Ihave a problem. I am a psychiatrist in the 21st century and yet I still evaluate patients the way Freud did a century ago: I sit with a patient and, by carefully observing how and what they say, I expect them to tell me what’s wrong.

The problem isn’t that I speak with and listen to my patients. Every doctor of every speciality does that. Rather, my problem is that I never measure the data I think are most important to my treatment of psychiatric diseases.

Consider how I evaluate a patient for psychosis in the emergency room. When I speak with them, I want to know what their life is like—what’s their day like? What’s on their mind? How social are they? How’s their sleep? These data depend on my patient’s ability to remember, accurately report, make sense of, and tell me about their experience—and further, my treatments depend on my own ability to listen to and make sense of what I’m hearing.

While we speak, I look for things like rapid or disorganized speech, somewhat incongruent facial expressions, or even recurrent ideas that might help me guage their mind’s function. I ask a series of finely-honed questions to poke and prod at their mind, creating a trove of essential clinical data. But my problem is that the only tool I use to gather and understand these data is my own brain. In other words, I leave the vast majority of that data unrecorded, unanalyzed and untapped. This is a problem. Consider what I’d do if this patient has chest pain.

Chest pain is a vague symptom that can be present in anything from heartburn to a panic attack to a heart attack. I would of course, ask them about their chest pain—when did it start? have they had it before? But I would dig deeper than conversation.

Heart rate is important in chest pain. I could put my fingers on their wrist and count out their heartbeats per minute, but I wouldn’t do that—I’d use a calibrated machine. I might carefully ascultate the lub-dub of their heart valves closing, but I would without question measure the flow of electricity through their heart each millisecond with an electrocardiogram. If I wasn’t reassured by these measurements, I’d probably draw some blood to check for protein SOS signals from their heart and call cardiology. Because I take chest pain seriously, in a few short minutes, I’d gather a host of measurements and would know whether their chest pain was caused by a heart attack.

Before decades of public-private partnerships developed the tools I use to evaluate chest pain, clinicians accepted that some data—in this case the essential data that defines the clinical problem of a heart attack—are invisible without technology and essential to provide good clinical care. Yet as a psychiatrist, I continue to ask questions without measuring the data I think are important to define my clinical problems like psychosis, even though the technology exists.

You probably have the most sophisticated behavioral measurement device ever created in your hand. The smartphone boasts a suite of technologies that might dramatically advance my ability to assess and treat my patients. Right now, our smartphones collect data that measure things I already believe are clinically important: what’s on our mind, how social we are, even how we sleep.

In addition to asking “what’s on your mind?” I might—with my patient’s consent and support, of course—analyze their online search history or social media profile, looking for subtle changes in the way they express themselves, changes that, studies have shown, might define an opportunity for us to work more closely together to improve their mental health. I could ask, but also measure.

Right now, I don’t use technology because, frankly, it’s not necessary. I diagnose and bill based on conversations, not measurements. Psychiatric diagnoses—organized before the advent of technology—are without exception based on patterns of symptoms and signs, or what a patient tells me and what I observe. Though psychiatry has tried to better define the diagnosis of, say, schizophrenia, this has backfired. The more we fiddle with our existing framework, the more muddled it becomes: I recently calculated that the latest diagnostic criteria (DSM-5) for schizophrenia describes ~7.6 trillion different patterns of symptoms and signs.

Notwithstanding these barriers, psychiatry has never been working more quickly or more effectively towards the goal of better defining the clinical problems we treat. The National Institute of Mental Health recently announced the Accelerating Medicines Partnership for Schizophrenia (AMP SCZ), an investment of over $82.5 million over five years and one of the largest private-public partnerships in the organization’s history.

For one of the first times at this scale, a band of psychiatrists and researchers from academic hospitals, pharmaceutical companies, and tech companies will combine traditional clinical conversation with measures of brain function, cutting-edge data from smartphones, personal measurement devices and audio-visual recordings.

For example, recording and analyzing a conversation might help clinicians detect subtle changes in the way people string ideas together or refer to themselves. Without technology, these changes would remain invisible even to a skilled clinician, yet studies have shown that they predict the onset of a psychosis episode in at risk patients. Such patients—previously in a grey zone—might have access to more and better treatments, thereby leading to better outcomes.

