AI For Social Good
Topic idea: public health
In the United States, close to 495,634 preventable premature deaths occurred in
2022, a major increase from 308,286 in 2010 (Hacker, 2024). Many of these deaths are
linked to treatable conditions that were not addressed through early detection or access
to medical care. This shows how serious the problem of delayed diagnosis and limited
healthcare access has become. Public health continues to face challenges like doctor
shortages, expensive treatment, and data overload, which can slow down the process
of helping patients in time. Artificial intelligence has the potential to change this by being
able to identify diseases earlier, improving access to healthcare, and supporting doctors
with more accurate information. By using AI for early detection and diagnosis, the
healthcare system can prevent countless deaths and make medical care more efficient
and accessible for everyone.
AI detects cancer by analyzing medical images (like mammograms and MRIs)
and tissue slides, often finding subtle patterns missed by the human eye. This
technology is used alongside regular checkups to help with closing the gap and
stopping preventable deaths that happen from lack of early detection systems. These
“... algorithms are performed with flying colors. It correctly identified 92 percent of
patients who developed Alzheimer’s disease... (Smith, 2019). “ They can help doctors
get patients the right kind of care earlier than waiting on doctors for the pickup on what
is wrong.
Some ethical and environmental challenges may get in the way of our potential
solution of using AI to detect preventable diseases. For example, the heavy usage of AI
to obtain large amounts of data contributes to the increasing levels of greenhouse gas
emissions that is released into the environment. Additionally, with the development of
newer AI models, a lot of energy is required towards their creation, hence releasing that
into the earth and furthering climate change. (Doo, 2024). In terms of ethical challenges,
the usage of AI would require access to personal data, which clients might not approve
of. AI might also use the data of people in ways that they might not approve of, creating
distrust within the institution, such as a hospital, and the patient, who may or may not
still be a current client.
The increase in preventable mortality in the United States suggests that
improving early detection and access to care is no longer optional—it is urgent. Artificial
Intelligence can be a powerful agent for closing this gap through using AI to help
physicians detect disease sooner, to analyze complex clinical data more quickly, and to
reach patients who otherwise are unrecognized. To actually enhance the public good,
however, hospitals, policy makers and AI developers will need to be allied with one
another to build evidence-based, ethical systems.
To support this need, governments need to invest in sustainable AI, research in
systems that simultaneously decrease energies consumption to sustain our world.
Healthcare institutions must also implement data privacy safeguards—strict adherence
to systems that protect patient health information continues to be a critical factor in
patient trust. Clinicians also need training to use AI responsibly while reinforcing
technology as complementing human judgment—rather than replacing it.
With the power of AI, there are possibilities that have never been seen before.
You have the power to do things faster and better than ever before. Through the power
of AI, healthcare professionals will have the power to detect diseases faster which only
makes the lives of healthcare workers that much easier.
Work Sited:
Smith, Nina Bai and Dana. “Ai Could Catch Alzheimer’s in Brain Scans 6 Years Earlier.”
Archive: Artificial Intelligence Can Detect Alzheimer’s Disease in Brain Scans Six
Years Before a Diagnosis | UC San Francisco, 7 Nov. 2025,
www.ucsf.edu/news/2019/01/412946/artificial-intelligence-can-detect-alzheimersdisease-brain-scans-six-years.
Hacker, Karen. “The Burden of Chronic Disease.” Mayo Clinic Proceedings:
Innovations, Quality & Outcomes, vol. 8, no. 1, 1 Feb. 2024, pp. 112–119,
https://doi.org/10.1016/j.mayocpiqo.2023.08.005. Accessed 10 Nov. 2025.
Doo, Florence X, et al. “Environmental Sustainability and AI in Radiology: A Double-Edged
Sword.” Radiology, U.S. National Library of Medicine, Feb. 2024,
pmc.ncbi.nlm.nih.gov/articles/PMC10902597/. Accessed 10 Nov. 2025.
Koh, DM., Papanikolaou, N., Bick, U. et al. Artificial intelligence and machine learning in
cancer imaging. Commun Med 2, 133 (2022). https://doi.org/10.1038/s43856-022Dankwa-Mullan I. Health Equity and Ethical Considerations in Using Artificial Intelligence in Public Health and
Medicine. Prev Chronic Dis 2024;21:240245. DOI: http://dx.doi.org/10.5888/pcd21.240245