According to data from the World Health Organization, about 15 percent of couples of childbearing age experience difficulties in natural conception, with the rising trend linked to social, environmental and health factors.
The use of medically assisted procreation (MAP) techniques has grown exponentially in recent decades, but the structural limitations of these treatments remain evident. The average success rate per in vitro fertilization (IVF) cycle still hovers between 25 percent and 30 percent, despite technological advances and accumulated clinical experience. Each unsuccessful attempt carries not only significant economic costs but also a significant emotional impact on patients.
One of the main obstacles lies in the inherent complexity of treatment pathways: each patient represents a unique case, characterized by biological, genetic, clinical and behavioral variables that are difficult to standardize. Managing this data-heterogeneous and constantly updated-requires tools capable of processing complex information and returning personalized indications.
Predictive algorithms applied to fertility are capable of analyzing large volumes of clinical data, identifying hidden patterns, and suggesting optimized therapeutic strategies. Early concrete applications in assisted reproduction centers have already shown significant results: artificial intelligence-based systems have achieved predictive accuracy of up to 73-75% in selecting embryos most likely to implant, and personalized protocols supported by AI models have made it possible to reduce the risk of complications related to ovarian hyperstimulation by up to 30%.
The contribution of AI does not just improve clinical performance, but intervenes profoundly in the quality of care and personalization of treatments, opening up a new paradigm of reproductive medicine based on data, precision, and innovation.
Artificial Intelligence as a Clinical Decision Support System (CDSS) in Infertility Pathways
Infertility is a condition that requires extreme precision in clinical management. Each medically assisted procreation treatment is characterized by many variables: ovarian reserve, drug response, oocyte and embryo quality, genetic and environmental factors. Artificial intelligence fits into this context as an operational tool of precision medicine, intervening at the most delicate decision-making moments.
AI-based Clinical Decision Support Systems (CDSS) do not just automate procedures; they are engines for personalizing therapies. Through predictive models and machine learning, these systems rapidly process large amounts of individual clinical data and return personalized suggestions for:
- The dosage of drugs in ovarian stimulation;
- The choice of hormone support protocols;
- The identification of the optimal time for oocyte pick-up;
- The selection of embryos with the greatest evolutionary potential.
A distinctive feature of these tools is their ability to significantly reduce the subjective component of some phases of reproductive medicine, which have traditionally relied on individual experience and intuition. Advanced image analysis systems, for example, allow objective and standardized analysis of morphological and dynamic characteristics of embryos, thus helping to limit variations in judgment between operators and clinical centers.
This is accompanied by the evolution of Large Language Models (LLMs), which enable CDSSs to read and understand even complex clinical documentation, medical histories, electronic medical records, and up-to-date scientific studies. Their implementation in fertilization centers enables the integration of not only structured data (numerical or laboratory), but also narrative and textual data, completing the overall view of the patient.
The result of this transformation is twofold: on the one hand, the physician is supported in making more informed, data-driven clinical choices; on the other hand, the patient receives a more targeted, potentially more effective treatment with fewer side effects.
Artificial intelligence, in this scenario, emerges not as an alternative to the physician, but as an extension of his or her decision-making capabilities, amplifying his or her skills through computational power and predictive analytics.
Conceptual map of the main applications of Artificial Intelligence as a clinical decision support system (CDSS) in infertility pathways.
Data Integration: An Organizational and Management Priority.
Without a complete and accurate information base, even the best predictive models are ineffective or even misleading.
The first major qualitative leap required of reproductive medicine centers concerns the ability to collect, organize, and integrate complex clinical data in a systematic way. Individual medical expertise, however fundamental, is no longer sufficient in the face of the increasing complexity of patients' reproductive profiles.
These processes generate enormous amounts of information: hormonal examinations, ultrasound features, ovarian stimulation progress, genetic parameters, embryonic quality, and past medical history. To these are added behavioral and environmental data-such as lifestyle, stress level, sleep quality, or diet-that can significantly influence the response to treatments.
All these data, to be useful, must be:
- collected in a homogeneous and standardized way;
- stored in interoperable and secure systems;
- easily accessible for processing and analysis.
This is where a decisive part of the future quality of infertility treatments is at stake. The centers that are able to build integrated digital ecosystems capable of collecting structured and unstructured data throughout the treatment pathway will be the ones able to truly harness the potential of artificial intelligence.
