Question

Luella seeks medical attention for chest pains. A nurse uses an artificial intelligence Program to diagnose the cause. Why is this system likely not really intelligent?

A. The program can only be intelligent if the patient provides a complete medical history.
B. The program only matches her symptoms to steps in a system an expert created.
C. The program is only intelligent if a patient has been there before.

Answer

B. The program only matches her symptoms to steps in a system an expert created.

Explanation 1

This is an example of weak AI, unlike strong AI in which the system possesses some human traits.

Explanation 2

Explanation 3

The correct answer to the question is B. The program only matches her symptoms to steps in a system an expert created. This system is likely not really intelligent because it does not exhibit any of the characteristics of artificial intelligence, such as learning, reasoning, or creativity. It simply follows a predefined set of rules or algorithms that an expert has designed based on medical knowledge and experience. The program does not understand the meaning or context of the patient’s symptoms, nor does it generate any new knowledge or insights from the data. It is merely a tool that assists the nurse in making a diagnosis, but it does not have any intelligence of its own.

Explanation 4

B. The program only matches her symptoms to steps in a system an expert created.

The reason why the AI program used by the nurse is likely not really intelligent is because it operates based on a predefined system created by an expert. This system consists of a set of rules or algorithms that match the patient’s symptoms to potential causes or diagnoses. The program does not possess true intelligence or the ability to understand the underlying reasons behind the symptoms.

In this case, the AI program is essentially a decision support tool that relies on pattern recognition and matching. It compares the patient’s symptoms to a database of known symptoms and corresponding diagnoses. The program does not have the capability to reason, learn, or adapt beyond what has been explicitly programmed into it.

True intelligence involves the ability to understand, learn, reason, and adapt based on new information or experiences. It requires a deeper level of comprehension and problem-solving skills that go beyond simple pattern matching. While the AI program may be effective in providing potential diagnoses based on symptom matching, it lacks the true intelligence that a human expert would possess.

It’s important to note that AI programs in healthcare can be valuable tools for assisting medical professionals in making diagnoses, but they should always be used in conjunction with human expertise and clinical judgment.

Explanation 5

Explanation 6

The answer to the question is B. The program only matches her symptoms to steps in a system an expert created.

The reason is that artificial intelligence (AI) is generally defined as the ability of a machine or a program to perform tasks that normally require human intelligence, such as reasoning, learning, decision making, and problem solving . A program that only matches symptoms to steps in a system an expert created does not exhibit these abilities, but rather follows a predefined set of rules or algorithms. Such a program is not really intelligent, but rather mimics intelligence by applying the knowledge of an expert.

A program that can be intelligent if the patient provides a complete medical history (option A) or if a patient has been there before (option C) implies that the program can learn from new data and adapt its behavior accordingly. This would be an example of machine learning, a type of AI that enables machines or programs to learn from data and improve their performance without explicit programming . However, these options are not correct because they are not necessary conditions for intelligence. A program can be intelligent even if it does not have access to complete or previous data, as long as it can reason, learn, decide, and solve problems based on the available data.

Explanation 7

The answer to the question is B. The program only matches her symptoms to steps in a system an expert created. This is because the program is not really intelligent but rather it is programmed to follow a set of rules created by an expert. The program does not have the ability to learn or reason on its own. It can only provide a diagnosis based on the information it has been programmed with.

Explanation 8

Explanation 9

The answer to the question is B. The program only matches her symptoms to steps in a system an expert created. The reason why this system is likely not really intelligent is that it does not have the ability to learn from experience or adapt to new situations. It can only match the patient’s symptoms to a pre-existing database of symptoms and diagnoses.

While AI programs can be very useful in medicine, they are not perfect and have their limitations. For example, they may not be able to take into account all of the patient’s medical history or other factors that could affect their diagnosis.

Explanation 10

The correct answer is B. The program only matches her symptoms to steps in a system an expert created.

An artificial intelligence program that is used to diagnose the cause of chest pains is likely not really intelligent because it only matches her symptoms to steps in a system an expert created. This type of system is called an expert system, which is a computer program that mimics the knowledge and reasoning of a human expert in a specific domain. Expert systems use a set of rules or facts, also known as a knowledge base, that are derived from the expertise of human specialists. Expert systems also use an inference engine, which is a component that applies logical rules to the knowledge base to infer new information or conclusions.