Of course, none of these technologies will replace the empathic charm and human touch of a skilled clinician. Some clinical data are necessarily bespoke, artfully gathered by a skilled clinician; but not all data are like this. Modern medicine has brought chest pain from heart attacks from routinely fatal to often survivable and even preventable. Progress in evaluating chest pain required decades of fastigious measurement and, crucially, novel treatments to pair with those measurements.

Though technology isn’t a magic bullet, history has shown that the more we harness technology, the better we can define our clinical problems and treat our patients.

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The availability of massive data is driving the FinTech industry to harness the power of the hidden gems that only data analytics can deliver.

The FinTech industry has witnessed a massive shift owing to digital transformation. From banks to e-commerce platforms, astronomical amounts of data are being generated in the form of transactional and non-transactional data.

Ruled by the power of algorithms and data science, it is enabling businesses to spot consumer trends, and empowering them to create real-time growth opportunities. In a fiercely competitive environment like the payments industry, data science approaches have already matured.

Despite the industry being highly regulated, businesses can attain an edge over their competition by leveraging powerful insights unearthed through data science. The availability of massive data is driving the FinTech industry to harness the power of the hidden gems that only data analytics can deliver.

Here are the top three ways in which data science is being leveraged by the FinTech industry:

1. Fraud detection and prevention– The number of frauds, as well as their new mechanisms, make it difficult for traditional rule-based approaches to detect them. A scalable way to keep track of fraud is to use data science. Data science techniques are widely utilized to identify and predict fraudulent financial transactions. Gradient boosting models are a popular choice. If interpretability is an important factor, more simple models like logistic regression could be used, or advanced techniques like Local Interpretable Model-agnostic Explanations (LIME) and Shapley Additive exPlanation (SHAP) could be tapped to explain more complex models. Owing to an exponential rise in the number of daily online transactions, there is a need for FinTech players to place fraud prevention on the top of their agenda. Employing the right mix of predictive analysis, behavioral profiling, and real-time detection, data science can enable financial organizations to keep abreast of new ways of committing fraud with low to no manual intervention in an automated fashion using algorithmic approaches. While fraud detection and prevention are critical aspects that data science can aid, their true potential and capability extend far beyond these functions.
2. Credit scoring models- Assigning a credit score to people who quantify the likelihood of default is an extremely important part of FinTech companies dealing with providing loans. In some emerging economies, people prefer not to have bank accounts, leading to discrepancies in accounting transactional details holistically. This has posed a significant challenge to the FinTech industry to assign them a credit score. Businesses are harnessing the power of data science techniques like profiling based on psychographic surveys to go beyond the traditional credit scoring methods which require a banking history. From geocoding, analyzing SMS messages to psychographic surveys, these data points could serve as a substitute for traditional banking history and might predict likely defaulters. Technologies like machine learning are playing a key role in providing loans to people who are not yet in the formal banking sector.
3. Customer lifetime value models- To grow more, businesses need to sell more, which can be achieved by acquiring new customers. A recent Gartner survey revealed that 44% of CMOs expect marketing budgets to decrease because of COVID-19[i]. This will mean an increased focus on ensuring that customer acquisition costs (CAC) are reduced. With the dynamics of business changing rapidly and revolving around its customers, it is very critical to get to know a customer’s lifetime value (CLV). CLV enables businesses to concentrate their efforts on their best clients. Better their understanding of CLV, the better they can employ their strategies to retain their most profitable customers. Another efficient way to apply this would be to use machine learning models to calculate customer lifetime value (CLTV models). The CLTV can ensure that customers identical to existing customers with a higher CAC than their CLTV are not acquired again.

Today consumers have numerous payment methods at their disposal, there is not a single value-based ecosystem that effectively connects cash, digital, and loyalty rewards today. The FinTech Industry is enormous in its own right, and by employing the advanced methods offered by data science it can scale hitherto unknown heights of growth and profit.

Herein lies a crucial opportunity for businesses to drive engagement, higher customer satisfaction, and elevated experiences.

