Amazon Web Services (AWS) is partnering with Deloitte to help customers securely find, subscribe to, and use third-party data in the cloud using AWS Data Exchange, a new service that can help address unique healthcare issues.

The partnership will build on the existing relationship between the two organizations and transform biomedical research, clinical trials, real-world data insights, population health, and reimbursement.

Most digital health data generated by patients, researchers, health systems, and payers is inaccessible to other organizations for multiple reasons, from security concerns to technology constraints and business model challenges. This means healthcare organizations aren’t fully leveraging the benefits of this data, slowing the pace of medical innovation and limiting the potential to improve care delivery.

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“The explosion of digital healthcare data and advances in artificial intelligence (AI) and machine learning (ML) hold the promise to answer some of healthcare’s most important questions — what’s working for whom, why and at what cost,” said Brett Davis, principal, Deloitte Consulting LLP, and general manager, ConvergeHEALTH by Deloitte.

“However, this data is locked in siloes across many organizations within the health ecosystem. The industry needs new business models that break down these silos to connect healthcare stakeholders, reduce inefficiencies and accelerate the translation of research to practice and improve patient outcomes. AWS Data Exchange provides the technology and infrastructure to support these new business models.”

AWS Data Exchange creates a secure, scalable data exchange infrastructure under terms and conditions that data owners can control. AWS Data Exchange also allows customers to benefit from third-party software, artificial intelligence algorithms, and professional services.

Deloitte’s ConvergeHEALTH Miner integration with AWS Data Exchange means users of Miner can easily find, access, and analyze aggregated and de-identified data from collaborators outside their organizations. This could transform the way organizations conduct research, clinical trials, population health management, and reimbursement.

The partnership will help life sciences and healthcare organizations use more data to perform better research and improve patient outcomes.

“AWS Data Exchange provides a secure and cloud-native channel to exchange data at scale, with cloud-based solutions like ConvergeHEALTH Miner supporting the analytical needs for data analysts, academic researchers and data scientists in the medical community,” said Stephen Orban, general manager, AWS Data Exchange, Amazon Web Services, Inc.

“We’re delighted to be working with Deloitte on AWS Data Exchange to help life sciences and healthcare organizations establish their strategies around the new data-driven collaborations and business models, while also helping data publishers with the engineering tasks needed to package, aggregate and anonymize their data for collaboration.”

This partnership adds to Amazon’s efforts to enhance healthcare using data. In November 2018, the company announced a new machine learning service that can extract meaningful information from unstructured EHR data and free-text clinical notes. Called Amazon Comprehend Medical, the service allows developers to comb through unstructured EHR data and pull out key clinical terms related to a patient’s diagnosis.

Deloitte has also highlighted the importance of data and analytics in the healthcare industry. In a 2019 survey, the organization emphasized the need for organizations to invest in innovative data technologies.

“Heading into the future, data analytics is one of the backbones for health systems seeking to use emerging technologies such as artificial intelligence (AI) and robotic process automation (RPA) to transform their care delivery or workforce,” the report stated.

“It is also important for strategies and initiatives that depend on data mining—such as understanding social determinants of health and the importance of customer experience and preferences.”

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Carnegie Mellon University Libraries recently hosted a conversation on harnessing the power of artificial intelligence for scientific data discovery.

The AIDR (Artificial Intelligence for Data Discovery and Reuse) 2019 conference took place May 13-15 and brought 150 researchers, computer scientists, librarians and industry representatives from 10 countries and 65 institutions and organizations to CMU May 13-15.

Supported by the National Science Foundation (NSF)’s public access initiative, organized by the Carnegie Mellon University Libraries with the assistance of the Pittsburgh Supercomputing Center, and in-cooperation with the Association for Computing Machinery (ACM), AIDR 2019 focused on innovative solutions that would enable scientists and researchers to extract more value from large, complex datasets.

“With the recent advances in machine learning and AI, it is possible to train computers to find optimal solutions to a problem, such as integrating different datasets and extracting metadata,” said Huajin Wang, a CMU librarian and conference chair. “We created AIDR 2019 because it’s about time that people working in a variety of disciplines come together to benefit from diverse expertise, and address these mutual challenges together, using the power of AI.”

