sybil AI, created by MIT and Massachusetts General Hospital, is a breakthrough in predicting lung cancer risk. Using low-dose computed tomography (LDCT) scans, it assesses the likelihood of developing lung cancer within six years, providing tailored risk evaluations. Sybil AI stands out for its ability to detect early lung cancer signs that are often missed by traditional methods. Its accurate predictions are essential for catching lung cancer early when it’s more treatable, potentially improving survival rates. Effective for both smokers and nonsmokers, Sybil AI is a key development in lung cancer screening, offering a proactive approach to lung health.

– Treatment Personalization

AI cancer treatment is advancing in one of healthcare’s toughest challenges: personalized medicine. With computational power, AI is transforming the approach to cancer care by being particularly useful in creating customized care plans.

In terms of treatment prediction, methods like using the Support Vector Machine (SVM) and Random Forest (RF) algorithms are effective. These algorithms can process large sets of data quickly, developing cancer treatment plans that are more accurate and personalized (Rezayi S. et al., 2022).

Additionally, personalizing medicine often relies on detailed genetic tests. For example, tests can check breast tumor cells for excessive HER2 protein. Information like this helps in choosing a focused treatment plan. If a unique genetic issue is found, specialized treatments can target it directly (Hauser A. S. et al., 2017).

– Monitoring and Follow-Up

AI cancer treatment is growing in importance, especially in monitoring and follow-up care. EU’s Horizon 2020 program funds several projects that highlight how how AI helpsdevelops cancer treatment that is both tailored and adaptable.

QUALITOP: AI in Immunotherapy

The QUALITOP project aims to create an open digital platform using AI. This platform targets immunotherapy, identifying key health factors, profile patients, and making real-time suggestions. By incorporating AI, QUALITOP offers precise treatment options that meet individual patient needs.

ASCAPE: Focus on Breast and Prostate Cancer

ASCAPE is working to build an open AI framework for healthcare settings. It focuses on breast and prostate cancer, delivering services like smart interventions for physical and emotional support. It also improves patient and family counseling. Additionally, ASCAPE aims for early diagnoses and tracks disease patterns.

CLARIFY: AI Cancer Treatment and Long-term Health

CLARIFY targets long-term health issues in cancer survivors. The project gathers data on survivors of different cancer types. For example, it collects data on breast, lung, and lymphoma cancers. With AI, it identifies factors linked to poor health outcomes, aiming to guide healthcare providers in planning better post-treatment care.

Limitations and Ethical Considerations in AI Cancer Treatment

While AI cancer treatment shows promise in diagnosis and care, limitations and ethical issues exist.

– Data Accuracy and Diversity

A key challenge is data quality. AI algorithms need accurate, comprehensive data that represents all populations. If the data skews toward one demographic, AI cancer treatment could become biased, with treatment plans not working for other groups.

– Privacy and Security Concerns

Health data is sensitive. Its collection and storage for AI cancer treatment must follow strict rules to keep patient information safe. Ethical standards must guide how data is managed, especially when shared across healthcare systems or countries.

– Who is Accountable?

If an AI system errs, like misdiagnosing or suggesting wrong treatment, accountability becomes an issue. Is it the healthcare provider or the AI creators? Doctors use AI for help, but algorithms aren’t always right. A balance between human skills and AI input is vital, with clear procedures for handling errors.

– Access to AI Cancer Treatment

Creating and maintaining advanced AI technologies isn’t cheap. Only well-funded healthcare systems might afford it. This raises ethical questions about fair access to such life-saving AI cancer treatment tech. Efforts must ensure that AI developments in cancer treatment reach everyone, regardless of their financial standing.

– Understanding and Consent

In healthcare, AI usually requires using patients’ data, making informed consent complex. Patients must know how their data will be used and what risks and benefits an AI approach to treatment could bring.

– Need for Ethical Oversight

Finally, AI’s fast growth means ethics must keep pace. Medical ethical committees may need AI experts to grasp the full impact of using AI in healthcare.

The Bottom Line

Cancer is a complex set of diseases, not just one illness. Each kind, such as breast cancer, has different subtypes needing specific treatments, highlighting the importance of AI in oncology, especially in AI breast cancer detection.

AI is changing how healthcare approaches cancer. In early detection, AI tools analyze medical images and blood tests quickly, as well as make personalized treatment plans. However, there are some issues. These include data quality, privacy, and who is responsible if mistakes happen. Ethical points like informed consent also need careful attention.

Understanding the different types of cancer helps to see the big healthcare challenge. AI cancer treatment offers more precise and effective plans. However, to get the best out of AI, it needs to address challenges and ethics.

Ultimately, even though a one-size-fits-all AI cure for cancer isn’t currently possible because of the disease’s complexity, advancements in AI technology are promising improved results for cancer patients. As AI develops, it’s bringing more accurate and tailored treatments, pointing towards a future with better outcomes for different kinds of cancer.

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Maria Webb
Technology Journalist
Maria Webb
Technology Journalist

Maria is a technology journalist with over five years of experience with a deep interest in AI and machine learning. She excels in data-driven journalism, making complex topics both accessible and engaging for her audience. Her work is prominently featured on Techopedia, Business2Community, and Eurostat, where she provides creative technical writing. She holds a Bachelor of Arts Honours in English and a Master of Science in Strategic Management and Digital Marketing from the University of Malta. Maria's background includes journalism for Newsbook.com.mt, covering a range of topics from local events to international tech trends.