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Artificial intelligence can drive not only India’s economy but also the world economy

India is not only the world’s most populated nation, but also the youngest country in the world. The country is currently in the developing phase. But it has garnered global interest in terms of the fastest developing nation and digital adoption. This is largely because India has the 3rd largest startup ecosystem in the world, according to Startup India. And every year the number of startups in the country is snowballing. Like other countries, India is also making aggressive efforts to achieve the AI-led digital economy, with its “AIforAll” strategy.

The potential of artificial intelligence is not clandestine for today’s enterprises. It frees up organizations from expensive resources for higher-level tasks. AI reduces the time taken to perform a task, enabling multitasking and easing the workload for existing resources. It facilitates decision-making by making the process faster and smarter. It has mass-market potential and can be deployed in any business scenario, for any work, and in every industry.

With keeping such capabilities in mind, countries across the world have built their AI strategies. “AIforAll” aims at enhancing and empowering human capabilities to address the challenges of accessibility, affordability, scarcity and inconsistency of skilled talent. It is designed for the effective implementation of artificial intelligence initiatives to develop scalable solutions for emerging economies.

As artificial intelligence refers to a machine that mimics human behaviour and ability, it is being used in manufacturing, healthcare, agriculture, education and skilling.

AI Startup Ecosystem in Indian Economy

The IT and ITeS services segment is critical to India’s economy. In FY2020, the domestic revenue of the IT industry was estimated at US$44 billion and export revenue was at US$147 billion. The IT-BPM industry’s revenue was estimated at around US$191 billion, growing at 7.7% year over year. It is expected to reach US$350 billion by 2025. On the other side, revenue from the digital segment is expected to grow by 38% of the total industry revenue by 2025, to reach US$1 trillion (INR 69,89,000 crore) by 2025.

In the adoption of artificial intelligence technology, India is making good progress. A report from Accenture suggests that this technology can likely add US$957 billion, or 15% of India’s current gross value in 2035. This progress seems achievable as the country’s AI strategy emphasizes harnessing collaborations and partnerships, and aspires to ensure prosperity for all.

While India has been ranked second on the Stanford AI Vibrancy Index primarily for its large AI-trained workforce, leading technology institutes like the IITs, IIITs and NITs have the potential to be the cradle of AI researchers and startups. With India’s push for digitization and AI initiatives, private firms now are racing to win big contracts by spending huge capital to develop new technologies and spinning out new AI and data science-based startups.

Today, the country is on the way to be a major contributor to AI-powered solutions that address issues not only in India but around the world. For instance, HyperVerge, a B2B SaaS company, builds AI models for real-time image and video analysis. Its deep learning networks power applications for large businesses in financial services, telecom, energy, security and defense. Similarly, India’s eCommerce giant Flipkart is powering its decisions in ordering, distribution and product pricing on its platform using AI.

For big techies and firms, artificial intelligence can foster growth and profitability while transforming businesses. It can help drive innovation for entrepreneurs and young companies while improving public safety and advancing the quality of lives in society as a whole.

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Amid the Covid-19 pandemic, there’s been a heavier reliance on technology, including the internet of things to facilitate communications amid social distancing and lockdown measures. However, it’s also positively impacting the way medical services are delivered with more advanced technology.

“The Internet of Things (IoT) is revolutionizing the way we live and work,” wrote Alyssa Rapp in a Med Tech Intelligence article. “From tweeting refrigerators to driverless cars, a vast array of physical devices are now connected to the Web.”

“Nowhere is the impact felt as acutely as in the healthcare sector,” Rapp wrote. “This year, IoT deployments will grow faster in the health arena than in any other industry. In fact, according to McKinsey, the so-called ‘Internet of Medical Things’ (IoMT) will have a global economic impact of $1.6 trillion by 2025.”

From robots to artificial intelligence, a number of disruptive technologies are entering the fray to combat Covid-19. That also goes for applying internet technology to medicine.

“Web-enabled medical technology has the potential to be truly transformative,” Rapp added. “To understand why, you only need to consider some of the potential applications. Imagine, for instance, a ‘smart pill’ that, once swallowed, could collect diagnostic information from inside your body, and wirelessly transmit its findings to your doctor. Or how about a wearable ECG that inconspicuously monitors your heart 24/7 and pings an early warning to your doctor days or weeks in advance of a cardiac event?”

