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Home / Lifestyle / Health & Fitness / Photos / IN PHOTOS: Decoding the role of artificial intelligence in cancer diagnosis

IN PHOTOS: Decoding the role of artificial intelligence in cancer diagnosis

Updated On: 21 December, 2023 05:12 PM IST | Editor

Artificial intelligence excels in handling data-intensive tasks. By automating initial analysis of medical imaging, AI systems can reduce the workload on healthcare professionals and detect more precise cancer diagnosis

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Artificial intelligence excels in handling data-intensive tasks. By automating initial analysis of medical imaging, AI systems can reduce the workload on healthcare professionals and detect more precise cancer diagnosis

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Artificial intelligence excels in handling data-intensive tasks. By automating initial analysis of medical imaging, AI systems can reduce the workload on healthcare professionals and detect more precise cancer diagnosis

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

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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.

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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

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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

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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

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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

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