New Challenges in Healthcare Ethics: AI and Assisted Reproduction Technologies

Dear Colleagues,

Artificial Intelligence (AI) and Machine Learning (ML) are transforming various fields of medicine, including reproductive medicine. Their ability to analyze vast datasets, identify patterns, and support decision-making holds promise for improving outcomes, personalizing treatments, and increasing efficiency.

The potential benefits of AI and ML in reproductive medicine mainly have to do with enhanced diagnostic accuracy, since AI algorithms can assist in the assessment of ovarian reserve, sperm quality, and embryo viability with high precision. Imaging analysis, such as time-lapse microscopy of embryos, enables better selection of viable embryos for transfer. In addition, the possibility for personalized treatment plans should not be overlooked:

ML models can predict individual responses to ovarian stimulation, optimizing medication dosages and reducing risks like ovarian hyperstimulation syndrome. Tailored protocols can in fact improve success rates and patient safety, while AI-driven algorithms analyze morphological and developmental parameters to select embryos with the highest implantation potential, potentially increasing success rates of IVF. Moreover, automating tasks such as sperm analysis, embryo grading, and data management reduces human error and saves time. AI models can also forecast pregnancy success, miscarriage risks, and other outcomes, aiding clinicians and patients in decision-making.

Still, ethical and legal challenges are substantial as well: handling sensitive reproductive data raises concerns about patient privacy and informed consent for data use in AI systems. In addition, AI models trained on non-diverse datasets may perpetuate biases, leading to disparities in access and outcomes across different populations. Complex AI models may act as "black boxes," making it difficult for clinicians and patients to understand how decisions are made, potentially undermining trust. Then there is the potential impact on patient autonomy, since overreliance on AI recommendations might diminish patient involvement in decision-making processes. AI could also be used to select for non-medical traits, raising ethical questions about eugenics and designer babies.

Defining legal and regulatory standards to tackle such complexities and obtaining approval processes for AI tools in reproductive medicine is complex and evolving, as is determining responsibility in cases where AI-assisted decisions lead to adverse outcomes that are legally challenging. Clarifying who owns reproductive data and AI algorithms is critical, especially with cross-border data sharing. Ensuring AI systems adhere to regulations like GDPR or HIPAA is essential for legal compliance. Most importantly, patients need clear information about AI involvement in their care, including potential risks and limitations.

AI and ML hold transformative potential in reproductive medicine, promising improved accuracy, personalized care, and better outcomes. However, realizing these benefits requires careful navigation of ethical and legal challenges, including privacy, bias, transparency, and liability issues. Ongoing dialogue among clinicians, ethicists, legal experts, and patients is essential to develop responsible frameworks that maximize benefits while safeguarding rights and ethical standards.

For this Special Issue, we welcome original studies, qualitative, as well quantitative and mixed methods and (all kinds of) reviews.

We look forward to receiving your valuable contributions.

Dr. Susanna Marinelli
Prof. Dr. Robert Krysiak
Guest Editors

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• assisted reproductive technology (ART)
• artificial intelligence (AI)
• machine learning (ML)
• precision/personalized medicine
• ethical, legal and social implications (ELSI)