AI News, Pondering the Ethics of Artificial Intelligence in Health Care artificial intelligence

Ultra-modern medicine: Examples of machine learning in healthcare

in the use of genetic information “within care management and precision medicine to uncover the best possible medical treatment plans.” “AI will affect physicians and hospitals, as it will play a key role in clinical decision support, enabling earlier identification of disease, and tailored treatment plans to ensure optimal outcomes,” Paruk explained.

Location: Alpharetta, Georgia How it's using machine learning in healthcare: Ciox Health uses machine learning to enhance 'health information management and exchange of health information,' with the goal of modernizing workflows, facilitating access to clinical data and improving the accuracy and flow of health information.  Industry impact: The company recently partnered with Chicago-based Northwestern Memorial Healthcare 'to bring efficiency and transparency to Northwestern Memorial’s release of information (ROI) process.'

View Jobs + Learn More Location: Redmond, Washington How it’s using machine learning in healthcare: Microsoft's Project InnerEye employs machine learning to differentiate between tumors and healthy anatomy using 3D radiological images that assist medical experts in radiotherapy and surgical planning, among other things.  Industry impact: InnerEye is used in the United Kingdom to produce 3D imaging that pinpoints the precise location of tumors and enables more accurately targeted radiotherapy.

Location: New York, New York How it’s using machine learning in healthcare: With the help of IBM’s Watson AI technology, Pfizer uses machine learning for immuno-oncology research about how the body’s immune system can fight cancer.  Industry impact: According to fiercebiotech.com, Pfizer expanded its collaboration with Chinese tech startup XtalPi “to develop an artificial intelligence-powered platform to model small-molecule drugs as part of its discovery and development efforts.The project will combine quantum mechanics and machine learning to help predict the pharmaceutical properties of a broad range of molecular compounds.”  

Location: Boston, Massachusetts How it’s using machine learning in healthcare: Via its machine learning platform Augusta, Biosymetrics “enables customers to perform automated ML and data pre-processing,” which improves accuracy and eliminates a time-consuming task that’s typically done by humans in different sectors of the healthcare realm, including biopharmaceuticals, precision medicine, technology, hospitals and health systems.

Location: New York, New York How it’s using machine learning in healthcare: Concerto Health AI uses machine learning to analyze oncology data, providing insights that allow oncologists, pharmaceutical companies, payers and providers to practice precision medicine and health.  Industry impact: Its recently launched platform, Eureka Health Oncology, uses deep data from electronic medical records to offer AI solutions for the management, delivery and use of clinical data.

With an assist from machine learning, Prognos’s AI platform facilitates early disease detection, pinpoints therapy requirements, highlights opportunities for clinical trials, notes gaps in care and other factors for a number of conditions.                Industry impact: Last year Prognos reportedly raised $20.5 million in a Series C funding round.

The future of artificial intelligence in health care

From patient self-service to chat bots, computer-aided detection (CAD) systems for diagnosis, and image data analysis to identify candidate molecules in drug discovery, AI is already at work increasing convenience and efficiency, reducing costs and errors, and generally making it easier for more patients to receive the health care they need.

Ethics In AI: Why Values For Data Matter

Many analysts have projected that by 2020, around 70% of the data that a company uses will come from Internet of Things (IoT) enabled devices and external data streams – in other words, external, non-transactional sources.

It is a blessing — for example whenAI and Predictive analytics are using big data to monitor growing conditions, to help an individual farmermake everyday decisions that can determine if they will be able to feed their family (or not).

Companies should ask themselves: “Is data the core asset that I monetize?” or “Is data the glue that connects the processes that have made my products or services successful?” This is especially urgent as companies start to use third-party data sources to train their algorithms — data about which they know relatively little.

This question becomes more and more important when artificial intelligence is applied in fully automated processes that judge eligibility for a clinical trial, performs resume selection, or evaluates applications for mortgages.

In other cases, this could vary between a simple “what’s the logic behind this mortgage application approval or rejection and a more prominent “show me what happened under the hood in providing this answer.” Yet, that’s not enough.

will close by asking one question to ponder when thinking about how to treat data as an asset in your organization: “How will machines know what we value if we don’t articulate (and own) what we value ourselves?” * Dig deeper: Want to hear more on ethics in AI, transparency, and treating data as an asset?Watch Marc’s recent masterclass at Web Summit 2019 here.

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