Not sure we understand what it is you are asking. Models have been implemented to aide in the identification of tumors in the software of the systems by OEMs (expect that to continue) there are also a number of solutions for the "off-line" processing of image sets.

I think it is still too early for the market to pick the winners and losers. There will likely be a period of consolidation of the third-party companies first (as we have seen in other aspects of imaging.

An innovative microscope allows scientists to peek at the brain’s long-range neuronal circuitry without having to reconstruct it from individual brain slices. The technique removes lipids to make the tissue transparent before it is embedded in a material that expands when water is added. At the heart of the method is a type of lens that is usually used to identify pixel-sized defects in flat-panel displays. The prototype microscope’s resolution is comparable to that of high-resolution imaging using standard confocal microscopy...

https://www.nature.com/articles/d41586-023-02570-0

Yes, deep learning models particularly Convolutional Neural Network( CNNs) have demonstrated superior performance in many medical image analysis tasks such as detecting brain tumor with different imaging modalities such as MRI, CT scan , PET scan etc.

deep learning models can be effectively integrated with medical imaging technologies, such as MRI, CT, and PET, to enhance the accuracy and efficiency of brain tumor detection.

The steps to achieve this include:

*Data Acquisition and labeling data

*Data Preprocessing

*Model Selection

*Training: Split the dataset into training, validation, and test sets.

*Evaluation

*Integration into Clinical Workflow

*Continuous Improvement

The benefits of integrating deep learning models with medical imaging technologies for brain tumor detection include increased speed and efficiency, potentially leading to earlier diagnosis and treatment. However, it's essential to address challenges such as interpretability and ethical considerations, especially in a medical context where the stakes are high.

Any machine learning or deep learning model is built using huge amount of data. ML i.e machine learning is a subset of AI and DL i.e deep learning is a subset of ML. In medical image analysis , for the task of accurate detection/ segmentation/ classification / prediction of brain tumor ,we require an AI / ML/DL based model which is built from various imaging modalities such as MRI, CT scan , PET , SPECT etc. Convolutional Neural Network i.e. CNN is an established AI model for any computer vision task . For , brain tumor segmentation, U-Net has been employed successfully.