An emergent research area is " AI Bias in Healthcare". It shows how decisions / diagnosis could be biased due to biased predictions of ML models. This situation may affect marginalized groups (e.g. racial bias).

Parsa Sarosh Aside from reviewing and developing CNNs, there are various more research avenues for AI-assisted medical diagnostics. Here are a few examples:

1. Transfer learning is the process of fine-tuning a pre-trained model that has been built on a broad dataset for a specific medical application. When there is a limited amount of labeled data available for a certain job, this might be a valuable strategy.

2. Generative models, such as Generative Adversarial Networks (GANs) or Variational Autoencoders (VAEs), can be used to produce synthetic medical pictures to supplement existing datasets.

3. Recurrent Neural Networks (RNNs): These networks are capable of processing sequential data, such as time series data from EKG or EEG signals. This can help with duties like recognizing arrhythmias or seizure activity.

4. Attention-based models employ attention mechanisms to focus on certain portions of a picture, which can be beneficial for tasks like finding cancers in medical photos.

5. Explainable AI (XAI) for medical diagnosis is the development of models that can give interpretable and explainable reasons for the decisions they make.

6. Multi-modal learning: combining data from several sources, such as pictures, text, voice, and signals, to improve performance and detect more complicated diagnoses.

7. Federated learning employs a strategy that permits data to remain on the devices while only model parameters are exchanged and updated across devices. It can be beneficial for medical data since it increases privacy and security.

8. Robustness and adversarial assaults: investigating the robustness of an AI model against hostile attacks, which might be an issue in medical applications where model dependability is crucial.

9. Natural Language Processing (NLP) for medical diagnosis: the use of NLP techniques to extract insights from clinical notes that may be utilized to enhance diagnosis and treatment planning.

These are only a handful of the numerous research paths that might be pursued for AI-assisted medical diagnostics. Each strategy has advantages and disadvantages, and the optimum approach will be determined by the circumstances.

Thank you sir for your valuable insights.

Parsa Sarosh There are several other research directions that one might take for AI-assisted medical diagnosis, some of them include:

Accuracy of CAD and AI-assisted image segmentation and diagnosis, in radiological images.