Attendees heard from speakers including Tom Mitchell, the E. Fredkin University Professor of Machine Learning and Computer Science and interim dean of the School of Computer Science; Glen de Vries, a 1994 graduate of the Mellon College of Science and president and co-founder of Medidata Solutions; and Natasha Noy, staff scientist at Google AI and team lead for Google Dataset Search. Discipline-specific presentations and panel discussions rounded out the agenda.

Rema Padman, Professor of Management Science and Healthcare Informatics in theHeinz College of Information Systems and Public Policy works on data-driven decision making in the IT-enabled healthcare context, particularly to support complex clinical and consumer focused decisions. Her work involves the analysis of large amounts of structured data as well as video data to better understand challenges such as how patients can be more informed about their health conditions to improve self-care.

“The AIDR meeting with its focus on addressing the challenges of data quality, reproducibility and reuse is directly relevant to data driven decision making in healthcare and many other domains,” Padman said. “I was particularly struck by the range of topics presented at the conference, including astronomy, archeology, brain science and my own work — all examples of data driven decision making with different types of data, tools and methods, and motivated by exciting research questions.”

Convening a diverse set of speakers and attendees for this inaugural event was a priority for the conference organizers. As the explosion in the volume of scientific data has made it increasingly challenging to find data scattered across platforms, greater data complexity and lack of consistent data standards across disciplines present new hurdles to evaluating data quality, reproducing results and reusing data for new discoveries.

“Difficulty in scientific data reuse has been an important issue that impedes rapid progress in many disciplines, yet it is a problem that cannot be easily solved by any single discipline alone,” Wang said. “University Libraries have played an essential role in connecting the campus community, providing digital tools and services for open science and open data, and fostering collaborations across disciplines, so it is only fitting that we take a leading role in this initiative.”

Last year’s Open Science Symposium, organized by the Libraries and held Oct. 18-19 at the Mellon Institute Library, assembled a diverse audience from departments at CMU and the University of Pittsburgh to discuss the growing open science movement, which has aimed to make all research products, including data, code, and publications, freely available.

The Libraries will continue to create venues for cross-disciplinary opportunities for CMU scholars with the second Open Science Symposium on Nov. 7, and a second AIDR event in 2020. A newly created AIDR mailing list, is available for anyone who is interested in the topic of AI and data reuse, and is not limited to conference attendees.

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Technology revolutions come and go, and artificial intelligence is the next big innovation making its impact on the world. Generally, this term refers to machines and software capable of imitating human behavior. Often, AI programs can perform cognitive functions you’d expect a human mind to possess.

Examples include learning, problem-solving, perception, planning and natural language processing. Although artificial intelligence is still evolving and in the early stages, it’s starting to make its presence felt in a wide range of areas. AI systems are already competing in strategic games like chess, interpreting complex datasets and powering autonomous cars.

Artificial intelligence is hugely transformative, but that’s not to say everyone sees it in a positive light. There are many people and companies that believe AI is a danger to humanity and society. One of the biggest questions here is: “Will AI replace humans?”

In April, Kai-Fu Lee, founder of leading venture capital firm Sinovation, told CNBC AI will kill 50% of jobs within the next decade.

Research from Infosys, meanwhile, showed that 90% of organisations believe employees have concerns about the adoption of AI. The same company found 53% of businesses believe ethical concerns will stop AI from being as effective as it can be.

There are also questions around privacy. How do we know that AI systems aren’t tracking us and that companies are using this data inappropriately? Can we ever be sure AI is truly safe?

Bad AI can mean life or death

Specialist software design and engineering company Aricenis one of the pioneers of commercial in the world of artificial intelligence, having provided AI and machine learning expertise to the likes of IBM, Microsoft and Amazon. As you would expect, the CTO of the company, Walid Negm, is a huge believer in the technology but believes that organisations need to show responsibility when using it. He says businesses need to keep human involvement when it comes to implementing the innovation.

“Companies are investing in AI to create experiences that have already raised customer expectations. However, AI doesn’t just happen. For the foreseeable future, businesses will need to be responsible to curate, select, evaluate and fine-tune models that are meant to accurately understand and explain specific situations — eg recognise what’s a dog versus a hot dog or predict the onset of an engine’s failure,” he says.

“However, AI models are only as good as the underlying historical observations used to build them. When the data do not accurately represent the real world or are biased in some way, the recommendations, suggestions, and forecasts can quickly run amok. The recent failure of the “mushroom-identifying app” highlighted the dangers of bad AI.

