AI in HealthCare

Artificial Intelligence in Healthcare :

Healthcare organizations are using AI solutions to inform decisions and improve experiences with data.

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Why use AI for healthcare?

Artificial intelligence (AI) and machine learning solutions are transforming how healthcare is delivered. Health organizations have accumulated vast data sets in the form of health records and images, population data claims data, and clinical trial data. AI technologies are well suited to analyze this data and uncover patterns and insights humans could not find independently. With deep learning from AI, healthcare organizations can use algorithms to help them make better business and clinical decisions and improve the quality of their experiences.

                                

Benefits of AI in healthcare

1. Providing user-centric experiences: Using large datasets and machine learning, healthcare organizations can find insights faster and more accurately with AI, enabling improved satisfaction both internally and with those they serve.
2. Improving efficiency in operations: By examining data patterns, AI technologies can help healthcare organizations make the most of their data, assets, and resources, increasing efficiency and improving the performance of clinical and operational workflows, processes, and financial operations.
3. Connecting disparate healthcare data: Healthcare data is often fragmented and in various formats. By using AI and machine learning technologies, organizations can connect disparate data to get a more unified picture of the individuals behind the data.

Follow best practices to overcome the challenges of AI in healthcare

Implementing AI in the healthcare sector is not easy; it requires substantial investments and strategic planning. Here are some ways to overcome challenges that healthcare professionals might face while implementing AI in their facilities.

• Understand how AI works: A hybrid approach where doctors and the AI tool work together is common for AI implementation. This can be a problem if the doctors do not understand how the system works. For example, if a mammogram analyzing medical vision system detects cancer and the radiologist does not know how or why that system detected that cancer, it could put lives at risk.
• Train AI more to prevent diagnostic errors: AI-enabled medical diagnoses are accurate; however, not perfect. AI systems can make errors that can create dire consequences. Testing your AI models more is a good way of increasing accuracy and reducing false positives.
• Utilize innovative ways of data annotation: Gathering training medical data can be a challenge due to privacy and ethical constraints in the healthcare sector. This process can be expensive and time-consuming, even when automated. Innovative ways of data annotation are helping overcome this challenge. Investing in privacy-enhancing technologies can also help reassure
  people of data protection while gathering and working with sensitive medical data.
• Provide training to healthcare workers: Another challenge is the hesitation of healthcare workers in accepting AI. AI is perceived as a threat to replace human jobs, whereas, in reality, it cannot. Training and educating healthcare workers can eliminate this misconception.
• Educate to reduce patient reluctance: Patient reluctance is another major challenge in implementing AI in healthcare. At first glance, a robotic surgery might scare the patients, but as they understand and learn the benefits, the hesitations might fade away. Therefore properly educating the patients is very important to overcome this challenge.

AI in Deep Learning

 Deep Learning :

Deep learning is an AI technology that has made inroads into mimicking aspects of the human brain — giving a device the ability to process information for contextual analysis and action.

Researchers continue to develop self-teaching algorithms that enable deep learning AI applications like chatbots.

AI Use Cases With Deep Learning :

Deep learning promises to uncover information and patterns hidden from the human brain from within the sea of computer data. 

AI with deep learning surrounds us. Apple’s Siri and Amazon’s Alexa try to interpret our speech and act as our personal assistants. Amazon and Netflix use AI to predict the next product, movie, or TV show we may want to enjoy. Many of the websites we visit for banking, health care, and e-commerce use AI chatbots to handle the initial stages of customer service.

Deep learning algorithms have been applied to:

• Customer service: Conversational AI incorporates natural language processing call-center style decision trees, and other resources to provide the first level of customer service as chatbots and voicemail decision trees.
• Cybersecurity: AI analyzes log files, network information, and more to detect, report, and remediate malware and human attacks on IT systems.
• Financial services: Predictive analytics trade stocks, approve loans, flag potential fraud, and manage portfolios.
• Health care: Image-recognition AI reviews medical imaging to aid in medical analysis
• Law enforcement: 
  • Track payments and other financial transactions for signs of fraud, money laundering, and other crimes
  • Extract patterns from voice, video, email and other evidence
  • Analyze large amounts of data quickly

source by : dotnettutorials.org

Application of Deep learning :

1. Self Driving Cars
2. News Aggregation and Fraud News Detection
3. Natural Language Processing
4. Virtual Assistants
5. Entertainment
6. Visual Recognition
7. Fraud Detection
8. Healthcare
9. Personalisations
10. Detecting Developmental Delay in Children
11. Colourisation of Black and White images
12. Adding sounds to silent movies
13. Automatic Machine Translation
14. Automatic Handwriting Generation
15. Automatic Game Playing

AI in Machine Learning

 What is Machine Learning ?

