Artificial Intelligence (AI) has been creating ripples in healthcare, and radiology is undoubtedly no exception.
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Artificial intelligence & deep learning for the radiologist

AI empowers radiologists by swiftly analysing medical images, enhancing patient care while preserving the human touch in healthcare.

Artificial Intelligence (AI) has been creating ripples in healthcare, and radiology is undoubtedly no exception. Imagine a world where machines can analyse medical images with the precision of seasoned radiologists, leading to faster and more accurate diagnoses. This reality is getting closer daily, thanks to AI and Deep Learning. This research explores the latest research findings on how AI is revolutionising radiology.

AI’s superpower in image analysis

Radiology heavily relies on medical imaging to detect and diagnose diseases. Whether it’s X-rays, CT scans, or MRIs, these images offer valuable insights into a patient’s condition. However, analysing these images can be complex and time-consuming for human radiologists. That’s where AI comes in, acting as a powerful assistant.

AI algorithms, specifically deep Learning, are designed to mimic human brain activity. They learn from large datasets of medical images, becoming experts at spotting abnormalities and patterns. By comparing new images to the patterns they’ve learned, AI algorithms can quickly flag potential issues for radiologists to investigate further. This not only saves time but also enhances accuracy in detecting diseases.

Empowering radiologists, not replacing them

One might wonder, “Does this mean AI will replace radiologists?” The answer is a resounding no. AI is not here to replace the human touch but to complement and enhance radiologists’ capabilities. AI algorithms are trained to assist radiologists, providing valuable insights and supporting their decision-making process.

Radiologists play a crucial role in training AI algorithms by annotating and validating the images used for learning. This partnership between humans and machines ensures that AI remains a useful tool rather than a standalone diagnostician. Radiologists can focus on complex cases, patient interactions, and other aspects of their profession, while AI handles routine tasks quickly and precisely.

Artificial Intelligence (AI) has been creating ripples in healthcare, and radiology is undoubtedly no exception.
Credit. Midjourney

The hardware and software behind AI in radiology

Implementing AI in radiology requires robust hardware and specialised software. Graphic Processing Units (GPUs) with high computational power are essential for AI’s complex image analysis. These GPUs, like the RTX 2070 or RTX 2080 Ti, can quickly process vast amounts of data.

Additionally, AI relies on software to understand and analyse images. Some popular AI software for radiology includes, Accipiolox, AI-Rad Companion, and ClearRead CT, which the FDA has approved. These software solutions help integrate AI into radiology departments, making it easier for medical professionals to leverage AI’s capabilities.

The challenge of data availability

A significant hurdle in AI development is the availability of data. AI algorithms need large, diverse datasets to learn effectively. In radiology, that means accessing various medical images from different patients and conditions.

Creating machine-readable data is no small feat. Radiologists and healthcare providers must tag images and curate text with relevant keywords to enable AI’s automated analysis. Standardising reporting formats among radiologists ensures that AI can efficiently interpret the data.

While AI in radiology offers tremendous potential, it’s essential to understand some of the basic concepts behind the technology. One such concept is Convolutional Neural Networks (CNNs). Consider CNNs as sophisticated image recognition systems inspired by how human brains process visual information.

CNNs consist of multiple layers, each with a specific function. The first layer, known as “first layer convolution,” acts as an image recogniser, identifying key features. Subsequent layers process this information, refining the diagnosis further. For instance, CNNs can pinpoint nodules, consolidation, and pleural effusion in chest radiographs, providing invaluable assistance in diagnosing diseases like tuberculosis.

Challenges with medical images and AI

Medical images present unique challenges for AI algorithms. Each patient’s images are distinct, making comparing them difficult. Variations in scanner types used by different hospitals further complicate the process. Moreover, certain diseases exhibit similar imaging features, requiring AI to be fine-tuned with region-specific data.

When it comes to AI-based medical devices, adherence to regulations is paramount. Agencies like the FDA have established guidelines to ensure patient safety and device effectiveness. AI manufacturers must commit to transparency and continuous performance monitoring to meet these regulatory standards.

AI’s expanding role in radiology

AI’s impact on radiology extends beyond image interpretation. It can automatically enhance image quality by reducing artefacts and optimising exposure control. This advancement has created high-quality images from low-dose raw data, minimising patient radiation exposure.

AI also significantly optimises scanner utilisation and patient wait times, leading to more efficient radiology departments. By automating repetitive tasks, AI empowers radiologists to focus on cases requiring expertise and compassion.

Conclusions

The integration of AI and radiology has brought about a new era of healthcare. AI’s ability to analyse medical images swiftly and accurately has the potential to revolutionise patient care. However, it’s essential to remember that AI is not here to replace radiologists but to empower them with valuable insights and support.

As AI continues to evolve and become more accessible, the future of radiology holds exciting possibilities. With radiologists and AI working hand in hand, patients can expect smarter, faster, and more precise diagnoses, leading to improved healthcare outcomes for all.

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Journal reference

Lecler, A., Duron, L., & Soyer, P. (2023). Revolutionizing radiology with GPT-based models: Current applications, future possibilities and limitations of ChatGPT. Diagnostic and Interventional Imaging104(6), 269-274. https://doi.org/10.1080/14488388.2023.2199600

Dr. Som Biswas is a researcher and a 3rd-year fellow in the Department of Paediatric Radiology at Le Bonheur Children’s Hospital, University of Tennessee Health Science Center, Memphis, Tennessee.