Here are a pair of funds to consider:

• Global X Internet of Things ETF (SNSR): seeks to provide investment results that correspond generally to the price and yield performance, before fees and expenses, of the Indxx Global Internet of Things Thematic Index. The fund invests at least 80% of its total assets in the securities of the underlying index. The underlying index is designed to provide exposure to exchange-listed companies in developed markets that facilitate the Internet of Things industry, including companies involved in wearable technology, home automation, connected automotive technology, sensors, networking infrastructure/software, smart metering, and energy control devices.

• Global X Funds – Telemedicine and Digital Health ETF (EDOC): The Global X Telemedicine & Digital Health ETF (EDOC) seeks to invest in companies positioned to benefit from further advances in the field of telemedicine and digital health. This includes companies involved in Telemedicine, Health Care Analytics, Connected Health Care Devices, and Administrative Digitization. The Global X Telemedicine & Digital Health ETF (EDOC) seeks to provide investment results that correspond generally to the price and yield performance, before fees and expenses, of the Solactive Telemedicine & Digital Health Index.

Additionally, for more market trends, visit ETF Trends.

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When it comes to medical ailments and their diagnosis, time is absolutely of the essence. The sooner we’re aware of a developing condition, the better chance we have of treating and ultimately overcoming it. Lately we’re seeing how artificial intelligence is poised to play a greater and greater role in detecting tell-tale signs of disease long before doctors can, with potentially life-saving ramifications.

In terms of machines outperforming humans that excel in certain fields, artificial intelligence has claimed some pretty big scalps of late. This includes toppling professional poker players at Texas hold ’em, outperforming the world’s best players of the ancient Chinese game of Goand mastering the notoriously difficult game of Ms. Pac-Man on Atari.

When we consider this rate of technological progression in the medical realm, things get even more exciting. The reason it holds so much potential in this area is because through machine learning and the power of computing, AI has the ability to look at medical imaging data and health records to spot subtle, yet crucial changes that human clinicians can’t.

“AI is particularly strong in recognizing patterns in data,” Shlomo Berkovsky, associate professor at the Australian Institute of Health Innovation, explains to New Atlas. “For example, patterns in medical imaging like brain scans, X-rays or time series data like ECG or respiratory measurement. It can then map these patterns to past clinical records and outcomes of procedures. Sometimes it can even recognize patterns that cannot be spotted by a human clinician. Thus, it can learn from large volumes of data and support clinicians with automated decision support.”

The latest technology to make headlines in this area comes from the Google-bought and now Alphabet-owned DeepMind, which is purported to accurately predict acute kidney injury 48 hours ahead of our current best diagnosis methods. Acute kidney injury is a life-threatening condition whereby the organ suddenly stops working. It is difficult to detect and affects more than 300,000 people in the US every year, so there are plenty of reasons why doctors would like to pick up on it ahead of time.

The DeepMind team trained its AI on health records from more than 700,000 adult patients, an amount of data far too large to be pored over by mere humans. By considering tidbits such as vital signs, the AI was then able to predict the likelihood of acute kidney injury with 55.8 percent accuracy, two full days before its occurrence. It does so by spotting signs of deterioration much earlier than we currently can, and in more severe cases where patients went on to require kidney dialysis the AI was able to correctly predict acute kidney injury nine times out of 10.

DeepMind describes this advance as its biggest medical breakthrough yet, and while indeed significant, it may only be the tip of the iceberg when it comes to AI and improved medical diagnosis.

Change of heart

Heart trouble is another medical ailment where every second can count, particularly when it results in heart attacks or other serious cardiac events. But by mixing artificial intelligence with heart data gathered through more traditional means, algorithms may be able to tell us early on when something’s not quite right.

Back in January, researchers at the Mayo Clinic published a paper describing a new kind of AI-assisted electrocardiogram (ECG) that could pick up on left ventricular dysfunction, a difficult-to-detect condition that is a major precursor to heart failure. The AI was trained on data from over 600,000 patients and the resulting algorithm was able to detect the condition with around 85 percent accuracy. With more work, this technology promises far cheaper and more accessible diagnosis of a key early sign of impending heart failure.

