Relevance of Clinical AI in Oncology on World Cancer Day

By Sumona Bose

February 4, 2024

Introduction

Today, on World Cancer Day, we reflect on the progress made in cancer treatment and research, and the role that artificial intelligence (AI) is playing in the field. Cancer continues to be one of the leading causes of death worldwide, with 9.3 million deaths per year, according to the World Health Organization . As a result, academia and the pharmaceutical industry have been investing heavily in innovative technologies to improve cancer care. This article will explore the relevance of clinical AI in Oncology.

AI, a broad field encompassing technologies such as deep learning and machine learning, has emerged as a promising tool in the fight against cancer. One area where AI has shown significant potential is in early cancer detection. Currently, many cancer cases are diagnosed at an advanced stage, making treatment more challenging. However, studies have shown that AI algorithms can analyze medical images and other data to identify signs of cancer at an early stage, increasing the chances of successful treatment.

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Figure 1: Likelihood of expected future events derived from the use of AI in cancer care.

Ethical Dynamics around Clinical Decisions

One of the main concerns is the uncertainty surrounding legal responsibility and accountability for AI-supported clinical decisions. As AI algorithms become more integrated into healthcare systems, it is crucial to establish clear guidelines and regulations to govern their use. This includes standards for data collection, storage, and use, as well as guidelines for transparency and accountability in decision-making processes.

Another important consideration is the ethical implications of AI in cancer care, particularly the issue of bias. If the data used to train AI algorithms are not representative of the treated population, there is a risk of algorithmic bias. For example, if an AI algorithm for breast cancer detection is trained predominantly on data from white women, it may not be as effective at detecting breast cancer in women of other races. To address this, it is essential to ensure that the data used to train AI algorithms are diverse and representative of the population being treated. In addition to ethical concerns, there are also challenges related to the integration of AI into clinical practice. AI algorithms need to be aligned with the specific context of clinical practice, taking into account real-world data that may be incomplete or contain errors. Generalizability and reproducibility of AI algorithms in clinical settings are important considerations that need to be addressed.

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Figure 2: Areas of interest most likely to benefit from AI in the next ten years.

Challenges and Implications of AI driven Oncology

Furthermore, the lack of standardization in cancer-related health data poses a challenge for the adoption of AI in cancer care. Testing, validating, certifying, and auditing AI algorithms and systems can be difficult without standardized data. It is also important to ensure appropriate safeguards to protect patient privacy and prevent data misuse. As we celebrate World Cancer Day and reflect on the progress made in cancer treatment and research, it is crucial to address these ethical and regulatory challenges to fully harness the potential of AI in improving cancer care. By doing so, we can ensure that AI-powered solutions are used responsibly and effectively to benefit patients and healthcare providers alike.

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Figure 3: Barriers to the use of AI in cancer care.

Conclusion

The relevance of clinical AI in oncology cannot be overstated, especially on World Cancer Day. With cancer being one of the leading causes of death globally, the use of AI technologies such as deep learning and machine learning holds great promise in early cancer detection and improving treatment outcomes. However, to fully harness the potential of AI in cancer care, it is crucial to address challenges related to ethical considerations, algorithmic bias, standardization of health data, and the integration of AI into clinical practices. By overcoming these hurdles, we can  make significant strides in the fight against this devastating disease.

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