Ethical issues of AI in healthcare and how to solve them

The healthcare industry is experiencing a major digital transformation, leading to more effective patient care delivery approaches. Artificial intelligence (AI), machine learning (ML), and other advanced technologies are gaining momentum, assisting healthcare providers in their work. However, some challenges and issues must be addressed. While offering significant potential to improve medical research, diagnosis, and treatment, and streamline healthcare processes, the ethics of AI in healthcare also raise important concerns that can affect patient safety, privacy, and autonomy.

Here are some of the common ethical concerns of AI in healthcare and practical tips to navigate them:

Bias

AI algorithms produce outcomes based on the data they are fed, which reflect human experiences and cultural and societal contexts. AI systems can reflect existing ethnic, racial, social, and demographic inequities, leading to decisions biased against underrepresented communities. In healthcare, inaccurate and unfair results produced by AI lead to incorrect diagnoses and inappropriate or even harmful treatment.

Effective practices for mitigating biased AI results include carefully selecting diverse training datasets, increasing representation, constantly monitoring and refining AI systems, and engaging individuals from varied backgrounds to validate data and outputs.

Informed consent

Patients may be hesitant to accept AI assistance in medical diagnostics, treatment, and healthcare services. Therefore, physicians must consider when and how to inform patients about AI usage and how much detail to provide regarding the technology’s complexities. A U.S. survey revealed that 60% of respondents would be uncomfortable with AI being used in their care, mainly due to uncertainty about whether AI leads to better outcomes and the influence of media on public perceptions of the technology.

Patients should be informed of both the benefits and risks of using AI in their care, enabling them to make an informed decision and allowing them to opt out. A transparent informed consent process builds trust with patients and alleviates doubts about AI’s capabilities.

Patient data privacy and security

Training, deploying, and improving AI models requires access to vast amounts of data. This includes protected health information (PHI)—personal patient data used to provide healthcare services. While PHI is a valuable resource for implementing AI algorithms, sensitive information is also vulnerable to threats in terms of confidentiality and cyber security. Cyberattacks, data breaches, and unauthorized access lead to compromised patient safety, legal complications, financial losses, and other harmful consequences.

Data privacy concerns can be mitigated by following some effective practices. When training AI, datasets should be anonymized to safeguard patient privacy. In clinical research and trials, healthcare companies should limit AI systems to internal use or generate synthetic data to train AI models. Protecting data integrity also requires creating, updating, and observing security protocols and procedures in compliance with global and local regulations, such as GDPR and HIPAA. It is also important to ensure transparency in how data is collected, stored, and used, including its scope and purpose.

Emotional aspect

Patient care is based on human-to-human interactions, with physicians providing care with empathy and sympathy. A technological shift introducing AI-powered tools for diagnostics and treatment can impact patients’ emotions, causing stress and anxiety.

In healthcare delivery, empathetic emotional connection is irreplaceable. To achieve positive outcomes, humans and AI should collaborate, enabling healthcare providers to maintain high efficiency while fostering trust-based relationships with patients.

Despite the positive changes AI brings to healthcare, including improved patient care delivery, skepticism toward the technology remains prevalent. Only 38% of surveyed Americans believe that using AI will lead to better outcomes. To change public sentiment toward AI, healthcare providers must tackle the ethical challenges it poses. Safe and transparent use of AI should also be regulated at the legislative level, with many countries already working toward this goal. In August 2024, the EU Artificial Intelligence Act (AI Act) went into effect, establishing rules for the development and deployment of AI systems across different domains within Europe. For the healthcare industry, this act establishes a risk-based framework for regulating AI-powered digital medical products, ensuring their reliability and protecting patients’ rights.

Healthcare organizations must prioritize controlling how AI models are trained, ensuring representation in data, complying with information security standards and legislative acts, and educating patients on the benefits of AI assistance.

The team at EffectiveSoft helps healthcare companies navigate the world of AI while prioritizing ethics and addressing critical challenges, such as bias, data privacy, and patient safety. Contact our experts to bring ethical AI to your healthcare project.