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AI is Neutral Technology: What May Be Harmful in Social Media Can Help Healthcare – ReadWrite

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AI is Neutral Technology: What May Be Harmful in Social Media Can Help Healthcare - ReadWrite


Netflix’s new “The Social Dilemma” documentary has been eye-opening for millions of viewers (see in: hundustantimes, dotcom), sparking conversation — and concern — about how the algorithms used by social media platforms manipulate human behavior.

Here is: “AI is Neutral Technology: What May be Harmful in Social Media Can Help Healthcare — By Dr. Darren Schulte, MD is Chief Executive Officer at Apixio.

By leveraging artificial intelligence that has become shockingly good at analyzing, predicting, and influencing user behavior. The film asserts that the resulting unintended consequences have created real-life dystopian implications: excessive screen time that causes real-world relationships to suffer, addictive behavior, alarming societal divisiveness, and even higher rates of depression, self-harm, and suicide.

These consequences as users look to social media for validation. Big tech corporations profit enormously by harvesting and analyzing their user data and manipulating their behavior to benefit advertisers.

While the film appears to give machine learning algorithms a bad rap, these algorithms aren’t inherently evil. It all depends upon what the algorithms are trained to do.

In fact, the use of AI algorithms in healthcare has tremendous potential to transform health care by improving individual patient outcomes and overall population health, enabling more personalized medicine, reducing waste and costs, and accelerating the discovery of new treatment and preventative measures.

The same type of algorithms showcased in the Social Dilemma can be trained to analyze data generated by patients, care providers, and devices (like wearables). 

The algorithms can even use surveillance of body functions (like lab tests and vital signs) to provide deeper and more accurate insight into individual health, health-related habits, and behaviors over time.

By combining that individual data with anonymous, aggregated population data, we can discover better treatments, refine clinical guidelines, and discover new therapies to improve overall population health.

  • Improve response to emergent diseases like COVID-19. One of the problems we’ve had with effectively treating COVID-19 patients is that there’s been a lot of experimentation and trial-and-error. However, even the data on the results of those therapies has been slow to propagate across the global medical community.

Hospitals and physicians only have data on the patients that they are treating themselves. With no cohesive system for sharing patient data. Providers in America, for example, have not been able to benefit quickly enough from the knowledge and experience of providers in Asia and Europe — where the virus spread first.

By leveraging AI to mine aggregated medical records from millions of individuals, we could see what treatments have been most effective for specific patient cohorts.

Even further, we could analyze the characteristics of those already infected to see which attributes make one more likely to develop the most severe symptoms. By identifying vulnerable populations faster, we can then take targeted steps to prevent infection and implement the most effective treatments.

As we have seen, the analysis and exchange of this data manually, takes far too long, contributing to the propagation and death toll. With AI, we can surface this knowledge much faster and potentially reduce the impact of the next novel disease.

  • Provide better patient surveillance. Identifying how – and how fast – COVID-19 spreads has also been a significant challenge. Scientists traditionally use a metric called R0 (pronounced “R naught”), a measure of the average number of people infected by one infectious individual.

Using R0 to predict COVID-19’s spread has been problematic for several reasons, including the fact that different groups use different models and data, and asymptomatic individuals can spread the disease without knowing that they are infected.

AI can help resolve this issue to improve patient surveillance by analyzing both medical records of patients who tested positive alongside contact tracing data that indicates the potential for infection. By combining this data and analyzing it at scale, medical authorities can use this insight to determine where to implement aggressive testing programs and more restrictive shelter-in-place measures to slow the spread of disease.

  • Improve the quality of care. Health care providers want to deliver the best quality of care to their patients. But one of the challenges they face is measuring quality and patient outcomes with empirical evidence. With patient data scattered across different sources like electronic health records (EHRs), lab results, imaging studies, it is difficult to aggregate and analyze.

By implementing systems that consolidate this data and allow providers to use AI to mine it for insights, physician practices and hospitals can identify trends among patients and implement quality improvement programs.

For example, if they see that individuals with certain characteristics fail to follow-up on important health concerns, providers can intervene with appointment reminders, transportation resources, provide telehealth options, or other interventions to keep patients engaged in their own care.

On the flip side, insurers are also concerned about care quality and ensuring patients get the best possible outcome at the lowest possible cost.

AI can help insurers track and measure patient outcomes as they move through the care system—from a primary care provider to a specialist to a hospital for surgery and into a rehab facility, for example—and identify providers or treatment protocols that may not be delivering optimal results. Insurers can then work with providers to implement new approaches to improve success rates and overall patient outcomes.

