A great deal of effort has been devoted to discovering a particular genetic disorder, but its classification across a broad spectrum of disorder classes and types remains elusive. Early diagnosis of genetic disorders enables timely interventions and improves outcomes. This study implements machine learning models using basic clinical indicators measurable at birth or infancy to enable diagnosis in preliminary life stages. Supervised learning algorithms were implemented on a dataset of 22083 instances with 42 features like family history, newborn metrics, and basic lab tests. Extensive hyperparameter tuning, feature engineering, and selection were undertaken. Two multi-class classifiers were developed: one for predicting disorder classes (mitochondrial, multifactorial, and single-gene) and one for subtypes (9 disorders). Performance was evaluated using accuracy, precision, recall, and the F1-score. The CatBoost classifier achieved the highest accuracy of 77% for predicting genetic disorder classes. For subtypes, SVM attained a maximum accuracy of 80%. The study demonstrates the feasibility of using basic clinical data in machine learning models for early categorization and diagnosis across various genetic disorders. Applying ML with basic clinical indicators can enable timely interventions once validated on larger datasets. It is necessary to conduct further studies to improve model performance on this dataset.

We propose MMLU-SR, a novel dataset designed to measure the true comprehension abilities of Large Language Models (LLMs) by challenging their performance in question-answering tasks with modified terms. We reasoned that an agent that ``truly'' understands a concept can still evaluate it when key terms are replaced by suitably defined alternate terms, and sought to differentiate such comprehension from mere text replacement. In our study, we modified standardized test questions by replacing a key term with a dummy word along with its definition. The key term could be in the context of questions, answers, or both questions and answers. Notwithstanding the high scores achieved by recent popular LLMs on the MMLU leaderboard, we found a substantial reduction in model performance after such replacement, suggesting poor comprehension. This new benchmark provides a rigorous benchmark for testing true model comprehension, and poses a challenge to the broader scientific community.

Individuals with suspected rare genetic disorders often undergo multiple clinical evaluations, imaging studies, laboratory tests and genetic tests, to find a possible answer over a prolonged period of multiple years. Addressing this diagnostic odyssey thus have substantial clinical, psychosocial, and economic benefits. Many rare genetic diseases have distinctive facial features, which can be used by artificial intelligence algorithms to facilitate clinical diagnosis, in prioritizing candidate diseases to be further examined by lab tests or genetic assays, or in helping the phenotype-driven reinterpretation of genome/exome sequencing data. However, existing methods using frontal facial photo were built on conventional Convolutional Neural Networks (CNNs), rely exclusively on facial images, and cannot capture non-facial phenotypic traits and demographic information essential for guiding accurate diagnoses. Here we introduce GestaltMML, a multimodal machine learning (MML) approach solely based on the Transformer architecture. It integrates the facial images, demographic information (age, sex, ethnicity), and clinical notes of patients to improve prediction accuracy. Furthermore, we also introduce GestaltGPT, a GPT-based methodology with few-short learning capacities that exclusively harnesses textual inputs using a range of large language models (LLMs) including Llama 2, GPT-J and Falcon. We evaluated these methods on a diverse range of datasets, including 449 diseases from the GestaltMatcher Database, several in-house datasets on Beckwith-Wiedemann syndrome, Sotos syndrome, NAA10-related syndrome (neurodevelopmental syndrome) and others. Our results suggest that GestaltMML/GestaltGPT effectively incorporate multiple modalities of data, greatly narrow down candidate genetic diagnosis of rare diseases, and may facilitate the reinterpretation of genome/exome sequencing data.

Artificial intelligence (AI) is revolutionizing genomic medicine by providing better health outcomes. Genomic diagnostic is an area that can benefit hugely from the capabilities of AI. The involvement of AI in healthcare can potentially be beneficial in genetic diagnostics. Genomic medicine is an emerging medical discipline that involves using genomic information about an individual as part of their clinical care (e.g. for diagnostic or therapeutic decision-making) and the health outcomes and policy implications of that clinical use. Rare diseases are fairly common in the world, with nearly half a billion people suffering from some or the other kind of lesser-known ailments.

In 2012, Moti Shniberg sold his face recognition startup to Facebook and started looking for a new challenge. "We wanted to take our expertise and do something good," he says. Then he met the head of a medical genetics center, who explained the difficulty of diagnosing rare genetic disorders in children. Specialists sometimes use the shape and appearance of a child's face as a clue because some conditions, such as Down syndrome, give a child's face a distinctive appearance. For many other diseases, however, the signs are more subtle, and the cases very rare.

The healthcare industry is increasingly focusing on niche patient populations. Around half of FDA approvals in the past two years were for rare or orphan drugs that serve fewer than 200,000 patients in total in the US and 1 in 2,000 patients in Europe. By 2024, orphan drug sales are expected to capture one-fifth of worldwide prescription sales. However, finding these hard-to-reach patients is difficult and keeping them engaged over time even more so. Could machine learning platforms that deliver personalized experiences for patients and caregivers be part of the answer?

Ours is a technologically advanced world. We are living in an age where rapid signs of progress in technology are taking the world by storm. One of such many new inventions is what we are going to talk about here. The artificial intelligence (AI) has been in use now in different forms and in different locations. However, scholars in China appear to have invented a new creative way of using AI.

Using large healthcare encounter datasets, a machine learning algorithm is able to identify patients with a common genetic disorder that carries a high risk for early heart attacks and strokes. While individuals with familial hypercholesterolaemia (FH) have 20 times the risk of developing cardiovascular disease than the general population, fewer than 10 percent of the 1.3 million Americans born with the genetic disease are diagnosed. "People born with familial hypercholesterolemia develop cardiovascular damage by puberty, often culminating in early heart attacks or the need for surgery as young or middle-aged adults," says Katherine Wilemon, founder and CEO of the FH Foundation, a non-profit research and advocacy organization. "Since diagnosis of this deadly but treatable condition has stalled in the American medical system, the FH Foundation harnessed artificial intelligence and big data to accelerate identification of those most likely to have FH." In a new study, a machine learning model created by the FH Foundation successfully leveraged healthcare encounter databases to identify individuals with the genetic disorder.

As artificial intelligence becomes a standard laboratory tool, scientists are quickly discovering both the promise and perils of algorithmically driven research. Artificial intelligence (AI) is cropping up everywhere these days, according to major news sources that are themselves increasingly driven by computer algorithms. Marketers use AI to target advertisements, engineers use it to anticipate device failures, and AI-driven social media platforms wield outsize influence on everything from fashion to politics. While all types of AI--also called machine learning--entail programming a computer to learn from examples and make inferences, practitioners distinguish different forms of it. Within the broader field of AI, a subset of strategies employ artificial neural networks. These mimic biological brains, with elements of a program connecting to each other like neurons.

Facial recognition can help unlock your phone. Could it also be able to play a far more valuable role in people's lives by identifying whether or not a person has a rare genetic disorder, based exclusively on their facial features? DeepGestalt, an artificial intelligence built by the Boston-based tech company FDNA, suggests that the answer is a resounding "yes." The algorithm is already being used by leading geneticists at more than 2,000 sites in upward of 130 countries around the world. In a new study, published in the journal Nature Medicine, researchers show how the algorithm was able to outperform clinicians when it came to identifying diseases.