Expert systems are not really intelligent because they do not learn from data or experience, but only follow predefined rules or steps that are given by human experts. Expert systems cannot handle situations that are not covered by their knowledge base or that require common sense, creativity, or intuition. Expert systems also lack transparency and explainability, meaning that they cannot provide justification or evidence for their decisions or recommendations.

Therefore, an artificial intelligence program that is used to diagnose the cause of chest pains is likely not really intelligent because it only matches her symptoms to steps in a system an expert created, rather than learning from data or generating new knowledge.

Explanation 11

The correct answer is D. It looks at the data and makes guesses, then it compares those guesses to the correct answer.

Artificial neural networks (ANNs) learn by examining data, making predictions or guesses, and then comparing those predictions to the correct answers. Here’s a more detailed explanation of how an artificial neural network learns:

1. Data Input: ANNs are fed with input data, which consists of features or patterns that the network needs to learn from. The input data is typically represented as numerical values.

2. Forward Propagation: The input data is processed through the network’s layers of interconnected artificial neurons, starting from the input layer and moving through one or more hidden layers to the output layer. Each neuron performs a weighted computation based on the input it receives and applies an activation function to determine its output.

3. Prediction or Guess: As the input data propagates through the network, it generates predictions or guesses for the corresponding output values. These predictions are produced by the final layer of the network, which is the output layer.

4. Error Calculation: The predicted output is compared to the actual or correct output values. The difference between the predicted output and the correct output is quantified as an error or loss. Various loss functions can be used depending on the nature of the problem, such as mean squared error or cross-entropy loss.

5. Backpropagation: Backpropagation is the key mechanism for learning in artificial neural networks. It involves calculating the gradients of the error with respect to the network’s weights and biases. The gradients provide information on how much each weight and bias contributed to the overall error.

6. Weight and Bias Update: The gradients obtained through backpropagation are used to update the weights and biases of the artificial neurons in the network. This update is performed using optimization algorithms such as gradient descent or its variants. The objective is to adjust the weights and biases in a way that minimizes the error in subsequent iterations or epochs.

7. Iteration and Learning: The process of forward propagation, error calculation, backpropagation, and weight update is repeated iteratively for multiple epochs. During each iteration, the network refines its predictions and adjusts its internal parameters based on the comparison between predicted outputs and correct outputs. Over time, the network learns to make more accurate predictions and minimize the error.

Option A, “A computer scientist programs each neuron to have the correct answer to any question,” is incorrect. ANNs do not have their neurons individually programmed with the correct answers. Instead, they learn to generate predictions by adjusting the weights and biases of the neurons based on the training data.

Option B, “Only correct answers go into the input layer, so it learns what’s correct from the output layer,” is incorrect. ANNs do not receive only correct answers in the input layer. The input layer receives the raw data, and the network learns to associate the input patterns with the corresponding correct outputs during the learning process.

Option C, “The hidden layers hide the incorrect answers from the rest of the network,” is incorrect. Hidden layers in ANNs do not hide incorrect answers. They process the input data and contribute to the network’s ability to learn complex patterns and relationships in the data.

In summary, an artificial neural network learns by looking at the data, making guesses or predictions, comparing them to the correct answers, and adjusting its internal parameters (weights and biases) through backpropagation and optimization algorithms. Through this iterative process, the network gradually improves its predictions and learns to generalize from the provided data to make accurate predictions on unseen data.

Reference

• Artificial intelligence could improve heart attack diagnosis to reduce pressure on emergency departments (medicalxpress.com)
• Computer-aided detection in chest radiography based on artificial intelligence: a survey | BioMedical Engineering OnLine | Full Text (biomedcentral.com)
• Artificial intelligence could prevent unneeded tests in patients with stable chest pain (escardio.org)
• Artificial intelligence could prevent unneeded tests in patients with stable chest pain (medicalxpress.com)
• Current and emerging artificial intelligence applications in chest imaging: a pediatric perspective | SpringerLink
• Artificial Intelligence in Medicine: Applications, implications, and limitations – Science in the News (harvard.edu)
• Risks and benefits of an AI revolution in medicine – Harvard Gazette
• Artificial Intelligence in Medical Diagnosis (mit.edu)
• Artificial intelligence in disease diagnostics: A critical review and classification on the current state of research guiding future direction | SpringerLink
• Expert Systems and Applied Artificial Intelligence (umsl.edu)
• The 4 Types of Validity in Research | Definitions & Examples (scribbr.com)
• What is Alzheimer’s Disease? Symptoms & Causes | alz.org

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