  • Identify and mitigate concerning trends. During a typical patient encounter, doctors only have access to the medical information for the patient in front of them. Consulting their patient history provides a limited view of factors that might indicate declining health. With data scattered across different systems, doctors do not always have all the data they need at hand.

AI can help surface broader indicators that a patient’s health may be declining over time.

By analyzing aggregate data across a large population, AI can show that patients with certain vital signs or trends in their data might be headed toward developing certain conditions, like diabetes or heart disease.

Physicians can use this information as a predictor of potential trouble and begin implementing preventative action. Some solutions can alert physicians to these insights as notifications within the Electronic Health Record (EHR) during the patient encounter. This allows physicians to take swift action to prevent disease progression.

  • Enable personalized medicine. The health care industry has been moving toward personalized medicine for years, aiming to transform the “one-size-fits-all” approach to care into a customized plan for each individual. But this is practically impossible without access to aggregated data and insights that only AI can provide.

Consider the AI social media companies use to create and leverage personas to prompt engagement and drive advertising dollars. If we were to apply the same technique to build health care personas for each person, we could then provide this information to providers (with the patient’s permission).

Providers could then use tools like notifications, nudges, cues, or other communication (just like social media) to elicit positive behavior for better health.

For example, providers could target at-risk patients with prescription reminders, diet recommendations, or other resources relevant to their specific health situation.

  • Reduce diagnostic and treatment errors. Even the best providers can overlook important details and make mistakes, especially with the pressure they are under to squeeze more patients into a typical day.

Just as algorithms can help social platforms surface insights about their audience to woo advertisers, physicians can use algorithms to surface insights to diagnose and treat conditions accurately. For example, AI can highlight confounding conditions or risk factors for patients, allowing doctors to consider the individual’s entire health profile when making decisions.

AI can also aid in surfacing potential drug interactions that could put patients at risk. All of this can substantially lower the risk of errors that cause patients harm, not to mention reduce the risk of malpractice accusations.

The same way algorithms can identify Facebook users who might be interested in a new lawnmower and serve up an appropriate ad; they can help providers identify high-risk patients before they develop costly care needs. By culling through data to identify risk factors, AI allows providers to implement preventative and early intervention strategies.

For example, an algorithm might spot a specific obesity indicator that correlates with the risk for Type II diabetes or identify patients with high blood pressure that are at greater risk of heart attack, stroke, or kidney disease.

These insights can be delivered at the point of care, even during a patient encounter. If a patient displays a specific set of symptoms, as the data is entered into the EHR, the physician is alerted to the risk and can review trends in disease progression or confounding conditions to plot the best course of action.

  • Identify optimal treatment pathways through data-based referrals. Traditionally, when a patient needed to see a specialist, for surgery or physical therapy, for example, physicians typically referred to providers with whom they have existing relationships.

Unfortunately for patients, this does not always mean they get the best care for their unique situation. Does the provider have experience working with patients with co-morbidities? Do they specialize in complex surgeries or more typical procedures?

AI allows providers to refer to the best provider for each patient’s unique needs based on hard evidence of success and proven outcomes, rather than simply based on existing ties.

For example, if a patient with diabetes needs a knee replacement, AI can help primary care providers to identify orthopedic specialists and rehabilitation providers with proven, demonstrably better results in handling patients with this co-existing condition.

  • Reduce spending waste. About 30% of healthcare spending is considered “waste,” totaling up to $935 billion. Nearly $80 billion alone can be attributed to overtreatment or low-value care.

In other words, providers order more tests, services, and procedures that aren’t necessarily the best option—or even necessary at all—mostly in an effort to protect themselves against being accused of not doing enough and to meet insurer’s requirements (e.g., ordering x-rays before an MRI when an injury is clearly soft tissue related or sending patients for multiple repeat mammograms before conducting an ultrasound to evaluate a suspicious lump).

By mining data using algorithms, providers and insurers can focus on using the tests and procedures that demonstrate high value or necessary for specific instances. For example, is it necessary for patients on certain medications to get blood tests every 90 days? Do wellness visits add value to patients?

By looking at what is most effective across the larger population, AI can help point physicians in the right direction earlier, reducing unnecessary diagnostics and placing the patient on the path to better health more quickly.

AI thereby can reduce wasteful spending by identifying diagnostics that are most effective and economical, potentially saving patients and payers millions every year on ineffective tests and treatments.

