Page 30 - EngineerIT Nov-Dec 2025

HEALTHCARE

THE ETHICAL PULSE OF PROGRESS:
TH E E T H I C A L P U L SE O F P R OG R E S S :
PULSE OF PROGRESS:

AI’s promise and peril in healthcare
A I ’ s p r o m i s e an d p e r i l in h e al t h c ar e
and peril in healthcare

rtificial Intelligence (AI) is
revolutionising healthcare as
Aprofoundly as the discovery
of antibiotics or the invention of the
stethoscope. From analysing X-rays
in seconds to predicting disease
outbreaks and tailoring treatment
plans to individual patients, AI has
opened new possibilities for precision
medicine and increased efficiency.
In emergency rooms, AI-driven
diagnostic tools are already helping
doctors detect heart attacks or strokes
faster than human eyes alone.

However, as AI systems become
increasingly embedded in the
patient journey, from diagnosis to
aftercare, they raise critical ethical
questions. Who is accountable when
an algorithm gets it wrong? How can
we ensure that patient data remains
confidential in the era of cloud
computing? And how can
healthcare institutions, often
stretched thin on resources, balance
innovation with responsibility?

When algorithms diagnose: the
promise and the problem
AI’s strength lies in its ability to process
massive amounts of data, such as
medical histories, imaging scans, and
lab results, and detect patterns that
human clinicians might miss. This
can dramatically improve diagnostic
accuracy and treatment outcomes.
For instance, AI models trained on demographic group, the results may unfairly disadvantage others. A diagnostic
thousands of mammogram images model trained primarily on data from urban hospitals, for example, might
can help identify subtle indicators misinterpret symptoms in patients from rural areas or underrepresented ethnic
of breast cancer earlier than groups. Bias in healthcare AI isn’t just a technical flaw; it’s an ethical hazard
traditional methods. with real-world consequences for patient trust and equity.

However, the same data that powers The privacy paradox
AI can also introduce bias. If the The integration of AI in healthcare requires access to vast quantities of
datasets used to train an algorithm sensitive data. This creates a privacy paradox: the more data AI consumes, the
are skewed, say, over-representing one smarter it becomes, but the greater the risk to patient confidentiality.

30 | EngineerIT November/December 2025