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blockchain disinformation

How one high-tech solution is helping fight fake news

  • min read
    • As ways to create and disseminate disinformation get more sophisticated and widely available, it's getting harder to tell fact and fiction. But the next version of the internet, Web3, might offer a 'force field' that could protect citizens from this troubling phenomenon. Start-ups and established media companies are using blockchain as part of solutions that aim to verify information and filter out false news. Is this what the world needs now, or could it open the door to even more disinformation? Fakes harder to tell from reality "There's huge money and huge power in fake news. We've seen it here in the US and around the world," says Jim Nasr, CEO of blockchain developer Acoer. Acoer created blockchain-based news platform NewsHash. Disinformation can have far-reaching and sometimes dangerous impacts. We saw this over the pandemic years especially, with rampant covid-related medical disinformation. Tommaso Canetta, vice director of Italian fact-checking agency Pagella Politica, says how false news spreads is "ever-changing. It can [come from] and go in every direction – from traditional to online media to politicians, or the other way around." A 2020 Reuters report found over 56 percent of internet users are worried if they can tell fake from real online. And 'deepfakes' – highly realistic looking audio, video and photos created using AI – further muddy the waters. A technological solution for filtering out fakes? Blockchain could create a 'built-in truth index' for the internet, effectively labeling and verifying each piece of information, creating a sort of watermark for authenticity. Used the right way, this could help people be sure news and media they receive are real. But it's complicated. Using blockchain in journalism might solve some problems but create others. For example, it may trigger more sophisticated disinformation, poor transparency or a single company like Meta (formerly Facebook) dominating the industry. What is blockchain? Blockchain stores information as blocks added in chronological order to a virtual 'chain,' stored securely and permanently. Once added to the chain, blocks are hard to change. Well-known blockchain platforms like Bitcoin and Ethereum use it to store financial transaction information, but the technology can also store and distribute news. Blockchain-based news platforms provide a secure and decentralized way to store verified information, which cannot be changed – like putting a digital watermark on verified news stories. Initiatives like the now-defunct Civil for journalism, Steemit for social media and Truepic for photo and video let publishers permanently link verified information to content in a blockchain. New York Times is also developing a blockchain-news platform, News Provenance Project. Fighting a powerful form of disinformation, miscaptioning – real photos or videos described in misleading ways – the project uses blockchain to permanently attach context (like who shot the film or image, when and where) to media. Acoer's NewsHash works by giving an article an encrypted hash (a unique string of characters) containing metadata added by the publisher, like the author's name and a publication identification number. "If the article changes in any way – even the most minuscule change – we can tell," says CEO Nasr. "And all in real-time." Readers can scan a QR code, which consults the blockchain to check whether an article or video is real. Nasr's company also assigns 'confidence scores,' rating information's authenticity. But while blockchain-based platforms can help protect against doctored images or false news pretending to be from a reputed source, they may also create new problems. But what's further up the chain? Technologies chasing a disinformation-free internet can backfire by encouraging more sophisticated disinformation or offering different versions of 'the truth.' Nasr says there are blind spots when publishing a completed news article on the blockchain because there are many places data can come from. The immutable nature of blockchain also means it may become harder to correct inadvertent errors in published material, making journalism as a whole seem like a less trustworthy source. Another issue is the potential for centralization – a single platform or business may start to dominate the news. "It's easy for an AI algorithm or company to become a centralized location for everything. It's like the internet – the idea is great, but we now have four companies [Meta, Amazon, Apple and Google] dominating it," says Nasr. "That's why we use a public ledger, instead of specific tools or proprietary algorithms, to [let readers] interrogate how we're getting there." The first and second lines of defense aren't technology With advances in AI, video and image editing software, tools for spreading disinformation will improve. Deepfake videos have already fooled internet users many times. "With the progress of fact-checking and technological tools to fight disinformation, we'll also see more complex disinformation," says Canetta. There's also a paradox in journalism requiring trust, while blockchain seeks to eliminate the need for trust. "Journalism is built around gatekeeping. You need someone to approve a story before it goes online – a hierarchy or system in place to ensure quality," says Walid Al-Saqaf, former journalist and senior lecturer in media technology and journalism at Södertörn University, Sweden. "But the notion of Bitcoin and the original blockchain was that you don't need trust." Canetta believes awareness and media literacy are the first and second lines of defense in fighting disinformation. He feels blockchain journalism isn't a silver bullet because "misinformation is not just an online phenomenon. We must teach school children that while they have access to unlimited information, not all of it is true or accurate." Using blockchain to secure other information Decentralized platforms like blockchain have many applications, like more secure communication between organizations, facilitating payroll and making auditing easier. Start-ups are working on blockchain systems that authenticate supply chains, enable digital voting, create and implement smart contracts and more. Within companies, blockchain can help secure data and make sure digital communications aren't altered. But it's not failsafe. For example, an unauthorized user can access a blockchain with a stolen password. And although it would require significant resources, it's possible to take over a whole blockchain and force a change. As blockchain evolves, so will ways to hack into it. Keeping systems updated and secure, and following security best practice will remain crucial to safer business.
