Ethical AI in Healthcare: Who Controls the Algorithms?

6 min readJun 1, 2025

The global AI healthcare market has exploded to $37 billion in 2025 and is racing toward $674 billion by 2034, representing the largest technological transformation in medical history. But here’s the uncomfortable truth: while these algorithms can detect cancer earlier than the world’s best radiologists and predict heart attacks years in advance, they’re also making biased decisions that could cost lives.

In 2019, a widely-used U.S. healthcare algorithm was found to exhibit racial bias. It systematically underestimated the health needs of Black patients, resulting in millions receiving inadequate care. The developers didn’t set out to be biased, but the algorithm’s training data reflected systemic inequalities in healthcare access and spending.
This isn’t an isolated incident. It’s a warning.

As ethical concerns escalate, ranging from racial bias and privacy violations to explainability gaps, the need for transparent, accountable, and patient-centric AI governance is no longer optional.

The integration of Ethical AI in Healthcare is revolutionizing patient care, medical diagnostics, and treatment efficiency. However, the rapid expansion of AI Ethics in Healthcare raises concerns about bias, accountability, and control over the algorithms that determine critical medical decisions. As AI-driven solutions become more prevalent, ensuring ethical governance and transparency remains a top priority for industry leaders like Medifakt.

As of May 2024, the FDA has approved 882 AI-enabled medical devices — a 5x increase in just three years. As AI systems gain the power to make or heavily influence critical decisions in diagnosis, treatment, and resource allocation, we must ask:
Who controls the algorithms that dictate these life-altering choices? And who ensures they’re ethical, accurate, and fair?

The Ethical Dilemmas of Healthcare AI

1. Algorithmic Bias When Data Reflects Discrimination

One of the biggest challenges in AI Ethics in Healthcare is algorithmic bias. AI systems learn from historical medical data, which is often riddled with societal biases. When trained on skewed datasets, these models can perpetuate inequality, giving worse care recommendations to certain ethnic groups or genders.

Optum Algorithm (U.S.) This algorithm, used by health systems covering over 200 million people, predicted healthcare needs based on historical costs, not illness severity. Because Black patients historically received less care despite being sicker, the AI concluded they required less support. The result? Biased healthcare delivery.

2. Data Privacy Violations

The promise of personalized care comes with a price: data. AI needs massive patient datasets to learn, but where that data comes from and how it’s used is often murky.

AI systems require vast amounts of patient data to function effectively. Ensuring data privacy, security, and compliance with regulations such as HIPAA (Health Insurance Portability and Accountability Act) and GDPR (General Data Protection Regulation) is crucial. Unauthorized access or breaches of sensitive medical information can lead to legal and ethical dilemmas.

In the UK, Google DeepMind’s partnership with the NHS violated data protection laws by using 1.6 million patient records without informed consent.

Source: ICO Report, 2017

3. Opaque “Black Box” Decision-Making

Healthcare professionals often can’t see how AI reaches its conclusions. This lack of explainability undermines trust, and accountability,especially in high-risk areas like cancer treatment or emergency triage.

IBM Watson for Oncology was marketed as a cancer-recommending AI. But in multiple hospitals, doctors reported that Watson suggested unsafe treatments — sometimes based on fictional case data.
Source: STAT News Investigation

4. Regulatory and Ethical Frameworks

Current regulations struggle to keep up with the rapid advancements in AI technology. Governments and healthcare organizations must establish robust ethical guidelines and regulatory frameworks to govern AI usage in medicine. Ethical AI should prioritize accountability, fairness, and patient safety over commercial interests.

So, Who Does Control the Algorithms?

AI in healthcare is developed and controlled by various stakeholders, including tech companies, healthcare providers, researchers, and policymakers. Each has different incentives, which can sometimes conflict with ethical principles.