  • Accelerate drug and treatment discovery. The current pathway to new drugs, vaccines, and treatments is long and arduous. On average, it takes at least ten years for new drugs to go from discovery to marketplace, with trials alone taking as long as seven years on average. For new vaccines, the average time to market is up to 12 years (which puts hope for a COVID-19 vaccine by year’s end into perspective).

One of the reasons the process is so slow is the lack of advanced data and analytics capabilities in the process.

The use of AI to analyze patient and drug performance data could substantially accelerate the time to market for new drugs and vaccines, which could save lives.

Just as the lack of data analytics meant doctors struggled to devise effective COVID-19 protocols, the inability to rapidly analyze trial data and evaluate new use cases for existing drugs prevents patients from getting the treatment they need.

Algorithms can accelerate this analysis and get much-needed medicines into the hands of patients faster.

All this time can add up to a significant cost and take away from time spent in direct, face-to-face time with patients.

AI can help reduce this burden and lower operational costs by automating manual processes like prior authorizations, reducing retrospective chart reviews by surfacing the right data to the right people earlier. The right data, quickly obtainable, will help physicians make better, faster decisions.

These efficiencies enabled by AI, on the administrative side, ultimately lower the cost of health care services for both patients and payers and frees up more resources to improve direct patient care.

The negative use of social media comes when the data influences human behavior bringing negative consequences.

For the most part, technology is neutral. But in the wrong hands with the wrong motives or objectives, the use of algorithms can raise serious ethical questions.

The same algorithms that cause us to feel more anxious, isolated, or depressed when leveraged by social media can also be used to help us heal, stay healthy, and achieve optimal well-being.

The questions are all about the algorithm’s objective and training, testing, and user feedback data that are used by the algorithm.  The reality is that managing both individual and public health in the 21st century requires access to data and insights.

Without data-driven insights, we are just guessing what will work in healthcare and what doesn’t.

Leveraging algorithms to analyze health care data empowers physicians to devise a truly personalized care plan for each individual. The physician can improve the quality of care overall and lower health care costs by tapping into collective insight and knowledge gleaned from millions of patient records.

Image Credit: karolina grabowska; pexels

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Fintech Kennek raises $12.5M seed round to digitize lending

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Google eyed for $2 billion Anthropic deal after major Amazon play


London-based fintech startup Kennek has raised $12.5 million in seed funding to expand its lending operating system.

According to an Oct. 10 tech.eu report, the round was led by HV Capital and included participation from Dutch Founders Fund, AlbionVC, FFVC, Plug & Play Ventures, and Syndicate One. Kennek offers software-as-a-service tools to help non-bank lenders streamline their operations using open banking, open finance, and payments.

The platform aims to automate time-consuming manual tasks and consolidate fragmented data to simplify lending. Xavier De Pauw, founder of Kennek said:

“Until kennek, lenders had to devote countless hours to menial operational tasks and deal with jumbled and hard-coded data – which makes every other part of lending a headache. As former lenders ourselves, we lived and breathed these frustrations, and built kennek to make them a thing of the past.”

The company said the latest funding round was oversubscribed and closed quickly despite the challenging fundraising environment. The new capital will be used to expand Kennek’s engineering team and strengthen its market position in the UK while exploring expansion into other European markets. Barbod Namini, Partner at lead investor HV Capital, commented on the investment:

“Kennek has developed an ambitious and genuinely unique proposition which we think can be the foundation of the entire alternative lending space. […] It is a complicated market and a solution that brings together all information and stakeholders onto a single platform is highly compelling for both lenders & the ecosystem as a whole.”

The fintech lending space has grown rapidly in recent years, but many lenders still rely on legacy systems and manual processes that limit efficiency and scalability. Kennek aims to leverage open banking and data integration to provide lenders with a more streamlined, automated lending experience.

The seed funding will allow the London-based startup to continue developing its platform and expanding its team to meet demand from non-bank lenders looking to digitize operations. Kennek’s focus on the UK and Europe also comes amid rising adoption of open banking and open finance in the regions.

Featured Image Credit: Photo from Kennek.io; Thank you!

Radek Zielinski

Radek Zielinski is an experienced technology and financial journalist with a passion for cybersecurity and futurology.

