Sutton et al., 2022], is elegantly captured using the options framework [Sutton et al., 1999].

Large language models (LLMs) are increasingly adopted in clinical decision support, yet aligning them with the multifaceted reasoning pathways of real-world medicine remains a major challenge. Using more than 8,000 infertility treatment records, we systematically evaluate four alignment strategies: Supervised Fine-Tuning (SFT), Direct Preference Optimization (DPO), Group Relative Policy Optimization (GRPO), and In-Context Learning (ICL) through a dual-layer framework combining automatic benchmarks with blinded doctor-in-the-loop assessments. GRPO achieves the highest algorithmic accuracy across multiple decision layers, confirming the value of reinforcement-based optimization for structured prediction tasks. However, clinicians consistently prefer the SFT model, citing clearer reasoning processes (p = 0.035) and higher therapeutic feasibility (p = 0.019). In blinded pairwise comparisons, SFT attains the highest winning rate (51.2%), outperforming both GRPO (26.2%) and even physicians' original decisions (22.7%). These results reveal an alignment paradox: algorithmic improvements do not necessarily translate into higher clinical trust, and may diverge from human-centered preferences. Our findings highlight the need for alignment strategies that prioritize clinically interpretable and practically feasible reasoning, rather than solely optimizing decision-level accuracy.

Sutton et al., 2022], is elegantly captured using the options framework [Sutton et al., 1999].

In vitro fertilization (IVF) is a widely utilized assisted reproductive technology, yet predicting its success remains challenging due to the multifaceted interplay of clinical, demographic, and procedural factors. This study develops a robust artificial intelligence (AI) pipeline aimed at predicting live birth outcomes in IVF treatments. The pipeline uses anonymized data from 2010 to 2018, obtained from the Human Fertilization and Embryology Authority (HFEA). We evaluated the prediction performance of live birth success as a binary outcome (success/failure) by integrating different feature selection methods, such as principal component analysis (PCA) and particle swarm optimization (PSO), with different traditional machine learning-based classifiers including random forest (RF) and decision tree, as well as deep learning-based classifiers including custom transformer-based model and a tab transformer model with an attention mechanism. Our research demonstrated that the best performance was achieved by combining PSO for feature selection with the TabTransformer-based deep learning model, yielding an accuracy of 99.50% and an AUC of 99.96%, highlighting its significant performance to predict live births. This study establishes a highly accurate AI pipeline for predicting live birth outcomes in IVF, demonstrating its potential to enhance personalized fertility treatments.

Vector databases manage large collections of embedding vectors. As AI applications are growing rapidly, so are the number of embeddings that need to be stored and indexed. The Faiss library is dedicated to vector similarity search, a core functionality of vector databases. Faiss is a toolkit of indexing methods and related primitives used to search, cluster, compress and transform vectors. This paper first describes the tradeoff space of vector search, then the design principles of Faiss in terms of structure, approach to optimization and interfacing. We benchmark key features of the library and discuss a few selected applications to highlight its broad applicability.

When Dina Radenkovic, CEO of Gameto, a startup engineering stem cells to craft a lightweight version of IVF, injected herself with a needle loaded with hormones last December, she wasn't trying to get pregnant. Instead, she'd signed up for her own company's study of how to "mature" human eggs in a lab dish instead of inside their bodies. Gameto is among a group of startups trying to simplify the IVF process, as well as getting it to fit into women's busy schedules more easily. But experts say its technology still has some way to go before it can be embraced more widely. AI consciousness isn't just a devilishly tricky intellectual puzzle; it's a morally weighty problem with potentially dire consequences that philosophers, cognitive scientists, and engineers alike are currently grappling with.

Fox News contributor Dr. Marc Siegel weighs in on how artificial intelligence can change the patient-doctor relationship on'America's Newsroom.' AI's latest product – Remini – allows users to upload photos of themselves and their partner to generate images of what their future child could look like. There are two sides to this. First, the app lets people envision themselves as parents – potentially encouraging people to pursue, rather than delay, parenthood. As one woman said, "I can actually see myself being [pregnant] at some point."

Infertility is a common reproductive health problem that affects millions of people worldwide, causing social, psychological, physical and economic distress to the ones seeking to conceive [7]. In the coming years infertility rates are projected to grow due to environmental and lifestyle factors [18, 37]. In vitro fertilization (IVF) technology is used to overcome infertility, it involves the fertilization of an egg with sperm in the laboratory, followed by the transfer of the resulting embryos into the patient's uterus. The main challenge of IVF is the selection of the embryos that will be either selected for implantation, frozen (for later implantation) or discarded (if they exhibit undesirable features). This selection is to be performed during the early hours after embryo insemination, typically between three and five days after. During this time, embryos are monitored in time-lapse imaging incubators (TLI), facilitating uninterrupted embryo growth within stable culture conditions. This technology offers a dynamic perspective on in vitro embryonic development, augmenting the clinical effectiveness of IVF [29]. To assess quality, embryologists evaluate different morphological characteristics depending on the embryo development phase.

The main goal of automating IVF, say entrepreneurs, is simple: it's to make a lot more babies. About 500,000 children are born through IVF globally each year, but most people who need help having kids don't have access to fertility medicine or can't pay for it. "How do we go from half a million babies a year to 30 million?'" wonders David Sable, a former fertility doctor who now runs an investment fund. "You can't if you run each lab like a bespoke, artisanal kitchen, and that is the challenge facing IVF. It's been 40 years of outstanding science and really mediocre systems engineering."

One in four couples in developing countries is impacted by infertility. About 48.5 million couples experience infertility worldwide. Today, infertility is rapidly becoming an epidemic. In vitro fertilization (IVF) is a technique that helps people facing fertility problems have a baby. Despite IVF's potential, the outcomes are unpredictable. To make matters worse, access to fertility care is abysmal.