Principles for Ethical AI in Healthcare

1. Ethical AI Frameworks and Regulations

Governments and healthcare authorities must implement strong regulatory frameworks that mandate transparency, fairness, and accountability in AI-driven medical solutions. These frameworks should include guidelines for data collection, bias mitigation, and human oversight.

2. Explainable and Transparent AI (XAI)

To ensure trust in AI-driven medical decisions, AI models should be designed with explainability in mind. Explainable AI (XAI) allows medical professionals to understand and validate the rationale behind an AI’s recommendations.

3. Human-in-the-Loop (HITL) Approach

The Human-in-the-Loop (HITL) approach ensures that AI does not replace healthcare professionals but instead acts as an augmentative tool. Physicians should always have the final say in AI-generated decisions, ensuring ethical oversight and preventing errors.

4. Addressing Bias in AI Models

Developers must use diverse datasets to train AI models, reducing bias and improving accuracy across different patient demographics. Periodic audits should be conducted to assess and rectify potential biases.

5. Patient-Centric AI Development

AI systems must prioritize patient well-being over financial incentives. Ethical AI development should include input from patients, physicians, and ethics boards to create more balanced, patient-centric AI models.

The Playbook: Solutions That Actually Work

1. Mandatory Real-Time Bias Monitoring

Netherlands’ Success Model: The Netherlands’ Algorithm Register requires public disclosure of all government AI systems, with quarterly bias audits resulting in 47% reduction in discriminatory outcomes within two years.

Implementation Framework for Healthcare:

Continuous monitoring: AI systems checked for bias every 30 days
Automatic alerts: Real-time notifications when bias thresholds are exceeded
Public reporting: Monthly transparency reports for all healthcare AI systems
Patient access: Individuals can request their AI decision reasoning

2. Diverse Data Mandates

Stanford’s CheXNet 2.0 Success: After retraining with data from 20 countries and 7 continents, Stanford’s chest X-ray AI achieved 98.5% accuracy across all ethnic groups, up from 73% for underrepresented populations.

Minimum representation: 35% from each major demographic in training data
Global partnerships: Healthcare data sharing agreements with developing nations
Incentive programs: Financial rewards for hospitals contributing diverse datasets
Regulatory requirements: FDA mandate for demographic representation in AI training

3. Explainable AI (XAI) Standards

Mayo Clinic’s Breakthrough: Their explainable sepsis prediction AI provides detailed reasoning for each alert, reducing false positives by 61% while maintaining 99% sensitivity for actual sepsis cases.

Decision transparency: Every AI recommendation includes explanation
Confidence scoring: Uncertainty quantification for all predictions
Plain language: Patient-understandable reasoning for AI decisions
Audit trails: Complete decision history for regulatory review

4. Patient-Centered Governance Revolution

MIT’s Patient Advisory Success: Including 15 patient representatives in AI development led to 73% improvement in system usability and identification of 29 previously overlooked ethical concerns.

Governance Structure 2.0:

Equal representation: 50% patient voices on AI ethics committees
Community oversight: Local patient advocates reviewing AI deployments
Opt-out rights: Patients can refuse AI-driven care without penalty
Benefit sharing: Communities receive compensation for data contributions

We stand at a crossroads. With $613 billion flowing into healthcare AI by 2034, the decisions made today will determine whether artificial intelligence becomes medicine’s greatest equalizer or its most dangerous discriminator.

The technology exists to create AI systems that are both incredibly powerful and fundamentally fair. The economic incentives align with ethical imperatives. The regulatory framework is emerging. What’s missing is the collective will to demand better.

Healthcare AI bias isn’t a technical problem — it’s a choice. Every healthcare executive, policymaker, and technology leader must decide: Will they be part of the solution or part of the problem?

The algorithms making life-and-death decisions are being written today. The question isn’t whether AI will transform healthcare, it’s whether we’ll transform AI to serve everyone equally.

The future of ethical healthcare AI starts with companies like Medifakt leading by example. The future of your healthcare depends on supporting those who put patients before profits.