Mediaboss Marketing

(Getty Images/istockphoto / Peshkova)

The debate on contact-tracing highlights the urgency of tackling unregulated technologies like artificial intelligence (AI). With a strong democracy and reputation for first-class research, Switzerland has the potential to be at the forefront of shaping ethical AI.

What is Artificial Intelligence (AI)? "Artificial intelligence is either the best or the worst thing ever to happen to humanity," the prominent scientist, Stephen Hawking, who died in 2018, once said.

An expert group set up by the European Commission presented a draft ofethics guidelinesexternal linkfor trustworthy AI at the end of 2018, but as of yet there is no agreed global strategy for defining common principles, which would include rules on transparency, privacy protection, fairness, and justice.

Thanks to its unique features a strong democracy, its position of neutrality, and world-class research Switzerland is well positioned to play a leading role in shaping the future of AI that adheres toethical standards. The Swiss government recognizes the importance of AI to move the country forward, and with that in mind, has been involved in discussions at the international level.

What is AI?

There is no single accepted definition of Artificial Intelligence. Often, it's divided into two categories, Artificial General Intelligence (AGI) which strives to closely replicate human behaviour while Narrow Artificial Intelligence focuses on single tasks, such as face recognition, automated translations and content recommendations, such as videos on YouTube.

However, on the domestic front, the debate has just begun, albeit in earnest as Switzerland and other nations are confronted with privacy concerns surrounding the use of new technologieslike contact-tracing apps, whether they use AI or not, to stop the spread of Covid-19.

The European initiative the Pan-European Privacy-Preserving Proximity Tracing initiative PEPP-PT advocated a centralized data approach that raised concern about its transparency and governance. However, it was derailed when a number of nations, including Switzerland, decided in favour of a decentralized and privacy-enhancing system, called DP-3T (Decentralized Privacy-Preserving Proximity Tracing). The final straw for PEPP-PT was when Germany decided to exit as well.

"Europe has engaged in a vigorous and lively debate over the merits of the centralized and decentralized approach to proximity tracing. This debate has been very beneficial as it made the issues aware to a broad population and demonstrated the high level of concern with which these apps are being designed and constructed. People will use the contact-tracing app only if they feel that they don't have to sacrifice their privacy to get out of isolation," said Jim Larus. Larus is Dean of the School of Computer and Communication Sciences (IC) at EPFL Lausanne and a member of the group that initially started the DP3T effort at EPFL.

According to a recent survey, nearly two-thirds of Swiss citizens said they were in favour of contact tracing. The DP-3T app is currently being tested on a trial basis, while waiting for the definition of the legal conditions for its widespread use, as decided by the Swiss parliament.However, the debate highlights the urgency of answering questions surrounding ethics and governance of unregulated technologies.

+ Read more about the controversial Swiss app

The "Swiss way"

Artificial intelligence was included for the first time in the Swiss government's strategy to create the right conditions to accelerate the digital transformation of society.

Last December, a working group delivered its report to the Federal Council (executive body) called the "Challenges of Artificial Intelligence". The report stated that Switzerland was ready to exploit the potential of AI, but the authors decided not to specifically highlight the ethical issues and social dimension of AI, focusing instead on various AI use cases and the arising challenges.

"In Switzerland, the central government does not impose an overarching ethical vision for AI. It would be incompatible with our democratic traditions if the government prescribed this top-down," Daniel Egloff, Head of Innovation of the State Secretariat for Education, Research and Innovation (SERI) told swissinfo.ch. Egloff added that absolute ethical principles are difficult to establish since they could change from one technological context to another. "An ethical vision for AI is emerging in consultations among national and international stakeholders, including the public, and the government is taking an active role in this debate," he added.

Seen in a larger context, the government insists it is very involved internationally when it comes to discussions on ethics and human rights. Ambassador Thomas Schneider, Director of International Affairs at the Federal Office of Communications (OFCOM), told swissinfo.ch that Switzerland in this regard "is one of the most active countries in the Council of Europe, in the United Nations and other fora". He also added that it's OFCOM's and the Foreign Ministry's ambition to turn Geneva into a global centre of technology governance.

Just another buzzword?