Today, that same group of researchers has published another paper in The Lancet , detailing their latest breakthrough in using artificial intelligence to detect potential heart issues. The new AI searches through heart scans for signs of atrial fibrillation, or abnormal heartbeat rhythm, which is associated with heightened risk of stroke, heart failure and death.

Subtle changes to the organ’s structure as a result of these irregular heartbeats, such as chamber enlargement and scarring, are difficult to spot through current imaging techniques. But by training artificial intelligence for the task using data from around 180,000 patients, the Mayo Clinic scientists say their algorithm can detect atrial fibrillation with 83 percent accuracy.

Early-stage cancers

Back in 2017, a secretive Chinese startup called Infervision emerged from stealth mode trumpeting a new kind of imaging technology designed primarily to detect lung cancers through CT scans. It did so by building artificial intelligence trained on a huge stock of digital health records into a tool called AI Scholar, which then helped radiologists work through CT scans at triple the speed, while helping reduce the rate of missed cancer diagnoses by around 50 percent.

Earlier this year, a Google technology designed to better model and predict lung cancer was able to outperform certified radiologists, in some cases. The machine learning algorithm was trained on more than 45,000 chest CT scans, some of which featured cancers of various stages. With an ability to detect tiny malignant tissue in the lung nodules that would otherwise go unnoticed, Google says the algorithm was able to detect five percent more cancer cases than a board of six certified radiologists, while reducing false-positives by more than five percent.

Ovarian cancer is another area where AI has shown exciting potential, with a study published in February describing a new system that was able to help clinicians grade the severity of tumors and therefore design better treatments. Back in May, meanwhile, MIT scientists tuned this type of technology to breast cancer, training a deep learning algorithm on thousands of mammograms to produce an AI system that could predict breast cancer risks more accurately than current models, by identifying subtle changes in the breast tissue.

Diseases of the mind

Neurological conditions may not bear the same physical signs as the diseases mentioned above, but that doesn’t mean they are beyond the reach of cutting edge AI technologies. A small study published late last year explored their potential in this area by training a machine learning algorithm on neuroimaging data, among other data points, and seeing how it fared in predicting outcomes for psychosis and depression.

The team found that the AI tool was able to correctly predict social outcomes 83 percent of the time in patients who were at high-risk of psychosis, and 70 percent of the time when predicting the onset of depression. Both of these proved more accurate than clinicians in predicting a patient’s social functioning a year down the track, and though it is a small sample size, it is an interesting glimpse at how the future of mental health treatments may evolve.

Alzheimer’s is another example of where AI could improve outcomes for patients with neurological conditions. In November last year, an international team of scientists published a study detailing a new AI systemthat could detect the onset of Alzheimer’s up to six years earlier than current diagnostic methods.

It built the system by training a machine learning algorithm on around 2,100 brain images, one of the clearer Alzheimer’s diagnostic tools we currently have at our disposal. The AI system was more adept at detecting patterns of glucose uptake in the brain than human clinicians, a metabolic biomarker that can be indicative of the disease.

A look to the future

The AI systems discussed above are all in very early, experimental stages with a lot of further work needed before they enter clinical use. But they do highlight the potential of machine learning as a way of improving patient outcomes, with skin cancers, life expectancy, height and bone density just a few more elements of human health that AI could help track and predict.

Because health data is this type of AI’s stock and trade, things could be about to get a whole lot more interesting. The advent of wearable computers such as smart watches and fitness trackers means that never before have we had access to such an abundance of data on our health and well being, and we’re already seeing how AI might play doctor when things seem awry.

Back in 2017, the FDA approved its first medical attachment for the Apple Watch. The KardiaBand sensor integrates into the watch’s band and acts as a sort of wrist-worn version of an electrocardiogram device, taking 30-second recordings of a wearer’s heartbeat and helping to detect atrial fibrillation, among other things.

One of the features of the band is something called SmartRhythm, which uses AI to monitor the relationship between physical activity, as tracked by the Apple Watch, and the user’s heart rate. If it feels like the two are out of sync, it will automatically suggest the user uses the KardiaBand to perform an ECG. This early mish-mash of AI, medical intervention and discreetly worn wearable computers could very well be a sign of things to come, and one of the interesting paths this tech could take.

