Artificial Intelligence refers to the task designed and given to a computer and algorithm that works similarly to the human brain. It solves problems, acts as an assistant, and performs various works in many fields.

List of the various work done under AI and improved the cure of the disease.

1.Many companies have digitized Clinical trial paperwork with the help of OCR technology. It has an accuracy of 80%, and companies utilize it for analysis and reporting.

2.Deep learning methods affect finding the solution for AI health problems.

Radiology is well known for studying internal organs by generating images with the help of radiation such as CT- Scan, X-rays, ultrasound, and MT-Scan. Deep learning enables the detection of complex patterns to diagnose the disease.

3.Artificial Intelligence for the Diabetic Retinopathy Early Detection. Diabetic retinopathy is the cause of vision loss, and its early detection can help doctors treat the patient. AI helps in detecting without the use of any resources.

4.AI in performing thoracic surgeries. 

5.Remote monitoring of chronic diseases.

6.AI can act as a chatbot assistant for health-related questions and other details.

7.It can overcome billing errors and help in the detection of fraud.

Fig 1 Representation of AI in Medicines.

Whether we talk about Diabetes, Breast, Heart, or lung disease in every sector, AI has played a crucial role in early detection and cure.

AI technology has not only reduced the cost but made it faster, more efficient, and with improved accuracy, solving complex issues.

What was previously known as human work is now the expertise of AI. 

Digitized medical services present various open doors for lessening human blunders, working on clinical results, following information over the long haul, and so on. From machine learning to deep learning, AI techniques help in health-related fields, whether new clinical systems, managing patient data, or curing various diseases.

Fig 2: AI and robots have played a significant role during the COVID-19 Pandemic.

The technological field of AI and robotics has solved many problems linked to the health sector. The COVID-19 outbreak created new challenges for the healthcare industry and researchers and innovators that sum up with new solutions. 

Fig 3 Five steps are involved in using the data.

Xgboost Classifier shows an accuracy of 0.96 for early breast cancer detection. 

How to use Machine Learning for breast cancer detection (indiaai.gov.in)

Six standards announced by the WHO are as follows.

1.Confidential - Patients must give valid informed consent for data privacy and protection.

2.For well-defined use cases or indications, the developers of AI technologies ought to meet regulatory requirements regarding safety, accuracy, and efficacy. Quality improvement in the utilization of Artificial intelligence should be accessible.

3.Publication or documentation should be enough before AI techniques usage. This information must be simple to find and allow for meaningful public discussion and consultation regarding the technology's design and intended use.

4.Liability and responsibility. Stakeholders are responsible for ensuring that AI technologies are used appropriately and by appropriately trained individuals, despite their ability to perform specific tasks. 

5.Comprehensiveness expects simulated intelligence for well-being to energize the broadest conceivable impartial use and access, independent old enough, sex, orientation, pay, race, identity, sexual direction, capacity, or different qualities safeguarded under basic liberties codes.

6.To determine whether AI meets expectations and requirements adequately and appropriately, designers, developers, and users should continuously and transparently evaluate AI applications during actual use.

Summary

AI-powered solutions, including telepsychiatry, health services, virtual reality (VR), chatbots, and companion bots, are helping scientists and doctors do the work efficiently and with a fast speed and improved accuracy, which was impossible before AI. By standardizing the infrastructure, these services still have the potential to enhance the serviceable functionalities to a greater extent.

Deep learning (DL) will replicate human language convincingly and human-like in large language models like ChatGPT in content marketing, customer service, and business applications, and their use is growing.

As a consequence of this, it is unavoidable that language models will also make their debut in the healthcare industry.

Healthcare is a sector in which language models have a tremendous potential to enhance patients' lives and improve their health, but they also face challenges.

India AI, Springer