life sciences


AI's potential in the pharma life cycle - PMLiVE

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From the acceleration of regulatory submissions - by identifying data gaps that have led to delays or rejections in the past - to the transformation of the conduct of clinical trials and patient safety monitoring, artificial intelligence (AI) has substantial potential to change the way life sciences organisations operate. Back-end technology already exists to facilitate more intelligent and proactive health monitoring by taking things forward as drug companies rely on finding the optimum ways for patients to interact with and use the tools. There is also important safety monitoring potential and drug feedback potential, as long as intelligent tools based on AI and machine learning are in the background offering companies what to look for and ways of deciphering what it all means. As more and more companies identify opportunities to turn AI-enabled insights into timely and beneficial outcomes - whether by accelerating market entry, successfully mining social media for potential adverse events and other patient feedback, discovering new indications, or improving the manufacturing and supply chain process - advanced automation through increased machine intelligence looks set to be the way forward.


Cognitive health care in 2027

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The primary focus of these initiatives is on health care providers, helping them develop treatment approaches that are most effective for individual patients. One consortium of hospitals, researchers, and a startup, for example, is conducting "Project Survival" to identify effective biomarkers for pancreatic cancer.3 In other firms, real-world data sources are being used to identify molecules that might be particularly effective (or ineffective) in clinical trials. Another long-term challenge to be addressed by the life sciences and health care industry is collaboration and integration of data. Project Survival, for example--an effort to find a pancreatic cancer biomarker--involves collaboration among a big data drug development startup (Berg Health), an academic medical center (Beth Israel Deaconess in Boston), a nonprofit (Cancer Research and Biostatistics), and a network of oncology clinicians and researchers (the Pancreatic Research Team).


Why 500 Million People in China Are Talking to This AI

MIT Technology Review

Some also use it to send text messages through voice commands while driving, or to communicate with a speaker of another Chinese dialect. But while some impressive progress in voice recognition and instant translation has enabled Xu to talk with his Canadian tenant, language understanding and translation for machines remains an incredibly challenging task (see "AI's Language Problem"). In August, iFlytek launched a voice assistant for drivers called Xiaofeiyu (Little Flying Fish). Min Chu, the vice president of AISpeech, another Chinese company working on voice-based human-computer interaction technologies, says voice assistants for drivers are in some ways more promising than smart speakers and virtual assistants embedded in smartphones.


The Next Doctor You Consult Could Be a Robot: Healthcare Meets AI and the Blockchain -- Bitcoin Magazine

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On August 24, doc.ai announced that their advanced natural language processing technology platform, based on the blockchain, would timestamp datasets and decentralize artificial intelligence. The objective of the company is to help healthcare companies improve patient care and experience through an advanced natural dialogue system which will be able to generate insights from combined medical data. "We are making it possible for lab tests to converse directly with patients by leveraging advanced artificial intelligence, medical data forensics, and the decentralized blockchain. We are excited to collaborate with doc.ai and to be at the forefront of this technology," said Rajeev Ronanki, Principal of Life Sciences and Health Care at Deloitte Consulting LLP.


Data Science Developer at Institute of Data Science @ Maastricht University

@machinelearnbot

Develop data processing and system integration applications. Implement ETL processes, including data acquisition, data integration, data quality testing, data indexing and query answering. The university hosts 6 faculties: Faculty of Health, Medicine and Life Sciences, Faculty of Law, School of Business and Economics, Faculty of Humanities and Sciences, Faculty of Arts and Social Sciences, Faculty of Psychology and Neuroscience. IDS is an interfaculty institute consisting of a core team of data science experts that cooperate closely with researchers across disciplines such as medicine, life sciences, social sciences and humanities, business and economics, knowledge engineering and smart services.



Capital OneVoice: Apps With Smarts: The Story Behind Machine Learning

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Whether it's making your email smarter, streamlining tasks or solving the riddle of incurable diseases, AI and machine learning will probably have a huge impact in your life. Andrew Ng, former head of AI for Baidu, has said that machine learning and AI "Will also now change nearly every major industry--healthcare, transportation, entertainment, manufacturing." There's no doubt that a lot of people are starting to see how machine learning and AI might change their industries. Whether it's making your email smarter, streamlining tasks or solving the riddle of incurable diseases, AI and machine learning will probably have a huge impact in your life.


AI may just be the prescription for pharmaceutical's future

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In a recent Infosys multi-market research study, 'Amplifying Human Potential Toward Purposeful AI', pharmaceutical and life sciences emerged as the leader in the journey to AI maturity. It is AI-led technologies and analytics that enable the massive analysis of patient information, genetic profiling, and analysis of medical images, making medical advancements more accessible. And this translates into more good news: personalized medicine, more accurate diagnosis, and shortened regulatory approvals to make drugs available much faster. Recently, working to create a digital lab for a large pharmaceutical company, we shared the immense pleasure of lab chemists leveraging modern technology to automate processes, avoid errors and achieve near-zero rework.


How Medical Search Technology Relies on Google Alphabet and Big Data

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The November 2016 market research report "Artificial Intelligence Market by Technology (Deep Learning, Robotics, Digital Personal Assistant, Querying Method, Natural Language Processing, Context-Aware Processing), Offering, End-User Industry, and Geography – Global Forecast to 2022", reveals that the global artificial intelligence market is projected to be worth USD 16.06 Billion by 2022. The number of mobile search that uses voice recognition and relies on big data is growing, especially as Google AMP (Accelerated Mobile Pages) are rolled into the core of search. The National Center for Complementary and Integrative Health says, "The number of Web sites offering health-related resources--including information about complementary health approaches (often called complementary and alternative medicine)--grows every day." In healthcare, the President's Precision Medicine Initiative and the Cancer Moonshot will rely on AI to find patterns in medical data and, ultimately, to help doctors diagnose diseases and suggest treatments to improve patient care and health outcomes."


Top Uses For Machine Learning In Life Sciences

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Bio-pharmaceutical brands are critical intellectual property for life sciences companies, and marketing intelligence and insights are powerful ways to improve brand recognition and marketing ROI. There are a number of potential use cases for machine learning in life sciences. Smart business process enabled by machine learning, automation, and artificial intelligence can help achieve intelligent enterprise goals for the life science industry, particularly as the IoT technology adoption rate improves. SAP machine learning services in its SAP Leonardo IoT platform help life science companies automate and prioritize routine decision making processes in order to adapt to rapidly changing business environments.