“In the future, more and more information collected by wearable technologies will be used for diagnosis purposes,” Berkovsky tells us. “There exists a range of sensors and devices that can monitor patients unobtrusively and transmit the collected data. This data will allow the clinicians to build a more encompassing understanding of the patient’s condition and improve the accuracy of the AI decision support.”

Though there may be a time a lot further down the track when AI displaces your local doctor, for the foreseeable future it is likely to play more of a complementary role.

“It’s important to note that in the near future I’d expect AI to remain a decision support tool, while the diagnosis will still be done by the humans,” says Berkovsky.

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his might seem a particularly bad time to be investing in big tech.

President Trump said Tuesday morning that his administration would look into accusations that Google has been secretly working with the Chinese military. The charge came from Peter Thiel, a PayPal (PYPL – Get Report) co-founder and strong supporter of the president.

On the other hand, Bloomberg reported Tuesday that DeepMind, the artificial-intelligence arm of Alphabet,  (GOOGL – Get Report)  might be on the cusp of a major breakthrough in the way new drugs are discovered.

It’s an important innovation. It’s hiding inside the search giant. And it couldn’t come at a better time.

This business is on to something really big. Using data, machine learning and AI, Alphabet managers are incubating vibrant new businesses with innovative business models. One or more of these will become exciting stand-alone businesses.

Some analysts are already doing sum-of-the-parts analyses and they like what they see.

A Jefferies analyst pegged the value of Waymo, Alphabet’s self-driving-car business, at $250 billion in December 2018, according to a story at Business Insider.

Alphabet’s market capitalization is $798 billion, with units including YouTube, Google Search, Google Cloud, Android, the Nest security camera and peripheral businesses, Google Capital, and Stadia, its new video game streaming service set to launch in November.

Together, these parts are probably worth well over $1 trillion.

Until now, the business opportunity for DeepMind was not even on investors’ radar.

The subsidiary has its roots in DeepMind Technologies, a British AI startup that was making progress teaching computers the quirks of human short-term memory. Alphabet acquired the business in 2014.

Two years later, its custom AlphaGo code was so advanced that it became the first computer program to defeat a human in a match of Go, the ancient Chinese strategy game. That human happened to be Lee Sedol, the 18-time world champion.

At the CASP13 meeting in Mexico in December 2018, DeepMind was at it again. This time its human challengers were the brightest minds in biology. The task was predicting the shapes of proteins.

Understanding these structures is important because they govern how diseases form. The problem is there are more possible protein shapes than there are atoms in the universe, Bloomberg notes.

The math has vexed computational biologists for the past 25 years. They have been trying to build more predictive software models for protein folding, the process that leads to proteins taking three-dimensional shapes.

Despite its limited experience with folding, AlphaFold, DeepMind’s entrant, predicted the most accurate structure for 25 out of 43 proteins, taking the top spot over 98 participating teams, according to a report in the Guardian.

For perspective, the second-place team accurately predicted only three of the 43 proteins.

This does not mean Alphabet has an inside track to the next big drug discovery. It doesn’t work that way. Developing new drugs is both expensive and fraught with regulatory hurdles, patient trials and marketing expenses.

Even then, a 2013 study published by Nature Review Drug Discovery found that only 10% of medicines in development ever reach patients.

The business opportunity is increasing those odds.

In The Future Awakens, a November 2017 research study by Deloitte Center for Health Solutions, analysts posit that by 2022 medicine will be predictive, preventative (based on risk), personalized and participatory.

Computational biologists in hoodies and jeans will build personalized drug treatments based on what they know about a patient’s individual genomic makeup. Behind the scenes, data scientists using A, will comb through algorithmic models, looking for previously unseen biomarkers.

DeepMind has come out of nowhere to be a major player in that ecosystem, and it is hiding inside Alphabet shares, practically for free.

The parent’s stock trades at 21 times forward earnings and 5.6 times sales. These metrics reflect the consensus view that Alphabet is an advertising business, subject to regulatory attacks.

The regulation is coming. That’s true.

But the story of the stock is its valuable pieces. Investors are fretting about a potential breakup of Alphabet. They should be embracing that possibility. It will lead to much higher stock prices as the value of its businesses comes to light.

Growth investors should consider buying Alphabet shares into any material weakness.

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