How is it possible then to define what's ethical or unethical when it comes to technology? According to Pascal Kaufmann, neuroscientist and founder of theMindfire Foundationexternal linkfor human-centric AI, the concept of ethics applied to AI is just another buzzword: "There is a lot of confusion on the meaning of AI. What many call 'AI' has little to do with Intelligence and much more with brute force computing. That's why it makes little sense to talk about ethical AI. In order to be ethical, I suggest to hurry up and create AI for the people rather than for autocratic governments or for large tech companies.Inventing ethical policies doesn't get us anywhere and will not help us create AI.''

Anna Jobin, a postdoc at the Health Ethics and Policy Lab at the ETH Zurich, doesn't see it the same way. Based on her research, she believes that ethical considerations should be part of the development of AI: "We cannot treat AI as purely technological and add some ethics at the end, but ethical and social aspects need to be included in the discussion from the beginning." Because AI's impact on our daily lives will only grow, Jobin thinks that citizens need to be engaged in debates on new technologies that use AI and that decisions about AI should include civil society. However, she also recognizes the limits of listing ethical principles if there is a lack of ethical governance.

For Peter Seele, professor of Business Ethics at USI, the University of Italian-speaking Switzerland, the key to resolving these issues is to place business, ethics, and law on an equal footing. "Businesses are attracted by regulations. They need a legal framework to prosper. Good laws that align business and ethics create the ideal environment for all actors," he said. The challenge is to find a balance between the three pillars.

Artificial intelligence is being used to developrobots and drones that can explore dangerous places beyond the reach of humans and animals.

See in other languages: 4 See in other languages: 4 Languages: 4

The perfect combination

Even if the Swiss approach mainly relies on self-regulation, Seele argues that establishing a legal framework would give a significant impulse to the economy and society.

If Switzerland were to take a lead role in defining ethical standards, its political system based on direct democracy and democratically controlled cooperatives could play a central role in laying the foundation for the democratization of AI and the personal data economy. As the Swiss Academy of Engineering Sciences SATWsuggested in a whitepaper at the end of 2019, the model for that could be the SwissMIDATAexternal link, a nonprofit cooperative that ensures citizens' sovereignty over the use of their data, acting as a trustee for data collection. Owners of a data account can become members of MIDATA, participating in the democratic governance of the cooperative. They can also allow selective access to their personal data for clinical studies and medical research purposes.

The emergence of an open data ecosystem fostering the participation of civil society is raising awareness of the implications of the use of personal data, especially for health reasons, as in the case of the contact-tracing app. Even if it's argued that the favoured decentralized system does a better job preserving fundamental rights than a centralized approach, there are concerns about susceptibility to cyber attacks.

The creation of a legal basis for AI could ignite a public debate on the validity and ethics of digital systems.

Frida Polli is a neuroscientist and co-founder of pymetrics, an AI-based job matching platform based in the United States.

Horizontal Line

How the Swiss are moving back to the mountains

Form for signing up for free newsletter.

Sign up for our free newsletters and get the top stories delivered to your inbox.

View post:
Ethical artificial intelligence: Could Switzerland take the lead? - swissinfo.ch

Artificialintelligence (AI) comprises a type of computer science that develops entities,such as software programs, that can intelligently perform tasks or makedecisions.1 The development and use of AI in health care is not new;the first ideas that created the foundation of AI were documented in 1956, andautomated clinical tools that were developed between the 1970s and 1990s arenow in routine use. These tools, such as the automated interpretation ofelectrocardiograms, may seem simple, but are considered AI.

Today,AI is being harnessed to help with big problems in medicine such asprocessing and interpreting large amounts of data in research and in clinicalsettings, including reading imaging or results from broad genetic-testingpanels.1 In oncology, AI is not yet being used broadly, but its useis being studied in several areas.

Screeningand Diagnosis

Thereare several AI platforms approved by the US Food and Drug Administration (FDA)to assist in the evaluation of medical imaging, including for identifyingsuspicious lesions that may be cancer.2 Some platforms help tovisualize and manipulate images from magnetic resonance imaging (MRI) orcomputed tomography (CT) and flag suspicious areas. For example, there are severalAI platforms for evaluating mammography images and, in some cases, help todiagnose breast abnormalities. There is also an AI platform that helps toanalyze lung nodules in individuals who are being screened for lung cancer.1,3

AI isalso being studied in other areas of cancer screening and diagnosis. Indermatology, skin lesions are biopsied based on a dermatologists or primarycare providers assessment of the appearance of the lesion.1 Studiesare evaluating the use of AI to either supplement or replace the work of theclinician, with the ultimate goal of making the overall process moreefficient.

