🩺 How AI is gradually changing Healthcare

Hey AI enthusiast,

We are excited to announce that we’re planning a free workshop on ChatGPT and Large Language Models that will help clarify these topics. As there's so much happening in this field, we want to provide support to those who are interested. If you would like to attend, click here to apply.

📔 Case study: AI in Healthcare

The American Cancer Society (ACS) has worked with IBM Watson to create an AI engine that can help cancer patients access information about the disease.

The initiative incorporates personalized patient data and ACS's localized treatment advice and is part of IBM's Watson for Oncology for Doctors project.

They created a mobile app that offers insights into treatment options, social service support, and caregiver requirements. The app is always available to patients, enabling them to seek advice whenever needed.

We believe that initiatives like this show how AI can be used for good, and we're excited to see what other developments will arise in the future.

🔧 AI Tool: Clearbox AI

Talking about healthcare synthetic data can help maintain privacy while training AI models. For instance, a hospital can work with an AI startup or Google to develop an algorithm to aid doctors in making diagnoses without sharing the personal information of thousands of patients.

Synthetic data generate "fake" versions of the original data, preserving the same statistical properties without including any personal information, and significantly limiting the problem of re-identification.

Using Clearbox AI's platform, anyone can upload their data and get a synthetic version of it, along with a "utility score" and privacy level assessment. Click the button below to try it out for free!

Special thanks to Clearbox AI for sponsoring this email!

🧱 Foundational concepts: Input

In machine learning, "input" refers to the data that is fed into an ML model so that it can learn and make predictions. This data can be anything, such as images, text, numbers, or even sound. Input data is crucial for machine learning models as it determines how well the model can learn and make accurate predictions. The better the input data, the better the model's performance will be. Think of it like cooking a meal, if the ingredients are good, the dish will turn out delicious!

📚 AI Academy spotlight

Our Build Ethical AI program also delves into the application of AI in healthcare. While we acknowledge the potential of this technology to address significant issues, we also explore the potential for biased outcomes if not implemented correctly. Take the first step towards becoming a responsible AI Ethicist and join our course.

That was all. Let us know how we did by answering the poll.