Images of X-Rays

I am new to AI, but just thinking if we trained the AI software to what is good X-rays image and what rest are bad and have some problems, will this not diagnose the disease

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Hi @Kamlesh_Kumar2 , I am not sure if I got your point, but if you have a model trained with good images and then use it to infer using bad images, the model will not be able to diagnose diseases once the images cannot provide the useful information that the model must use for diseases detection.

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Hi @Kamlesh_Kumar2

Welcome to the community.

That is a great question. Well done!

So, here some points that a consider important in this discussion.

Training AI software to distinguish between good and bad X-ray images can indeed be a valuable approach to assist in disease diagnosis. However, it’s crucial to understand the complexities and challenges involved in developing such AI systems.

Training an AI model to recognize good X-ray images from problematic ones requires a large and diverse dataset of labeled X-ray images. These images should include a wide range of healthy and disease-related cases. The AI model then learns patterns and features that differentiate between normal and abnormal X-ray images.

Here are some important factors to consider:

  1. Dataset Quality: The accuracy of AI diagnosis heavily relies on the quality and representativeness of the training dataset. An insufficient or biased dataset may lead to inaccurate results.
  2. Ethical and Legal Considerations: AI models used for medical diagnosis must adhere to strict ethical guidelines and meet regulatory standards to ensure patient safety and data privacy.
  3. Interpretability and Transparency: AI models for medical diagnosis should be interpretable, meaning they can provide insights into their decision-making process, making it easier for medical professionals to understand and trust the results.
  4. Human Expertise: AI should be considered as a tool to assist medical professionals rather than a replacement. Human expertise is essential in interpreting AI results, considering patient history, and making informed decisions.
  5. Continual Improvement: AI models should be regularly updated and improved with new data and research advancements to maintain accuracy and relevance.

It’s important to recognize that AI models are not infallible and have limitations. They may not capture all nuances and complexities of medical conditions, and their results should always be cross-validated by medical professionals.

In conclusion, training AI software to differentiate good and problematic X-ray images can be a valuable aid in disease diagnosis. However, it must be done with caution, ethical considerations, and a deep understanding of the domain to ensure the safety and efficacy of its applications in healthcare. Collaboration between AI experts and medical professionals is key to harnessing the full potential of AI in medical diagnosis responsibly.

I hope that the points that i bring to this discussion helps you in your learning journey.



Hi @Kamlesh_Kumar2

It’s important to know that current algorithms can make a doctor’s job a lot easier. I work with digital pathology and AI helps to recognize patterns, quantify certain parameters, and even identify certain alterations that may not have been visualized by the pathologist, even more so if he is not specialized in that area. But the final diagnosis, both in radiology and pathology, must be given by the physician, using as many tools as he has, increasingly including AI. In my area, neuropathology, we still have fewer tools, but in others, such as gynecopathology and uropathology, they are already much more advanced.

Hope I´ve helped!