Big Data

Astechnology has improved, we now have the ability to create a vast amount ofdata. This highlights a challenge individuals have limited capabilities toassess large chunks of data and identify meaningful patterns. AI is beingdeveloped and used to help mine these data for important findings, process andcondense the information the data represent, and look for meaningful patterns.

Such toolswould be useful in the research setting, as scientists look for novel targetsfor new anticancer therapies or to further their understanding of underlyingdisease processes. AI would also be useful in the clinical setting, especiallynow that electronic health records are being used and real-world data are beinggenerated from patients.

Read this article:
Artificial Intelligence in Cancer: How Is It Used in Practice? - Cancer Therapy Advisor

May 15, 2020In the race to stem COVID-19, researchers around the world are testing the capacity of artificial intelligence (AI) to assist in tasks such as diagnosis and drug discovery. So far, AIs biggest success during the pandemic has been in speeding up the process of identifying existing drugs that can be repurposed to help suffering patients, said Deborah DiSanzo, who recently lectured on COVID-19 in the new course shes leading at Harvard T.H. Chan School of Public HealthArtificial Intelligence in Health.

DiSanzo cited in her lecture an AI knowledge graph developed by researchers at the UK startup BenevolentAI and the Imperial College London, which found that baricitinib, a rheumatoid arthritis drug, had the potential to inhibit the virus that causes COVID-19. It and other drugs identified in similar studies have now gone into clinical trials.

Two years ago, finding either a new or repurposed drug target would take six to 18 months, said DiSanzo, a former health care technology executive. These researchers did this in weeks.

Diagnosing COVID-19, however, has been less successful for AI so far, she said, with the limited lung imagery currently available from COVID-19 patients making it difficult for neural networks to learn the difference between the effects of the virus and standard pneumonia.

Enhance, not replace

For DiSanzos students, these mixed results provided a timely example of one of her courses main takeaways: AI can enhance health care delivery and research, but its not a replacement for the knowledge and skill of providers and scientists.

Im really excited about the technology and potential application of AI, said Nimerta Sandhu, MPH20, an MD candidate at Drexel University College of Medicine. This course provided insights on technology solutions that offer added value and others that have room for improvement. One of the biggest challenges is going to be ensuring that, as we incorporate more AI in our work, it doesnt detract from the empathy essential in the patient-provider relationship.

I want students to have a realistic view of what artificial intelligence can bring to public health, DiSanzo said. People usually have either a very positive viewthat its magic and can solve all the worlds problemsor they have a very negative view, that its biased and doesnt give accurate results. She said that she wants students to leave her course knowing the right questions to ask, because its likely to be a part of their jobs, whether they are in practice or policy.

Business background

Prior to joining Harvard Chan School, DiSanzos roles included CEO of Philips Healthcare, and general manager of IBM Watson Health, the IBM business unit founded to advance artificial intelligence in health. Last year, as a Harvard Advanced Leadership Initiative Fellow, she was encouraged by the programs faculty chair Meredith Rosenthal, C. Boyden Gray Professor of Health Economics and Policy, to develop a course for MPH students.

DiSanzo hadnt planned to cover COVID-19 as she worked on her syllabus in January, but as the full extent of the pandemic emerged, she added it to her list of lecture topicswhich also included drug discovery, medical imaging, and patient monitoring.

While the spring semesters move to online learning required the first-time instructor to pivot on the fly, DiSanzo has been delighted with the results so far, she said. Her 24 studentswho include physicians, a veterinarian, and a psychologisthave been very engaged, participating actively on discussion boards and in chats with guests including executives from Google and pharmaceutical companies.

DiSanzo hesitates to make predictions about the future of AI in health, noting the fields history of overly optimistic projections. But things are different today, she said. In recent years, computing power, available data, and neural network capacity have advanced by leaps and bounds. Its likely that in 10 yearsmaybe even fiveevery health care or public health decision that we make, or care that we give, or diagnosis that we make, will be made with some help from artificial intelligence, DiSanzo said. And with the COVID-19 pandemic pushing the field forward at even faster rates, she said, the next advancements may be just months away.

Amy Roeder

Illustration: Alina_Bukhtii/Shutterstock

More here:
Course introduces students to the promise, challenges, of artificial intelligence in health - HSPH News

A special, peer-reviewed edition of OMICS: A Journal of Integrative Biology, has highlighted the importance of key digital technologies, including Artificial Intelligence (AI), machine learning, and blockchain for innovation in healthcare in response to the challenges posed by COVID-19.

