Medical experts unravel the role of artificial intelligence in early cancer diagnosis

Artificial intelligence (AI) continues to expand into diverse facets of our lives - including cancer detection. Dr Aklesh Tandekar, Head Consultant Critical Care, Wockhardt Hospitals, Mira Road tells Midday: "The convergence of machine learning algorithms and the vast amounts of medical imaging data has paved the way for more precise detection methods, ultimately improving patient outcomes."

According to World Health Organisation, cancer is a leading cause of death worldwide killing nearly 10 million people in 2020. Through the application of machine learning, researchers have begun to develop tools aimed at diagnosing tumours and lesions that used to go unnoticed by doctors. The practice is also aimed at enhancing the capability to detect cancer at an early stage to curb the fatal disease.

Experts opine that machine learning: the method through which computers acquire the ability to decode complex data patterns for predictive purposes, stands ready to revolutionise cancer detection. To unravel the role of artificial intelligence in early cancer diagnosis, Midday.com spoke to healthcare professionals who provide an overview of how AI is seeping into cancer diagnosis and developing cures.

How AI enhances the diagnostic accuracy

One of the key ways AI contributes to diagnostic accuracy is through its ability to analyse complex patterns in medical images. Traditional methods of interpreting images, such as X-rays, MRIs or CT scans, often rely on the expertise of radiologists who may at times overlook subtle indicators of cancer - remarks Tandekar.

AI, however, can process massive datasets at incredible speeds, identifying patterns that may be imperceptible to the human eye. This capability allows for the detection of early-stage cancers, where the subtlest abnormalities may be indicative of the disease.

Machine learning algorithms - a subset of AI, thus, play a pivotal role in this enhancement of diagnostic accuracy. These algorithms learn from vast datasets, continuously improving their ability to recognise patterns and anomalies.

In the context of medical imaging, they can be trained on a diverse range of images representing both normal and cancerous conditions. As a result, the algorithm becomes adept at distinguishing between various tissue types, lesions and abnormalities, contributing to more accurate diagnoses.

Furthermore, AI offers the advantage of consistency and reliability. Human radiologists may experience fatigue or be influenced by external factors, potentially impacting their diagnostic accuracy. AI systems, on the other hand, maintain a consistent level of performance regardless of external conditions. This reliability can be particularly crucial in time-sensitive situations where quick and accurate diagnoses are imperative.

Which technology or algorithms are commonly employed in this context?

There are several technologies and algorithms are commonly employed in AI for early cancer diagnosis. The field of AI in healthcare, including cancer diagnosis, is rapidly evolving and various approaches are being explored. Some common technologies and algorithms shared by Dr Pranav Mahadevkar, consultant radiologist and HOD, Ruby Hall Clinic include:

Machine learning (ML):
• Supervised Learning: This is one of the most common approaches, where algorithms are trained on labeled datasets containing examples of cancer and non-cancer cases. Support Vector Machines (SVM), Random Forests and Neural Networks are commonly used in supervised learning for cancer diagnosis.
• Deep Learning: Deep neural networks, particularly convolutional neural networks (CNNs), have shown great promise in image-based cancer diagnosis, such as in the analysis of medical imaging data like X-rays, CT scans and MRIs.

Computer vision:
• Computer vision techniques are widely used for image analysis in medical imaging. AI algorithms can detect patterns and abnormalities in medical images, aiding in the early detection of cancer. For example, identifying tumours in mammograms or lesions in skin images.

Natural language processing (NLP):
• NLP techniques are employed for analysing unstructured data, such as clinical notes, pathology reports, and medical literature. Extracting relevant information from these sources can assist in diagnosing and predicting cancer.

Genomic data analysis:
• AI algorithms can analyse genomic data to identify patterns associated with different types of cancers. This includes identifying genetic mutations, gene expression patterns and other molecular markers.

Predictive modelling:
• AI models can be used to predict the likelihood of cancer-based on various risk factors. This includes incorporating patient history, lifestyle factors and genetic predispositions into predictive models.

Data fusion:
• Integrating information from multiple sources, such as medical images, genomic data, and clinical records, can provide a more comprehensive picture for accurate cancer diagnosis.

