Healthcare data, both structured and unstructured, can be analyzed with artificial intelligence. Techniques commonly used in AI include machine learning techniques for structured data, such as neural networks and deep learning, as well as natural language processing for unstructured data. Cancer, neurology, and cardiology are among the major disease areas for which AI tools are used.
Data privacy and security, Trust in AI, Accountability and responsibility Bias are some common ethical principles that should be addressed in the application of Artificial Intelligence.
For the implementation of AI precautionary principle ‘better safe than sorry’ should be addressed according to the Rio declaration and wingspread statement.

Please see my summarization in the link: https://prnt.sc/4NW0nQAg68SF

1. Responsible Research and Innovation (RRI)
A strategy of stakeholders to become mutually responsive to each other with a view to the (ethical) acceptability, sustainability and societal desirability

Risk/ Benefit versus Precaution
-> Precautionary principle:“Better safe than sorry”

2. Use of Artificial Intelligence (AI) in the field of Health
Benefits: Faster, Accurate, Reducing workloads of healthcare workforce

Ethical concerns over AI: Data privacy and security, Trust in AI, Accountability and
Responsibility, and Bias, especially in the context of low- and middle-income countries

In the discussion about AI and Ethics in Health webinar, we discuss the risk and benefits of using AI and whether it is justified for doing so. In the example of using AI from the CT-Scan data to diagnose COVID-19 in China, it is justifiable to do so because in the era of the pandemic, the target is to diagnose as soon as possible to stop the spread of the disease. So, it is aligned with the “better safe than sorry” principle. There is also the benefit of reducing the workload of healthcare workers and being able to have a source of diagnosis validation. The role of healthcare workers is still important, the Ai can help to improve healthcare by being a decision support tool.

However, there are also concerns and risks of using AI. First, it is prone to bias. The AI’s decision is dependent on it training datasets. The dataset that generated in China, which is composed of China’s population, might not be suitable to use in another country since there will be several disparities such as race that can affect the AI’s decision. The AI can also skip the radiologist role. While it could be beneficial for the patients because it reduces the wait time, it could lead to disrupting their role in healthcare. Other concern that should be highlighted is data privacy and security. The patient might be worried about the leak of their private identity. The system developer and hospital management need to bring assurance to the patients. The patients have their right to raise protests and complaints to the developer and hospital management should there be any case of error in AI diagnosis, even though they have given consent to use their data for AI training.

The implementation of AI needs to be discussed according to the disease. Other diseases that might be benefited from AI are cancer, stroke, and heart disease. There is also a study on implementing AI for malaria diagnosis in Indonesia, where the AI counts the parasite density of the malaria patient.

Hi everyone, here’s my wrap-up for this week. 🙂

(if the image is not embedded, click this link: https://prnt.sc/7jv2PW_T6hyW )

Hello. please find my warp-up attached.

Or access here: https://drive.google.com/file/d/19f836bf1VgSABjLgJbtQKFfEEAkLHhT4/view?usp=sharing

Please find attached
https://drive.google.com/file/d/1Nqn-YP4KoghIJY1_B1SmXx-nyePD8iNV/view?usp=sharing

Hi! Please see my attached:

or click this link: https://prnt.sc/S6gqQ8yQUy2h

Hi Prof,
Here my infographic,
Thank You

https://drive.google.com/file/d/1GDcI6W7u0llF9h_bksG6AwjVycE58JUT/view?usp=sharing