threats outer space

How criminals could use space junk to attack any business

  • min read
    • A powerful way cybercriminals can cause catastrophic damage on Earth is to attack in space. It may sound like science fiction, but now we all rely on space infrastructure to do business, any organization could be affected. Space enthusiast and Distinguished Chair of the cybersecurity program at Middle East Institute Chris Kubecka explains a little-known space disaster scenario we should be more aware of in Tomorrow Unlocked's video Threats in Outer Space. Smash-ups in space Although space exploration has brought many world-changing advances, it's left orbit like many places on Earth – littered. From obsolete satellites to rocket parts and chips of paint, space junk circles the Earth at high speeds, threatening to collide with useful things, like working satellites and the International Space Station. Collisions could be deadly or cause irreparable damage to expensive equipment. Any smash-up creates more space junk, raising the risk to other orbiting objects. For a sense of just how much 'stuff' there is up there, see known space objects tracked in real-time on StuffIn.Space, some known only as 'debris' or 'unknown.' The 'black swan' of space crashes One of the most serious consequences of a space collision is Kessler Syndrome. Kubecka describes it: "A big enough collision would create a chain reaction, causing space junk to smash into each other all over orbit, forming a dust cloud." An accident or deliberate attack could trigger a Kessler Syndrome event. Kubecka explains, "It's now so cheap to build 'attack CubeSats' or even larger satellites. You could cause collisions." She also points to the growing use of Internet of Things (IoT) devices in space tech. "If IoT goes rogue, that's a big problem." As wealthy business people become more visible in space exploration, the potential for huge media attention will attract cybercriminals to attack their space systems and use them to create chaos in space – and so, chaos on Earth. Preventing a 'Kessler' event As well as protecting space systems from cyberattack through the best available methods, like cyber immunity – building systems in a way that makes them almost impossible to compromise – we must take out the space trash. As part of the Securing Space initiative, Kaspersky supports fledgling innovators StartRocket as they develop a new way to remove space debris from orbit – with foam.
How AI can make therapy better

Could AI make mental health therapy more effective?

