Page 13 - AI for Good - Impact Report

AI for Good

conduct over 800,000 experiments weekly, combining high-throughput biology and automation
with the latest advances in AI. Additionally, AI technologies such as deep learning and natural
language processing are being used to detect patterns in genetic data, enabling precision
medicine. For example, a South Korean company developed a targeted anti-cancer medication,
using its AI platform. This platform employs machine learning and deep learning algorithms to
detect patterns within extensive datasets for potential drug candidates, reducing the time and
costs associated with drug development. 18

AI enhances clinical decision support systems, enabling healthcare professionals to make
informed decisions based on comprehensive data analysis. For instance, a healthcare app from
a major tech company uses AI solutions to empower physicians to make better clinical decisions
by analyzing electronic health records, identifying patients who need early hospitalization or
specific medication plans, thereby improving patient care and outcomes. Additionally, AI-
powered systems analyze large volumes of data to detect early signs of diseases such as
cancer and vascular diseases, providing valuable insights that aid in clinical decision-making.
A Rwandan-based health tech company aids health care facilities in Rwanda and across East
Africa in procuring essential medical supplies with its AI-driven medical procurement platform. 19

The education sector is adopting AI to provide customized learning experiences, developing
digital tutors for real-time feedback and support, as well as assessment tools to identify areas
where students require additional assistance. These AI-driven solutions aim to enhance the
20
learning experience and improve educational outcomes.

This involves creating customized learning programs tailored to each student's needs, tracking
their progress, diagnosing misconceptions, and offering timely guidance and feedback. For
instance, machine learning capabilities embedded in online learning programs can alert
teachers if a student misunderstands a particular concept, allowing for early intervention. This
trend is supported by substantial investments, such as the US$240 million from a well-known
foundation for initiatives focused on developing software for personalized learning plans based
on student performance, led mainly by private companies. 21

Intelligent Tutoring Systems (ITS) are one of the most widely used AI applications in education.
These systems use AI and machine learning technologies to gather in-depth data on individual
students, assess their progress, and offer feedback to promote productive learning behaviors
such as self-regulation and self-monitoring. ITS are now commonly used in schools and colleges,
particularly in the United States, to provide real-time feedback and support to students as
they go through their coursework. This use of AI can also help to bridge the educational gap
in developing countries. A pilot project in Rwanda used AI to help 90 high school students to
enhance their math skills through personalized learning and practice exercises, complementing
their regular lessons. 22

AI is also being used to develop assessment tools that help educators identify areas where
students require additional support. Digital tutors powered by AI can provide real-time feedback
and support, enhancing the learning experience. These tools are designed to offer personalized
solutions and adapt to the individual needs of students, making education more efficient and
effective. This trend reflects a broader move towards using AI to create more interactive and
engaging educational technologies, including homework support systems, science simulations,
virtual labs, educational games, and online courses.

3