Thus, multidimensional data analysis is not a technological "plus," but a prerequisite for ensuring personalized, predictive, and precision reproductive medicine.
In addition to its strictly clinical purpose, this advanced information management capability also produces obvious operational benefits: reduction of human errors, increased traceability of care pathways, possibility of audit and verification of results, and improved decision-making and communication processes within multidisciplinary teams.
Ultimately, there is no effective artificial intelligence without a robust and widespread culture of data. And in reproductive medicine-perhaps more than in other fields-data is not just technical information, but the key to interpreting the biological and human complexity of each patient.
Bias and Ethical Risks in Artificial Intelligence Models Applied to Fertility
The real effectiveness of these technologies and its clinical acceptability depend on the ability to responsibly manage the ethical risks and structural limitations that accompany these tools.
Among the main critical elements is the problem of algorithmic bias: artificial intelligence models are not independently unbiased. The quality of their predictions depends on the quality of the data used to train them. If these data do not reflect the true diversity of populations, the results produced are likely to be biased or unreliable for all patients.
This phenomenon takes on particular relevance in medically assisted reproductive pathways, where much of the available datasets are derived from patients belonging to well-defined geographic and genetic populations-mostly of Caucasian origin and from European or North American clinical settings. Indiscriminate application of these models to patients of other ethnicities or with different biological characteristics could result in less accurate predictions, irrelevant treatment suggestions, or even clinical errors.
In addition to the risk of bias, there are other relevant ethical issues related to the use of AI in reproductive medicine. The transparency of algorithms-often referred to as "black box"-is a crucial issue: many of the predictive systems currently in use do not allow for a full understanding of how the recommendations provided to clinicians are developed. This opacity can reduce patient trust and complicate medico-legal liability in the event of disputes.
Reproductive data management, moreover, requires particularly high security standards. The health information handled in fertility pathways is among the most sensitive of all, because it involves aspects related to the intimate, genetic and family spheres of patients. The protection of these data and their proper management is an unavoidable ethical and regulatory obligation.
At the regulatory level, the European Union has already embarked on a path of specific regulation on the use of AI in healthcare, with the goal of establishing common standards of safety, transparency and respect for patients' rights.
Toward Data-Driven Reproductive Medicine: Conclusions
Today, at least 11 Clinical Decision Support System (CDSS) algorithms applicable to clinical and laboratory management of medically assisted procreation pathways have already been identified. These tools, developed to support decisions in areas such as ovarian stimulation, embryo evaluation, and drug dosage management, represent state-of-the-art artificial intelligence applications in infertility treatments.
However, available evidence suggests that the widespread and safe adoption of these models will still require major steps forward. In particular, the systematic review of major international studies points to the presence of highly heterogeneous validation methods, with non-standardized verification protocols that are not comparable with each other.
The most mature end point of this transformation is not only technological, but first and foremost organizational and cultural. Artificial intelligence will be able to reach its full potential in reproductive medicine only when digital ecosystems are built that are truly capable of collecting, managing, integrating, and exploiting complex data in a systematic and secure way.
Bibliographical References
Artificial Intelligence, Clinical Decision Support Algorithms, Mathematical Models, Calculators Applications in Infertility: Systematic Review and Hands-On Digital Applications
Carlo Bulletti, MD; Jason M. Franasiak, MD; Andrea Busnelli, MD; Romualdo Sciorio, BSc, Msc; Marco Berrettini, PhD; Lusine Aghajanova, MD, PhD; Francesco M. Bulletti, MD; and Baris Ata, MD
Transforming Health Care With Artificial Intelligence: Redefining Medical Documentation
Archana Reddy Bongurala, MD; Dhaval Save, MD; Ankit Virmani, MSc; and Rahul Kashyap, MBBS
Transforming Large Language Models into Superior Clinical Decision Support Tools by Embedding Clinical Practice Guidelines.
Yanshan Wang, PhDa,b,c,d; Xiaoxi Yao, PhD, MPHe,f; and Xizhi Wua- Car J, Sheikh A, Wicks P, Williams MS.
https://doi.org/10.1016/j.mcpdig.2024.05.018
How are Machine Learning and Artificial Intelligence Used in Digital Behavior Change Interventions? A Scoping Review
Amy Bucher, PhD; E. Susanne Blazek, PhD; and Christopher T. Symons, PhD
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