Machine learning is part of Computer engineering course. Machine learning is way to create new thing as robot , Ai machine , bots etc.

There are a variety of different machine learning algorithms, with the three primary types being supervised learning, unsupervised learning and reinforcement learning. 

=> 3 types of machine learning algorithms

As with the different types of AI, these different types of machine learning cover a range of complexity. And while there are several other types of machine learning algorithms, most are a combination of—or based on—these primary three.

Supervised learning is the simplest of these, and, like it says on the box, is when an AI is actively supervised throughout the learning process. Researchers or data scientists will provide the machine with a quantity of data to process and learn from, as well as some example results of what that data should produce (more formally referred to as inputs and desired outputs).

The result of supervised learning is an agent that can predict results based on new input data. The machine may continue to refine its learning by storing and continually re-analyzing these predictions, improving its accuracy over time.

Supervised machine learning applications include image-recognition, media recommendation systems, predictive analytics and spam detection.

                                           

                                               source by : geeksforgeeks.org

Unsupervised learning involves no help from humans during the learning process. The agent is given a quantity of data to analyze, and independently identifies patterns in that data. This type of analysis can be extremely helpful, because machines can recognize more and different patterns in any given set of data than humans. Like supervised machine learning, unsupervised ML can learn and improve over time.

Unsupervised machine learning applications include things like determining customer segments in marketing data, medical imaging, and anomaly detection.

     source by : geeksforgeeks.org

Reinforcement learning is the most complex of these three algorithms in that there is no data set provided to train the machine. Instead, the agent learns by interacting with the environment in which it is placed. It receives positive or negative rewards based on the actions it takes, and improves over time by refining its responses to maximize positive rewards.

                                               source by : geeksforgeeks.org

Application of Machine learning :

• Automatic Language Translation
• Medical Diagnosis
• Stock Market trading
• Online fraud detection
• Virtual personal assistant
• Email spam and malware filtering
• Self driving cars
• Product recommendations

Artificial Intelligence

What Is Artificial Intelligence (AI) ? 

Artificial intelligence is a wide-ranging branch of computer science concerned with building smart machines capable of performing tasks that typically require human intelligence.

AI Performed task Compare to human. Now Day  AI is very Popular and Research Place in Science Field.

Type of AI :

Source by : techtarget.com

Main in AI ,  there is four type :

• Type 1: Reactive machines. These AI systems have no memory and are task specific. An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chessboard and make predictions, but because it has no memory, it cannot use past experiences to inform future ones.
• Type 2: Limited memory. These AI systems have memory, so they can use past experiences to inform future decisions. Some of the decision-making functions in self - driving cars are designed this way.
• Type 3: Theory of mind. Theory of mind is a psychology term. When applied to AI, it means that the system would have the social intelligence to understand emotions. This type of AI will be able to infer human intentions and predict behavior, a necessary skill for AI systems to become integral members of human teams.
• Type 4: Self-awareness. In this category, AI systems have a sense of self, which gives them consciousness. Machines with self-awareness understand their own current state. This type of AI does not yet exist.

Application Of AI :

1. AI in Astronomy
2. AI in Healthcare
3. AI in Gaming
4. AI in Finance
5. AI in Data Security
6. AI in Social Media
7. AI in Travel & Transport
8. AI in Automotive Industry
9.  AI in Robotics
10. AI in Entertainment
11. AI in Agriculture
12. AI in E-commerce
13. AI in education

Advantages in AI :

1. Fewer Chances of human error.
2. Reduce the Riske.
3. !00% Availability.
4. Digital Assistance.

Disadvantages in AI :

1. Costly in Creation.
2. Lessening Human Ability.
3. Unemployment.
4. Lack of Human Emotions.
5. Lack of Human Intelligence.