Blockchain technology:
• Blockchain can be employed to enhance the security and privacy of patient data, ensuring that sensitive information is protected while allowing for collaboration and data sharing among different healthcare entities.

Augmented reality (AR):
• AR can be used to enhance visualisation during surgery or medical procedures, providing real-time information to surgeons and improving precision.

Benefits of AI in cancer diagnosis

Cancer imaging tests are utilised by physicians to address various inquiries, such as distinguishing between cancer and benign masses, gauging the rate of cancer growth, assessing the extent of its spread and determining if it is recurring post-treatment. Research indicates that AI holds the potential to enhance the speed, precision and dependability of responses to these questions by healthcare professionals.

Additionally, AI has the capability to streamline evaluations and tasks currently performed by humans but are time-intensive. Once the AI generates a result, "a radiologist only needs to verify the accuracy of the AI's assessment," adds mahadevkar. While this automation is anticipated to save time and reduce costs, it still requires empirical validation, he added.

Moreover, AI is playing a crucial role in supporting patient care by aiding the treatment processes for the nuanced cures of varied types of cancer, remarks Tandekar. This dynamic involvement of AI in healthcare showcases its potential to contribute significantly to both the diagnostic and therapeutic dimensions of cancer management.

Challenges in cancer diagnosis

Cancer diagnosis utilising artificial intelligence (AI) faces several challenges, with data-related issues being a prominent concern. The accuracy of AI models heavily depends on the quality and diversity of the datasets used for training. Inadequate representation of certain demographics, rare cancer types, or specific medical conditions in the training data can lead to biased algorithms that may struggle to generalise effectively.

Furthermore, ensuring the privacy and security of sensitive health data poses an additional challenge. As AI systems require access to extensive patient records for robust training, there is a need to adhere to strict regulations to protect patient confidentiality.

Additionally, interpreting and explaining AI-driven diagnoses is another significant hurdle. Many AI models, particularly deep learning algorithms, operate as intricate "black box" systems, making it challenging to understand the underlying decision-making process.

The lack of transparency raises concerns among healthcare professionals who require a clear understanding of how AI arrives at a particular diagnosis to trust and integrate these technologies into clinical workflows. Striking a balance between the complexity of advanced AI models and the interpretability necessary for clinical acceptance is crucial for fostering trust and widespread adoption in cancer diagnosis and other medical applications.

Overcoming challenges to get ahead with AI in healthcare

One of the significant challenges in cancer diagnosis is the sheer volume of medical imaging data that needs to be analysed. AI excels in handling such data-intensive tasks. By automating the initial analysis of images, AI systems can significantly reduce the workload on healthcare professionals. This allows radiologists to focus on more complex cases and interpretive tasks, leveraging AI as a valuable tool to streamline their workflow.

AI's impact on diagnostic accuracy is not limited to mere pattern recognition. Advanced algorithms can provide quantitative assessments of various features within medical images. For example, in breast cancer screening, AI can analyse mammograms to provide precise measurements of lesion size, shape and density. This quantitative information can aid radiologists in making more informed decisions about the nature and severity of the detected abnormalities.

Moreover, AI can facilitate a multidisciplinary approach to cancer diagnosis and treatment planning. By integrating data from various sources, such as imaging studies, pathology reports, and clinical histories, AI systems can provide a comprehensive overview of a patient's condition. This holistic approach enhances diagnostic accuracy by considering a broader spectrum of information, enabling healthcare professionals to make more informed and personalised decisions regarding patient care.

The implementation of AI in medical imaging for cancer diagnosis also holds promise for improving screening programs. By automating the initial screening process, AI can help identify individuals at higher risk, ensuring that they receive more thorough examinations. This targeted approach can lead to earlier detection of cancer, potentially resulting in more effective treatments and improved prognosis.

In conclusion, the integration of AI in medical imaging represents a paradigm shift in cancer diagnosis. By harnessing the power of machine learning algorithms, AI enhances diagnostic accuracy through the analysis of intricate patterns, providing consistency and reliability, handling large volumes of data, offering quantitative assessments and facilitating a comprehensive, multidisciplinary approach. As these technologies continue to evolve, their role in improving cancer diagnosis and patient outcomes is likely to become even more prominent in the future.