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Biotech startup Gero grabs €1.9M to hack ageing and COVID-19

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Based out of Singapore, Gero develops new drugs for ageing and other complicated disorders using its proprietary developed artificial intelligence (AI) platform. Recently, the company has secured $2.2 million (€1.9 million) in Series A funding, bringing the total capital raised since Gero's founding to over $7.5 million (€6.4 million). Gero's founder Peter Fedichev, said, "We are happy with the recognition and support from these strategic investors who themselves are acknowledged leaders in the fields of AI and biotechnology. This will help us attain the necessary knowledge at the junction of biological sciences and AI/ML technologies that is necessary for the radical acceleration of drug discovery battling the toughest medical challenges of the 21st century. We hope that the technology will soon lead to a meaningful healthspan extension and quality of life improvements " The round was led by Bulba Ventures with participation from previous investors and serial entrepreneurs in the fields of pharmaceuticals, IT, and AI.


3 Ways Artificial Intelligence Will Change Healthcare

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It's no secret that healthcare costs have risen faster than inflation for decades. Some experts estimate that healthcare will account for over 20% of the US GDP by 2025. Meanwhile, doctors are working harder than ever before to treat patients as the U.S. physician shortage continues to grow. Many medical professionals have their schedules packed so tightly that much of the human element which motivated their pursuit of medicine in the first place is reduced. In healthcare, artificial intelligence (AI) can seem intimidating.


3 Ways Artificial Intelligence Will Change Healthcare

#artificialintelligence

It's no secret that healthcare costs have risen faster than inflation for decades. Some experts estimate that healthcare will account for over 20% of the US GDP by 2025. Meanwhile, doctors are working harder than ever before to treat patients as the U.S. physician shortage continues to grow. Many medical professionals have their schedules packed so tightly that much of the human element which motivated their pursuit of medicine in the first place is reduced. In healthcare, artificial intelligence (AI) can seem intimidating.


GPT-3 Creative Fiction

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What if I told a story here, how would that story start?" Thus, the summarization prompt: "My second grader asked me what this passage means: …" When a given prompt isn't working and GPT-3 keeps pivoting into other modes of completion, that may mean that one hasn't constrained it enough by imitating a correct output, and one needs to go further; writing the first few words or sentence of the target output may be necessary.


Machine Learning and AI -- Reshaping the Healthcare Industry

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The Healthcare industry is currently experiencing a massive digital transformation. There has been stable progress since the introduction of technology in this sector. Tech components like Machine Learning, Natural Language Processing (NLP), cloud computing, Artificial Intelligence, and the Internet of Things have helped in streamlining health care delivery and treating severe diseases. AI refers to multiple technologies that can be combined in different ways to sense, comprehend, and act with the ability to learn from experience and adapt over time. In basic terms, AI is a broad area of computer science that makes machines and computer programs capable of problem-solving and learning, like a human brain.


12 Real-World Applications of Machine Learning in Healthcare

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According to news, Machine Learning is one of the most prominent technology for the future of the Healthcare industry. Is there any significant value, or is it just optimistic forecasts? In this article, you will learn on some practical implementations of the technology, as well as some on-point predictions. Today, technology-enabled healthcare is a reality as smart medical devices become a widespread thing. The healthcare industry welcomes the innovation; that's why the future of AI in healthcare is very bright.


New effort to map brain tumors puts an AI approach to the test

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It's a contradiction that's long slowed the forward march of artificial intelligence in medicine: Machine learning models need to be trained on lots of diverse data from hospitals around the world -- but those hospitals are often reluctant to ship out their data due to privacy concerns, legal issues, and a cautious culture. One promising way to get around that problem is a technique known as federated learning, which allows models to be trained without having to share data to a central server or in the cloud. Now, the approach is being put to the test in an ambitious project to build an AI system from thousands of brain tumor scans from several dozen hospitals and research institutions around the globe. Unlock this article by subscribing to STAT Plus and enjoy your first 30 days free! STAT Plus is STAT's premium subscription service for in-depth biotech, pharma, policy, and life science coverage and analysis.


Finding unique drug structures with artificial intelligence and chemistry

AIHub

In the search for new medicines for diseases such as cancer, a Leiden team has developed a new workflow. This approach combines artificial intelligence (AI) with molecular modelling and is suitable for finding unknown and innovative drug structures, the researchers demonstrated. With their new method, the researchers of the Leiden Academic Centre for Drug Research (LACDR) and the Leiden Institute of Advanced Computer Science (LIACS) were able to find five substances with an inhibitory effect on a specific type of kinase. Kinases are enzymes that switch other proteins on or off and play an important role in the development of cancer. In their publication in the Journal of Chemical Information and Modeling, the team looked at so-called polypharmacology – drug development in which there are multiple targets in the body.


Digital Pathology and Artificial Intelligence

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The last few years have seen digital pathology emerge as a vital technology for laboratories around the world, facilitating the sharing of data, supporting the faster and more accurate analysis of tissue samples, and enabling the faster development of new therapeutic techniques. It is being seen as facilitating the progression of medicine, as well as supporting the emerging field of precision medicine. Digital pathology involves imaging tissue samples, creating digital copies of these images, and sharing them in an online platform so they can be used in research and to inform medical cases around the world. The idea itself is old, but the technology required to support it has only become recently available. Therefore, recent years have seen the rapid widespread adoption of digital pathology as technology has advanced.


AI and Machine Learning for Healthcare - KDnuggets

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The 21st century is only two decades old and it is certain that one of the biggest transformative technologies and enablers for human society of this century is going to be Artificial intelligence (AI). It is a well-established idea that AI and associated services and platforms are set to transform global productivity, working patterns, and lifestyles and create enormous wealth. For example, McKinsey sees it delivering global economic activity of around $13 trillion by 2030. In the short-term, research firm Gartner expects the global AI-based economic activity to increase from about $1.2 trillion in 2018 to about $3.9 Trillion by 2022. It is no secret that this transformation is being, to a large extent, fueled by the powerful Machine Learning (ML) tools and techniques such as Deep Convolutional Networks, Generative Adversarial Networks (GAN), Gradient-boosted-tree models (GBM), Deep Reinforcement Learning (DRL), etc.