'Neuroanatomy of Diagnosis': AI as a Doctor's Copilot in Medicine

The 'Neuroanatomy of Diagnosis' is a conceptual proposal that reconfigures the contemporary understanding of computed tomography and artificial intelligence in medicine. In an exclusive interview for LaSalud.mx/Oncologia.mx, Julián Retavizca, current Product Manager for Computed Tomography at United Imaging for Latin America, explained that diagnostic imaging is no longer based solely on pixels. From pixels to data: the 'neuroanatomy' of diagnosis According to Julián Retavizca, the 'neuroanatomy of diagnosis' describes the functional structure that makes up medical imaging today: robust data, trained algorithms, and clinical decisions. United Imaging, he noted, has developed a suite of equipment conceived with an integrated artificial intelligence architecture from its core, which increases diagnostic sensitivity in the market. Artificial intelligence: clinical copilot, not a replacement One of the central axes of his presentation was to demystify the idea of professional replacement. The current challenge, Retavizca stated, is not to accelerate but to perfect image quality and diagnostic precision. In oncology, he highlighted tools that improve low-contrast detectability, fundamental for characterizing lesions in early stages. During the LX International Course on Radiology and Imaging, the speaker emphasized that AI does not replace the radiologist but acts as a clinical copilot, optimizing times, reducing doses, and strengthening medical decisions. Precision over speed With temporal resolutions reaching 25 milliseconds, computed tomography has touched physical limits in speed. Although manually configurable, user experience shows a preference for automation. He also recalled that in 2024, at the International Congress of Molecular Imaging, United Imaging received the 'Image of the Year' award for a brain PET-CT. Equitable access and a global vision Julián Retavizca evoked the vision of Dr. Xue Min, president of United Imaging, who promoted technological democratization in China, seeking that both large cities and remote regions would have access to the same diagnostic solutions. This philosophy, he affirmed, guides the regional strategy: to close the gap between large and small hospitals, between urban and remote areas. Can AI detect the invisible? When asked if artificial intelligence can anticipate lesions imperceptible to the human eye, he was categorical: yes. Improvement in low-contrast resolution allows for the visualization of previously indistinguishable structures while maintaining a low radiation dose. The greatest benefit for the patient, he concluded, is threefold: access to first-line technology, dose reduction, and early diagnosis. To these three elements, he added a fourth, frequently overlooked: hardware. Having equipment capable of capturing signals with high fidelity is crucial for generating quality data. In cardiology, tools that reduce noise, preserve low doses, and correct coronary motion artifacts. Voice command and advanced automation Among the mentioned technological innovations was the first angiograph fully controlled by voice, capable of proposing configurations to the interventional physician and executing movements with high flexibility. In neurology, algorithms for vascular analysis and aneurysm detection. He also mentioned automated hepatic segmentation, virtual surgery, and advances in theragnostics supported by molecular imaging, dynamic MRI, and dynamic tomography.