If a business fails to take AI safety seriously, it can end up facing public humiliation and financially damaging lawsuits. “An AI algorithm that is fitted on faulty knowledge can mean life and death in the case of industrial operations or self-driving cars. In the best case a faulty model will result in customers abandoning a product. So, without human judgment of machine learning models, companies introduce the risk of reputation damage, financial losses, potential lawsuits and/or a public backlash. Over time, product makers will need to figure how to catch this AI tiger by the tail,” he adds.

Data challenges

Nick Patience, research vice president of software at 451 Research, says AI will certainly have an impact on jobs but that the most challenging problem will be around data. He explains that companies need to create systems that are transparent and ethical. “AI and machine learning-driven applications will initially take over certain tasks – rather than entire jobs – currently performed by humans. Initially this will be the most repetitive and mundane tasks. Over time, though, some jobs will be replaced entirely, in areas such as transportation and retail,” he tells IT Pro.

“Data is the feedstock of AI, especially unstructured data, giving insights into customer intent, employee behavior. However, as consumers realise quite how much data is being collected on them to fuel these models and algorithms, there will be pushback as more stringent privacy controls are demanded.

“There is the danger of bias being baked into machine learning applications at any stage, be it the data, the training of models and or the programming of algorithms. Developers and owners of those applications need to guard against this but also make the applications sufficiently transparent so biases can be detected and fixed at whatever stage they occur.”

While artificial intelligence can certainly speed up business processes, that’s not to say the technology will be best thing for companies. Jane Zavalishina, CEO of Yandex Data Factory, argues that firms will likely struggle to integrate AI systems into existing business operations and humans will still be more capable in other areas, such as common sense and compassion.

“Due to its ability to make better predictions or recommendations for routine decisions, AI will become a natural part of business. While we may argue about job automation, task automation is inevitable. This leads us to a core challenge: successfully integrating functions, now executed by AI, into the existing business processes,” she says.

“AI can be very efficient when applied well, but it is very different from your usual employee. For example, it doesn’t have common sense. Thus, when defining the tasks, one should always be very careful in outlining restrictions and goal metrics – not forgetting the small details that seem obvious to humans.

“Nor can AI generate trust in a way humans do, by supporting decisions with solid arguments. AI will set us free from mundane, repetitive activities because it’s much better at this job. In exchange, we will need to learn how to be better “bosses” for our AI “employees.”

Academic views

Daniel Kroening, a professor of computer science at the University of Oxford and founder of AI company DiffBlue, says much unpredictability surrounds AI. This, in his opinion, is the most worrying thing about the technology. “The unpredictable and complex nature of AI presents one of the biggest challenges for humans in understanding its behaviour. This is why we need to develop AI that will be highly intelligent, but transparent enough for humans to understand its complex decisions. At Diffblue we are creating AI that fixes bad code in a way that a developer can comprehend and review easily,” he says.

Meanwhile, Dr Aniko Ekart, senior lecturer at Aston University in Birmingham, says AI will become a core part of our daily lives, introducing many benfits rather than challenges. “Artificial intelligence and robotics aren’t science fiction anymore; they are becoming part of our daily lives. Research in this field is driven by curiosity about how humans and animals operate, as much as desire to improve quality of life. The rapid advances are certainly leading to reduced need for some jobs and skills, while continuously changing and shaping other jobs,” she says.

“But should we be scared that machines will take over? Consider the example of town criers, initially having a major role in making public announcements. Over the centuries, communication has been transformed through the invention of the loudspeaker, radio and television broadcast, internet, YouTube and social media. As some jobs disappeared, many new ones came into existence and instantaneous communication of news to large audiences is now available to virtually anyone.

“Similarly, advances in Artificial Intelligence Research (AIR) are bound to further improve our quality of life and bring many benefits. It’s our responsibility as scientists and educators to educate the public and prepare the next generation for a future alongside robots — and empower them to embrace AIR and ensure its use for the benefit of humanity.”

It’s clear that over the next few decades, artificial intelligence technologies will play an integral role in our daily lives and society. But while there will be benefits, the technology industry can’t shy away from the challenges. Businesses need to take in mind the impact AI will have on jobs and develop systems that put safety first.

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