  • min read
    • Artificial intelligence (AI) can analyze vast amounts of data more quickly than humans and draw accurate conclusions, making for myriad business applications. But now, some technologists and mental healthcare clinics are using artificial intelligence (AI) to inform mental health therapy. They think it could help pinpoint the most effective strategies to help sufferers get better. This may mean better staff retention and productivity and greater value for money from employee assistance programs for business. UK-based mental health clinic Ieso and US-based mental health technology firm Lyssn are among those experimenting with AI-augmented therapy. Why mental health is harder to treat I spoke with Valentin Tablan, Chief AI Officer at Ieso. He says while physical healthcare has MRIs, CAT scanners and hundreds of blood tests to diagnose conditions, "In mental healthcare, we've been slower to adopt technology. But that's changing. We can start using artificial intelligence and natural language processing techniques to understand therapy-specific language and gain insights from that." Ieso uses AI to analyze transcripts of patient-therapist conversations to identify which elements of therapy give the best outcomes, identify cases that may need a more experienced therapist and help evaluate the quality of care. They're also working on a cognitive behavioral therapy (CBT) companion app that supports patients in between therapy sessions. Lyssn also uses AI to evaluate the quality of psychotherapy and give therapists actionable feedback. Practitioners hope to increase recovery rates and quality of care to bring it on par with physical healthcare's higher recovery rates. Ieso has seen recovery rates increase 2 percent alongside its AI program – small but encouraging. AI no replacement for therapists "Psychotherapy's quality is usually measured by experts observing a conversation. Our AI is trained to replicate this human judgment, reducing the cost and time to evaluate the conversation," says Zac Imel, chief science officer and co-founder of Lyssn. More accessible, AI-powered forms of therapy include chatbots. These can help support patients between sessions, collect useful data and provide emotional support to people who cannot access therapy because of stigma, time or financial reasons. Chatbots simulate a conversation between humans, using AI to learn from examples of real conversations. The AI in this case is often trained on therapy session transcripts. It replicates how a therapist would respond to a patient with some accuracy. UK's National Health Service (NHS) says up to 8 million people in England want to access mental health treatment but do not qualify for it. High costs, long waiting lists and not wanting to unburden themselves to a stranger also keep people away from therapy. "One barrier to providing care to more people is that CBT, the most common evidence-based treatment for depression, is hard to teach. Having tools to speed up the training would help," Dr. Paola Pedrelli, Assistant Professor of Psychology at Harvard Medical School, told me. Additionally, supervisors usually evaluate and provide feedback to therapists in training, but there isn't always the time or resources. AI could bridge that gap. Pedrelli is part of a team that worked on a project using machine-learning algorithms to diagnose and monitor major depressive disorder. "Sometimes treatment takes months to work. Tools to speed the process would be beneficial," she added. AI's advantage over the human brain AI's biggest advantage over the human brain is being fast and reliable at analyzing data, said Imel of Lyssn. AI can quickly analyze, process and gain insight from large data sets, making it a powerful tool in mental healthcare where markers of recovery are vague. "With repetitive tasks, humans get tired and can drift over time. And if you show a human the same conversation twice, they might evaluate it differently. With AI – same data in, same answer out," Imel says. How AI-powered mental health can help employees AI-based therapy tools like Ieso's CBT companion app, Ellie, Woebot designed by Institute for Creative Technologies (ICT) at the University of Southern California, The Trevor Project's Crisis Contact Simulator and others cannot replace human therapists but help improve access and reduce cost. In a randomized, controlled trial of 70 participants, Woebot users displayed a significant decrease in depression symptoms compared with the control group. Both groups showed a decrease in anxiety. Those experiencing mental distress or stress at work could be directed to therapy bots as a bridge between needing therapy and getting access to it. Chatbots can also gather data about how a patient feels and suggest clinical intervention where required. Organizations considering employee access to therapy bots should consult an expert and make sure therapy is available alongside these solutions rather than as a substitute. Privacy and security in AI-powered therapy Alongside benefits, AI-powered therapy programs come with obvious cybersecurity and privacy risks. Sensitive and private conversations between patients and therapists could be exposed or manipulated. Iron-clad security is the first step to protecting patient privacy. Tablan and Imel shared the importance of data security to their companies and how they keep patient data safe, such as using multiple layers of security and robust security certifications. "Most people working at Ieso can't access this data. It's accessible to computers, but few humans," said Tablan. Even with the strongest security practices, risks to patient privacy still exist, as a lot depends on humans who can access the data. But those involved believe the potential benefits of AI-powered therapy outweigh the risks. AI-augmented, not AI-based therapy There's much to suggest unconscious bias is present in the therapy room as in other parts of life. Black and minority ethnic people are less likely to find counseling fit for purpose. Lesbian, gay and bisexual people also report greater dissatisfaction with counseling and psychotherapy than their heterosexual counterparts. When AI programs can imbibe biases in data used to train them, there must be concerns that AI-based outcomes could further marginalize some patients. Dr. Tonya Davis, faculty member at Northwestern University, Illinois, suggests "unconscious biases – things hidden just beneath the surface of our awareness" might impact training AI chatbots. "I think that if there is a deliberate effort to identify, understand and resolve biases beneath the surface of our awareness, we could be one step closer to addressing the cultural challenges in person-to-person interactions, thereby minimizing these challenges in the development of AI." Pedrelli said, "There have been cases where AI has provided inappropriate directions in the context of therapy. And AI is still unable to detect subtle emotional changes." However, AI tools only augment diagnosis and therapy. They shouldn't be relied on for unilateral decisions on patient care. "The tools we've built look over our quality of care – they never make any decisions automatically," said Tablan. "They just present information to a qualified clinician who has access to the transcripts and make up their own minds." Imel doesn't describe what Lyssn does as "AI-based therapy." Instead, he says, "This is about using technology to augment the human talent of therapists. So, it's human-based therapy, augmented by AI."
