The relationship between artificial intelligence and doctors in diagnosis

Diagnosis is one of the most important responsibilities for all doctors, and its significance to mankind cannot be overstated. Physicians are tasked with deducing diseases or devising treatments based on specific signs and symptoms, observations, and knowledge while diagnosing.

By providing techniques that uncover complex associations that cannot be reduced to an equation, artificial intelligence (AI) has increased the chances of physicians with little or no statistical experience applying the benefits of artificial intelligence-based diagnostic approaches to enhance service improvements.

Artificial Intelligence (AI) techniques have contributed considerably to the advancement of biomedicine and medical informatics by providing reasoning capabilities, which consists of inferences from facts and rules utilizing heuristics, pattern matching, or other search methodologies. The expert system, fuzzy logic, Artificial Neural Networks, and neuro-fuzzy expert system are recent areas of progress in AI in relation to medical diagnostics that are the major approaches with which physicians are supported in this difficult endeavor.

In recent years, artificial intelligence systems (particularly computer-aided diagnostics and artificial neural networks) have found a growing number of applications in medical diagnosis.

These algorithms are adaptive learning algorithms that can handle diverse and heterogeneous types of clinical data and integrate them into classified outputs.

Are computational techniques designed to simulate the way the human brain performs a particular task

  And that is through massive processing distributed in parallel and made up of simple processing units. These units are simple processing. These units are nothing but computational elements called (neurons or nodes), which have a neural property in that they store practical knowledge and empirical information to make them available to the user, by adjusting the weights

So (ANN) is similar to the human brain in that it acquires knowledge by training and stores this knowledge using connections within neurons called synaptic weights.

  There is also a neurobiological similarity, which gives biologists the opportunity to rely on the science (ANN) to understand the evolution of biological phenomena.

Through analysis of several selected physical and mental disorders, the concept, capability, and usefulness of artificial neural network techniques to medical diagnosis are explored.

The majority of review studies in this field focused solely on physical problems, ignoring mental illnesses.

Artificial Neural Networks (ANN) are gaining such popularity that their models and methods are becoming standard tools in computer science, particularly in decision support and expert systems. ANN is undeniably a powerful tool for assisting physicians and other medical specialists and stakeholders with diagnosis, prognosis, and other procedures.

The benefits of ANN include: ability to process a large volume of data; system adaptability and flexibility; timely disease diagnosis; reduced likelihood of overlooking relevant data; and ability to process datasets collected from multiple sources such as voices, images, symptoms, text messages, and so on.

And other uses of Artificial Neural Network that we will talk about in the next articles

The applications of Artificial Neural Network have greatly increased as a result of technical development in the fields of artificial intelligence, including:

1. Getting to know people

2. Recognize situations

3. Marketing and advertising, by anticipating the likelihood of responding to direct mail marketing

4. Detecting and preventing fraudulent transactions in the credit card and insurance claims database

5. Forecasting consumer demand to simplify production and delivery costs

6. Recognize fonts and handwriting

7. Voice or image recognition, fingerprints, etc.

8. Control and simulation of systems, including flight simulation

9. Analysis of financial systems and files in banks in terms of loan, disclosure and deposit transactions