Vural zdemir, MD, PhD, Editor-in-Chief ofOMICS, said: COVID-19 is undoubtedly among the ecological determinants of planetary health. Digital health is a veritable opportunity for integrative biology and systems medicine to broaden its scope from human biology to ecological determinants of health. This is very important.

Articles in the special issue include an interview on Responsible Innovation and Future Science in Australia byJustine Lacey, Commonwealth Scientific and Industrial Research Organisation (CSIRO), and Erik Fisher, Arizona State University, Tempe, Blockchain for Digital Health: Prospects and Challenges and Integrating Artificial and Human Intelligence: A Partnership for Responsible Innovation in Biomedical Engineering and Medicine.

In Blockchain for Digital Health: Prospects and Challenges the article explores the challenges that can be faced with the use of blockchain technology.

The article states: Although still faced with challenges, blockchain technology has an enormous potential to catalyse both technological and social innovation, turning the promise of digital health into a reality. By reshaping both the technological and social environment, the rise of blockchain in digital health can help reduce the disparity between the enormous technical progress and investments versus our currently inadequate understanding of the social dimensions of emerging technologies through commensurate investments in the latter knowledge domain.

A recent report by Market Study Report, Blockchain Technology in Healthcare Market, notes that blockchain technology in the healthcare market is anticipated to cross $1636.7m (1513.46m) by the year 2025.

Privacy is a major concern when it comes to storing and sharing health data, and with current healthcare data storage systems lacking top end security, blockchain can provide a solution to vulnerabilities such as hacking and data theft.

Blockchain technology in healthcare offers interoperability, which enables exchange of medical data securely among the different systems and personnel involved, offering a variety of benefits such as effective communication system, time reduction, and enhanced operational efficiency.

According to the report, the use of blockchain technology for claims adjudication and billing management application is predicted to register 66.5% growth by the year 2025, owing to several issues such as errors, duplications, and incorrect billing. All of these problems can be eliminated with blockchain.

Nearly 400 individuals including doctors were convicted for $1.3bn (1.2m) fraud in 2017 in the United States. The report highlights that the need to mitigate such frauds and fake drug supply will encourage the adoption of technology in this application segment.

See original here:
AI, machine learning, and blockchain are key for healthcare innovation - Health Europa

Dublin, May 15, 2020 (GLOBE NEWSWIRE) -- The "Artificial Intelligence Markets in IVD" report has been added to ResearchAndMarkets.com's offering.

This report examines selected AI-based initiatives, collaborations, and tests in various in vitro diagnostic (IVD) market segments.

Artificial Intelligence Markets in IVD contains the following important data points:

The past few years have seen extraordinary advances in artificial intelligence (AI) in clinical medicine. More products have been cleared for clinical use, more new research-use-only applications have come to market and many more are in development.

In recent years, diagnostics companies - in collaboration with AI companies - have begun implementing increasingly sophisticated machine learning techniques to improve the power of data analysis for patient care. The goal is to use developed algorithms to standardize and aid interpretation of test data by any medical professional irrespective of expertise. This way AI technology can assist pathologists, laboratorians, and clinicians in complex decision-making.

Digital pathology products and diabetes management devices were the first to come to market with data interpretation applications. The last few years have seen the use of AI interpretation apps extended to a broader range of products including microbiology, disease genetics, and cancer precision medicine.

This report will review some of the AI-linked tests and test services that have come to market and others that are in development in some of the following market segments:

Applications of AI are evolving that predict outcomes such as diagnosis, death, or hospital readmission; that improve upon standard risk assessment tools; that elucidate factors that contribute to disease progression; or that advance personalized medicine by predicting a patient's response to treatment. AI tools are in use and in development to review data and to uncover patterns in the data that can be used to improve analyses and uncover inefficiencies. Many enterprises are joining this effort.

The following are among the companies and institutions whose innovations are featured in Artificial Intelligence Markets in IVD:

Key Topics Covered

Chapter 1: Executive Summary

Chapter 2: Artificial Intelligence In Diagnostics Markets

Chapter 3: Market Analysis: Artificial Intelligence in Diagnostics

For more information about this report visit https://www.researchandmarkets.com/r/vw8l7u

Research and Markets also offers Custom Research services providing focused, comprehensive and tailored research.

The rest is here:
Artificial Intelligence Markets in IVD, 2019-2024: Breakdown by Application and Component - GlobeNewswire