Democratizing artificial intelligence

How we can make sure AI benefits everyone

  • min read
    • We're at a tipping point in history, but the potential in our situation is also palpable. Smart technology has been disruptive. Artificial intelligence (AI) is growing exponentially, creating radical change in how we live, work and play. Every country and industry feels its reverberations. Whole systems of production, management and governance are changing while we face huge challenges like climate change and a global pandemic. Add ongoing issues like fake news, wealth inequality and bias to the mix. We need visionary leadership and action to ensure humanity goes in the right direction as we unlock AI's potential. If we navigate this era wisely, AI will be good for society. Max Tegmark, President of Future of Life Institute, says, "Everything we love about civilization is a product of intelligence, so amplifying our human intelligence with artificial intelligence could help civilization flourish like never before – as long as we keep the technology beneficial." AI is a purpose for humanity My recent book Democratizing Artificial Intelligence to Benefit Everyone examines the smart technology era, how AI affects every part of our world and how it could benefit everyone. I also introduce Sapiens – a human-centered AI-driven platform that puts us in control as individuals. It could monetize the data and services of individuals, companies and even countries. Sapiens addresses a big gap: There is no large-scale solution helping us make the most of our lives. The Sapiens AI Assistant helps you optimize your life, get money for data and services and learns from your behavior. It supports lifelong learning, relationships and wellbeing. It gives guidance, makes recommendations and even helps you build character strengths. Why do we need this? Imagine living in a thriving, uplifting and compassionate society that creates beneficial outcomes for everyone. Smart technology helps this society solve problems and create opportunities. Cities are greener, work is low stress and living more meaningful. Jobs are well-paid, your community is rich in activities and you can easily learn throughout your life. You can focus on quality of life and work with others to further improve society, rewarded for your active participation and positive contributions. I'm developing Sapiens with that future in mind. How business leaders should respond to AI AI is progressively disrupting and changing most sectors and business models. It's activating new markets and future industries. Global management consultancy McKinsey estimates AI will add 13 trillion US dollars to the global economy over the next decade. In my experience developing AI software over two decades, business is getting better at gathering and responding to data, transforming customer retention, productivity and quality. Increased computing power, data storage and processing capability have all helped. Markets are evolving. We see faster, scalable marketplaces, more informed consumers and leaner operations. Advances in AI are birthing new industries and customer segments. Senior executives must make fast, strategic shifts to monetize new opportunities and adapt models as AI adoption spawns new industries. Early examples of new AI-driven market segments include recommendation-driven entertainment streaming and GPS-driven ride-sharing. We've seen tremendous increase in start-ups using AI, with investment growing 18-fold in six years. AI applications today mainly optimize business processes in existing industries like customer support, logistics and supply chain management. But the biggest economic benefits of AI will be in solving complex problems that could start new industries. Business leaders must keep track of machine learning developments to spot trends and react. AI in cybersecurity Cyberattacks can target all businesses, with aims ranging from customer data theft to corporate espionage. IT security economics research by Kaspersky saw the average cost in 2020 of resolving a data breach for an enterprise exceeds $1 million, and more than $100k for small- to medium-sized businesses. Large data breaches can leak the data of millions of people, opening the door for further cybercrime and fraud. AI will play an increasingly important role in protecting businesses from cyberthreats, with security tools analyzing data from millions of threats and using it to identify threats like ransomware and phishing as they become more sophisticated. Staying up to date with AI-based cybersecurity tools and using them to assist security teams helps keep businesses safe from increasingly potent cyberattacks. AI-driven cybersecurity learns how humans react, helping a business become better prepared to face future threats. Personalized engagement for consumer-facing businesses Consumer-facing businesses must offer personalized customer experiences at scale. Tech giants like Amazon, Google and Alibaba have led the way. Machine learning can accurately predict customer needs by building rich, real-time profiles as we interact with websites and virtual assistants. Accenture says investment in AI and human-machine collaboration could boost retail