Data mining in healthcare offers transformative insights yet surfaces multilayered
ethical and governance challenges that extend beyond privacy alone. Privacy
and consent concerns remain paramount when handling sensitive medical data,
particularly as healthcare organizations increasingly share patient information with
large digital platforms. The risks of data breaches and unauthorized access are stark:
725 reportable incidents in 2023 alone exposed more than 133 million patient
records, and hacking-related breaches surged by 239% since 2018. Algorithmic bias
further threatens equity; models trained on historically prejudiced data can reinforce
health disparities across protected groups. Therefore, transparency must span three
levels–dataset documentation, model interpretability, and post-deployment audit
logging–to make algorithmic reasoning and failures traceable. Security vulnerabilities
in the Internet of Medical Things (IoMT) and cloud-based health platforms amplify
these risks, while corporate data-sharing deals complicate questions of data
ownership and patient autonomy. A comprehensive response requires (i) datasetlevel artifacts such as “datasheets,” (ii) model-cards that disclose fairness metrics, and
(iii) continuous logging of predictions and LIME/SHAP explanations for independent
audits. Technical safeguards must blend differential privacy (with empirically
validated noise budgets), homomorphic encryption for high-value queries, and
federated learning to maintain the locality of raw data. Governance frameworks
must also mandate routine bias and robust audits and harmonized penalties for
non-compliance. Regular reassessments, thorough documentation, and active
engagement with clinicians, patients, and regulators are critical to accountability.
This paper synthesizes current evidence, from a 2019 European re-identification
study demonstrating 99.98% uniqueness with 15 quasi-identifiers to recent clinical
audits that trimmed false-negative rates via threshold recalibration, and proposes
an integrated set of fairness, privacy, and security controls aligned with SPIRIT-AI,
CONSORT-AI, and emerging PROBAST-AI guidelines. Implementing these solutions
will help healthcare systems harness the benefits of data mining while safeguarding
patient rights and sustaining public trust