Page 31 - EngineerIT Nov-Dec 2025

HEALTHCARE

The digitisation of health Accountability must therefore be clearly defined. Human oversight
records, combined with should remain central to any AI-powered decision, ensuring that
AI’s hunger for data, technology supports rather than replaces clinical expertise. Ethical
exposes systems to new frameworks that mandate explainability, where AI systems must provide
vulnerabilities. A single understandable reasoning for their outputs, are key to maintaining trust.
breach can compromise Moreover, continuous auditing of AI models, which involves regularly
thousands of medical reviewing and testing the system performance, can help detect and
histories, potentially correct biases or errors before they lead to harm, thereby ensuring the
leading to identity theft or ongoing ethical use of AI in healthcare.
misuse of personal health
information. The paradox Behind the code: who keeps AI ethical
underscores the need for While hospitals and clinics focus on patient care, many lack the
robust data protection internal capacity to manage the complex ethical, security, and
measures in AI-driven technical demands of AI adoption. This is where third-party IT
healthcare systems. providers play a pivotal role. These partners act as the backbone of
responsible innovation, ensuring that AI systems are implemented
Striking a balance securely and ethically.
between data utility
and privacy protection By embedding ethical principles into system design, such as fairness,
has become one of the transparency, and accountability, IT providers help healthcare
healthcare industry’s most institutions mitigate risks before they become crises. They also
pressing ethical dilemmas. play a crucial role in securing sensitive data through advanced
Encryption, anonymisation, encryption protocols, cybersecurity monitoring, and compliance
and strict access management. In many ways, they serve as both architects and
controls are essential, custodians of ethical AI, ensuring that the pursuit of innovation does
but technology alone not compromise patient welfare.
isn’t enough. Patients
need transparency: clear Building a culture of ethical innovation
explanations of how their Ultimately, the ethics of AI in healthcare extend beyond technology;
data is used, who has they are about culture and leadership. Hospitals and healthcare
access to it, and what networks must foster environments where ethical reflection is
safeguards are in place. as integral as technical innovation. This involves establishing
Ethical AI requires not multidisciplinary ethics committees, conducting bias audits, and
only compliance with training clinicians to critically evaluate and question AI outputs rather
regulations but also the than accepting them without examination.
cultivation of trust through
open communication. The future of AI in healthcare depends
not on how advanced our algorithms
Accountability in the become, but on how wisely we use
age of automation them. Ethical frameworks, transparent
When an AI system governance, and responsible
makes a medical partnerships with IT providers can
recommendation, who transform AI from a potential risk into
is ultimately responsible a powerful ally. As the healthcare
for the outcome - the sector continues to evolve, the
algorithm’s developer, institutions that will thrive are those
the healthcare provider, or the institution that remember that technology
that deployed it? The opacity of AI should serve humanity, not the
decision-making, often referred to as other way around.
the “black box” problem, complicates
accountability and transparency. Clinicians
may rely on algorithmic outputs without
fully understanding how conclusions were By Vishal Barapatre,
reached. This can blur the line between Group Chief Technology
human and machine judgment. Officer at In2IT Technologies

31 | EngineerIT November/December 2025