revenue by 38 percent. As we flick through Netflix recommendations or search on Google, AI calculations happen in data centers, with our devices communicating their results to us. This will change as machine-learning algorithms become more efficient and capable of running on low-power devices at the 'edge' (known as edge computing.) Future AI applications in money and finance AI has already had an enormous impact on the financial services sector. Mordor Intelligence estimated AI in fintech was worth nearly 8 billion US dollars in 2020, predicting its value will triple by 2026. AI-automated processes are reducing costs, increasing productivity and preventing fraud. AI could offer personalized engagement for the growing number switching to mobile, in-app transactions. It would provide another possible route for cyberattack and financial fraud, but AI can help address these risks too. Intelligent virtual assistants for banking, powered by messaging platforms, mobile devices or voice assistants, could give convenient, easy customer service for everything from account balances to loan inquiries. My company, South African AI business Cortex Logic, envisages major banks using AI-driven models to optimize home loan prices in a dynamic and personalized way. In this industry, slight increases in market share are of high value. US residents take out 4.4 trillion US dollars in home mortgages each year. By mining structured and unstructured data, banks can better segment and personalize home loan price positioning. AI-powered stock and share trading could use new data sources like satellite imagery relevant to commodity prices and social media sentiments around brands. These could complement established market signals like company performance and news sentiments to optimize investment portfolios. AI-enabled education, healthcare and wellness AI ultra-personalized solutions present a way to use smart tech for a fairer world. McKinsey estimates in the US AI-powered solutions could save 300 billion US dollars a year in healthcare costs. While in the UK, AI-driven preventive care could save 3.3 billion pounds yearly in decreased hospital admissions. These preventative care solutions also forecast disease spread and identify high-risk groups. Developing one new drug costs around 2.6 billion US dollars, but almost 90 percent of proposed therapies fail between first-phase trials and regulatory approval. AI-based solutions are helping speed up developing treatments and making them more cost-effective. AI's impact on society, government and the public sector AI is transforming the public sector with improved delivery, efficiency and safety. Many governments see AI as a game-changer and are putting policies in place to smooth its way. Professional services giant Deloitte's Center of Government Insights report examines how AI can redesign public sector work. For the US federal government alone, task automation could mean savings of 3.3 to 41 billion US dollars (depending on the type of task being sped up and to what extent). Debates around AI's future AI's progress and future direction are hotly debated. Everyone wants to ensure its path is ethical, transparent and beneficial. We're getting better at understanding the complexity and inner workings of AI. We must now develop a thorough understanding of where it's going. How will AI affect our ability to exercise free will? How will it impact our evolution and long-term survival? There are concerns about using AI for surveillance, including tech giants, corporates and governments using it to control, manipulate or profit. Other concerns center around how bias in data can make AI unreliable. Solutions to potential negative impacts include developing policies to ensure AI is directed at the common good. Skills for a human-centered AI-driven workplace OECD's Future of Education and Skills 2030 report recommends students develop three competencies they'll need to shape a healthy and sustainable future: Creating new value, reconciling tensions and dilemmas, and taking responsibility. University of Phoenix's Institute of the Future outlines ten work skills crucial for success in the future workplace in their Future Work Skills 2020 report, including sense-making: finding deeper meaning and significance in information and novel and adaptive thinking: coming up with solutions and responses beyond rules. AI is a vital companion to support people to thrive in the future of work. Democratizing AI is a multi-faceted problem. It's not only dependent on the progress of tech and policy making, but excellent collaboration between the public and private sector, academia and global organizations. We need a strategic plan to ensure we use AI for good outcomes. We must democratize AI throughout the lifecycle starting with its development before deployment and use. We must build human-compatible, ethical and trustworthy AI and address bias and discrimination. With the accelerating pace of AI-driven automation and its impact on work, we must become lifelong learners. We need to make proactive choices as opportunities shift and target areas where we can make meaningful contributions. That way, we'll shape a better future together in the smart technology era.