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Politics

Fortune 500’s race for generative AI breakthroughs

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Deanna Ritchie


As excitement around generative AI grows, Fortune 500 companies, including Goldman Sachs, are carefully examining the possible applications of this technology. A recent survey of U.S. executives indicated that 60% believe generative AI will substantially impact their businesses in the long term. However, they anticipate a one to two-year timeframe before implementing their initial solutions. This optimism stems from the potential of generative AI to revolutionize various aspects of businesses, from enhancing customer experiences to optimizing internal processes. In the short term, companies will likely focus on pilot projects and experimentation, gradually integrating generative AI into their operations as they witness its positive influence on efficiency and profitability.

Goldman Sachs’ Cautious Approach to Implementing Generative AI

In a recent interview, Goldman Sachs CIO Marco Argenti revealed that the firm has not yet implemented any generative AI use cases. Instead, the company focuses on experimentation and setting high standards before adopting the technology. Argenti recognized the desire for outcomes in areas like developer and operational efficiency but emphasized ensuring precision before putting experimental AI use cases into production.

According to Argenti, striking the right balance between driving innovation and maintaining accuracy is crucial for successfully integrating generative AI within the firm. Goldman Sachs intends to continue exploring this emerging technology’s potential benefits and applications while diligently assessing risks to ensure it meets the company’s stringent quality standards.

One possible application for Goldman Sachs is in software development, where the company has observed a 20-40% productivity increase during its trials. The goal is for 1,000 developers to utilize generative AI tools by year’s end. However, Argenti emphasized that a well-defined expectation of return on investment is necessary before fully integrating generative AI into production.

To achieve this, the company plans to implement a systematic and strategic approach to adopting generative AI, ensuring that it complements and enhances the skills of its developers. Additionally, Goldman Sachs intends to evaluate the long-term impact of generative AI on their software development processes and the overall quality of the applications being developed.

Goldman Sachs’ approach to AI implementation goes beyond merely executing models. The firm has created a platform encompassing technical, legal, and compliance assessments to filter out improper content and keep track of all interactions. This comprehensive system ensures seamless integration of artificial intelligence in operations while adhering to regulatory standards and maintaining client confidentiality. Moreover, the platform continuously improves and adapts its algorithms, allowing Goldman Sachs to stay at the forefront of technology and offer its clients the most efficient and secure services.

Featured Image Credit: Photo by Google DeepMind; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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Politics

UK seizes web3 opportunity simplifying crypto regulations

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Deanna Ritchie


As Web3 companies increasingly consider leaving the United States due to regulatory ambiguity, the United Kingdom must simplify its cryptocurrency regulations to attract these businesses. The conservative think tank Policy Exchange recently released a report detailing ten suggestions for improving Web3 regulation in the country. Among the recommendations are reducing liability for token holders in decentralized autonomous organizations (DAOs) and encouraging the Financial Conduct Authority (FCA) to adopt alternative Know Your Customer (KYC) methodologies, such as digital identities and blockchain analytics tools. These suggestions aim to position the UK as a hub for Web3 innovation and attract blockchain-based businesses looking for a more conducive regulatory environment.

Streamlining Cryptocurrency Regulations for Innovation

To make it easier for emerging Web3 companies to navigate existing legal frameworks and contribute to the UK’s digital economy growth, the government must streamline cryptocurrency regulations and adopt forward-looking approaches. By making the regulatory landscape clear and straightforward, the UK can create an environment that fosters innovation, growth, and competitiveness in the global fintech industry.

The Policy Exchange report also recommends not weakening self-hosted wallets or treating proof-of-stake (PoS) services as financial services. This approach aims to protect the fundamental principles of decentralization and user autonomy while strongly emphasizing security and regulatory compliance. By doing so, the UK can nurture an environment that encourages innovation and the continued growth of blockchain technology.

Despite recent strict measures by UK authorities, such as His Majesty’s Treasury and the FCA, toward the digital assets sector, the proposed changes in the Policy Exchange report strive to make the UK a more attractive location for Web3 enterprises. By adopting these suggestions, the UK can demonstrate its commitment to fostering innovation in the rapidly evolving blockchain and cryptocurrency industries while ensuring a robust and transparent regulatory environment.

The ongoing uncertainty surrounding cryptocurrency regulations in various countries has prompted Web3 companies to explore alternative jurisdictions with more precise legal frameworks. As the United States grapples with regulatory ambiguity, the United Kingdom can position itself as a hub for Web3 innovation by simplifying and streamlining its cryptocurrency regulations.

Featured Image Credit: Photo by Jonathan Borba; Pexels; Thank you!

Deanna Ritchie

Managing Editor at ReadWrite

Deanna is the Managing Editor at ReadWrite. Previously she worked as the Editor in Chief for Startup Grind and has over 20+ years of experience in content management and content development.

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