Drones and emergency response

How drones with neural networks could transform disaster response

  • min read
    • Climate change and COVID-19 have made conversations on how we respond to emergencies mainstream. We've become more aware of the essential workers who risk their lives to save lives or even just keep things ticking over. We've seen multiple applications of new technology fast tracked in the quest to address never-before-seen challenges. In disaster relief, it's great to see ever-widening applications for drones (also known as unmanned aerial vehicles or UAVs,) with their ability to give a fast and relatively inexpensive overview. For example, during 2020's locust invasion, Kenyan Red Cross used drones to assess impact on agriculture and livestock and the effectiveness of control efforts. In Oregon, California and other fire-ravaged US states, firefighters used drones to map fires and even to drop incendiary fireballs that help stop forest fires. These start small fires, removing fuel an approaching fire needs to spread. Artificial brain takes to the sky At Kaspersky, we've been working on a particular contribution to making it easier for authorities to respond to disasters and emergencies with drones and artificial intelligence (AI.) We're piloting an emergency analytics system that uses a neural network. These are mathematical models or algorithms made up of interconnected nodes (neurons) working a little like a human brain. Drones gathering data and identifying objects Of this technology's many possible applications, the most important are in situations where human life is at risk. Drones collecting data for real-time analysis by neural networks can search for missing people, animals and houses washed away in floods. It can assess damage from aerial images after an incident or provide crowd management information, helping to avoid human crush incidents. We're working with drone manufacturers Albatros and have already installed the Kaspersky Neural Network in two of their drone models. In developing the system, we focused on its capacity to detect small objects in limited visibility. For example, emergency services need to find buildings that survived after an earthquake and distinguish residential from non-residential buildings. In a flood, they need to know the amount of debris in a flooded area. Machine learning technology like a neural network can process images and recognize objects instantly. In one of our tests, the Kaspersky Neural Network processed thousands of high-resolution images of rough terrain on a laptop with limited computing power in a few minutes. Our neural networks self-teaches – each time you use it, results improve. Trained on tens of thousands of images, it can recognize and classify people, houses, cars and more. You can process data onboard a drone or on a server or laptop after the flight. Nikita Kalmykov, CEO of drone makers Albatros says, "The system is initially focused on incident analytics and people search. It can also help in the agricultural, construction and oil industries. It has the flexibility customers need for intelligent aerial surveys." Concerns around drones in emergency relief Using drones that process and categorize information using a neural network comes with challenges. Drone technology researcher Faine Greenwood points out public distrust of drones has slowed their adoption in disaster relief. To help smooth over this turbulence, Humanitarian UAV Network educates people and develops standards for humanitarian drone use. When training a neural network, as with all AI, it's important to consider potential bias in data sets and work to reduce it. For example, can the system identify people of all skin colors, genders and body sizes? Can it identify buildings or plants seen in a wide range of countries? Gathering data ethically and storing it securely is another area of concern. In their best practice guidance, Humanitarian UAV Network advises, "Only collect aerial data that has a clear value for specific humanitarian purposes. Other data could be used in discriminatory or other negative ways, or exploited by third parties to harm local communities." The International Red Cross also issues a handbook for data protection in humanitarian action. One thing is clear: In a world of growing challenges in preserving human life, every new tool is worth having. While AI rapidly transforms every part of our lives, drones using neural networks have an important role in disaster relief. It's no surprise humanitarian organizations and emergency services increasingly use drones in their work. Processing information with neural networks can add another level of efficiency and effectiveness to their important work. I hope we'll see this technology widely adopted, and with it, many lives saved.
digital health report

How has COVID-19 changed medicine’s view of digital health?

  • min read
    • It's often said that necessity is the mother of invention. The last few years in digital health also show necessity is also the mother of adoption and acceleration. In a recent Deloitte survey, physician leaders reported increases in virtual appointments for nearly all types of care. For chronic condition management, appointments increased ninefold. In the same survey, more than half said their organization's virtual health strategy changed significantly in response to the pandemic. Telehealth is being adopted worldwide, from the UK's Babylon Health, to Doctolib in France, to China's Ping An Good Doctor. And it's been necessity – like quarantine regulations, contamination risks and growing consultation requests – driving the acceleration. The COVID effect Although the healthcare industry has historically been slow to adopt digital tech, the 'COVID effect' put telemedicine front and center for patients and providers. Kaspersky's 2021 report, Telehealth take-up: The risks and opportunities, confirms healthcare is ready to embrace digital. More than 90 percent of providers use some form of telehealth, such as video consultation, mobile app or remote patient monitoring. Interestingly, only half of providers used telehealth before COVID-19. The same report finds more than one in three, or 37 percent, in the medical community think tech could replace doctors by 2042. Is this fear of AI replacing humans or optimism about how fast digital technologies are advancing? At the same time, projected digital health adoption varies across regions. When will we see AI doctors and psychologists? Asia-Pacific, the Middle East, Africa and Russia are most optimistic about digital doctors. Around half of healthcare professionals in the Asia-Pacific and Middle East regions think digital technologies could replace doctors in the next 20 years, but only around one in three in North America and Europe. These differences may arise from varying public perceptions of digital health. In China in 2019, publicized events in which digital technologies assisted in animal operations, minimally invasive heart surgery and remote brain surgery may have helped public and medical trust in digital health. In medical communities across regions, 40 percent predict there'll soon be digital replacements for psychologists. This may be because of the relative maturity of related technologies like text analysis and natural language processing (NLP.) What will most drive value in healthcare? Kaspersky's report found providers in the Middle East, Asia-Pacific and Latin America think AI and big data will be the biggest driver of value for healthcare by 2045. But North America and Russia think it will be telemedicine and mobile apps. Wearables are valued similarly across regions. These differences might be explained by differences in how regions innovate. In the US, it tends to within an industry: Uber and Lyft for ride sharing and AirBnB for travel. In the Middle East and China on the other hand, super-apps integrate services across industries, such as China's Meituan, providing everything from concert tickets to hotel booking to food delivery. Another factor could be government and business emphasis on AI, machine learning and data analytics. In 2019, I spoke at the AI Everything summit in Dubai, positioned as the largest AI conference in the world. The general level of interest in technologies like AI and uptake in other industries may influence how strongly healthcare professionals and patients welcome these. Taking advantage of digital health optimism How can we use the increased confidence in digital health to accelerate development? I suggest four approaches. 1.    Enhance rather than replace experts Digital health best works side-by-side with physicians, nurses and other care providers. For example, image recognition by machine learning can better detect some cancers. This could free up oncologists and radiologists from routine image-reading to focus on treatment. 2.    Carefully manage and govern data Health data is personal and sensitive. We must pay attention to how we collect and use it. In Kaspersky's survey, over half (52 percent) of frontline telemedicine providers said patients had declined video consultations over privacy and other data concerns. But healthcare systems can also use patient data to improve outcomes and reduce costs. We've seen encouraging results from using big data to improve clinical trial design. 3.    Improve data security across all stakeholders In Kaspersky's survey, around a third of healthcare organizations said they'd had cybersecurity issues, and 30 percent had had data breaches from virtual consultations. Causes likely include outdated IT systems, poor security and not following protocols. We must keep improving security technologies and practices across all stakeholders, like government agencies, insurers and nursing homes. 4.    Encourage collaboration To realize the potential of digital health, we need broader collaboration. To facilitate data sharing, we must share investment to build better data infrastructure and fairly distribute value gained from data. These negotiations will be critical to advancing data-related collaboration in healthcare. The healthcare sector showed its agility and solidarity throughout the COVID-19 pandemic. The ramp-up of telehealth increased provider and patient optimism around digital health. Let's retain the momentum and build healthcare's capability to use technology securely and creatively to improve knowledge, efficiency and patient care.
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