Clinical nutritionists won’t be left out of the medical AI revolution, as researchers are exploring use cases for augmented diet optimization, food image recognition, risk prediction and diet pattern analysis.

The state of the science is described in a paper published this month in Current Surgery Reports.

Applications for AI and other digital technologies are “still young, [but] there is much promise for growth and disruption in the future,” write multidisciplinary specialists at UCLA Health, San José State University and the Mayo Clinic.

The authors represent expertise in gastroenterology, molecular and biochemical nutrition, anesthesiology and general internal medicine.

Surveying recent research and literature reviews, lead author Berkeley Limketkai, MD, PhD, of UCLA Health and colleagues home in on the four aforementioned use cases. Here are snapshots of their reports on each.

1. Diet optimization. A machine learning model for predicting blood sugar levels after people eat a meal was significantly better at the task than conventional carbohydrate counting, the authors report. The algorithm’s creators used the tool to compose “good” (low glycemic) and “bad” (high glycemic) diets for 26 participants.

“For the prediction arm, 83% of participants had significantly higher post-prandial glycemic response when consuming the ‘bad’ diet than the ‘good’ diet,” Limketkai and colleagues note. … “This technology has since been commercialized with the Day Two mobile application on the front.”

2. Food image recognition. A primary challenge in alerting dieters to likely nutritional values and risks going by photos snapped on smartphones is the sheer limitlessness of possible foods, the authors point out. An early neural-network model developed at UCLA by Limketkai and colleagues achieved impressive performance in training and validating 131 predefined food categories from more than 222,000 curated food images.

“However, in a prospective analysis of real-world food items consumed in the general population, the accuracy plummeted to 0.26 and 0.49, respectfully,” write the authors of the present paper. “Future refinement of AI for food image recognition would, therefore, benefit on training models with a significantly broader diversity of food items that may have to be adapted to specific cultures.”

3. Risk prediction. Machine learning algorithms beat out conventional techniques at predicting 10-year mortality related to cardiovascular disease in a densely layered analysis of the National Health and Nutrition Examination Survey (NHANES) and the National Death Index.

A conventional model based on proportional hazards, which included age, sex, Black race, Hispanic ethnicity, total cholesterol, high-density lipoprotein cholesterol, systolic blood pressure, antihypertensive medication, diabetes, and tobacco use “appeared to significantly overestimate risk,” Limketkai and co-authors comment. “The addition of dietary indices did not change model performance, while the addition of 24-hour diet recall worsened performance. By contrast, the machine learning algorithms had superior performance than all [conventional] models.”

4. Diet pattern analysis. Here Limketkai et al. look at a prospective study of more than 7,500 pregnant women who self-reported dietary intake approximately three months prior to giving birth. Comparing logistic regression with machine learning for predicting adverse pregnancy outcomes, researchers found logistic regression failed to find an association between undesirable outcomes and suboptimal consumption of fruits and vegetables.

Meanwhile the machine learning model “found that the highest fruit or vegetable consumers had lower risk of preterm birth, small-for-gestational-age birth and pre-eclampsia,” which is a pregnancy complication marked by elevated blood pressure and, in cases, organ damage.

Wrapping their discussion, Limketkai and co-authors reiterate that the widening acceptance and use of digital devices as well as AI have:

The post 4 ways machine learning is fixing to finetune clinical nutrition appeared first on Artificial Intelligence.

Having a hard time boosting your team’s sales for the month? Or are you a business owner who’s considering the use of artificial intelligence in improving your company’s sales? Regardless of your reasons, there’s no denying the extensive impact of AI in the business and marketing industry.

The Role Of Artificial Intelligence In The Workplace

Artificial intelligence (AI) is one of the many products of technological advancements that continue to prove its necessity in human life. Be it in the healthcare industry, education sector, agriculture, business and marketing industry, and in the workplace, AI serves as a beneficial tool in enhancing productivity and quality of work. When it comes to sales, in particular, the use of sales analytics software is said to be beneficial in reducing human error, handling repetitive jobs, and providing digital assistance.

In terms of its use in the workplace, there’s no going around the fact that AI offers tremendous help in managing tasks that are deemed difficult or inconvenient for human employees. Apart from that, it’s also shown to effectively bolster team efficiency and productiveness. While the issue of AI replacing human employees in the workplace is still a debatable topic, you don’t have to worry about these technological tools replacing human workers. Instead, they provide tools to better boost productivity, handle management tasks, and improve sales forecasting.

4 Benefits Of Artificial Intelligence In Sales

Artificial intelligence (AI) isn’t only known to offer convenience and ease in the workplace, it can also be a great help in boosting company lead volumes, improving sales rates, and bolstering overall sales performance. Because of these numerous advantages, sales agents can now channel all their efforts and focus on what matters most—their sales revenue.

Specifically, some of the greatest advantages of AI in terms of company sales include the following:

1. Boosts Customer Experience

AI is crucial in providing sales teams with digital assistance to immediately gain customer insight, better understand customer needs, and manage the customers’ expectations. Such intelligent tools can also be beneficial in building the clients’ trust and loyalty.

In terms of tracking customer experience metrics, AI also helps sales teams effectively monitor customer service and customer satisfaction key performance indicators (KPIs). Artificial Intelligence also makes for an easier process of managing customer activities regarding their product choices, the services they’ve availed of, and their previous engagements with the firm.

As a result, sales teams and representatives can now focus more on improving their sales revenue and promoting personalized interactions with clients. Additionally, AI streamlines and highlights the most productive accounts, which influences sales marketers to prioritize generating leads. With such a complete grasp of the opportunities and their potentials, sales teams are more career-driven and fixated to bolster their sales.

2. Improves Sales Forecasting

Sales forecasting is a crucial part of any business marketing strategy. Not only that, but it’s also an integral part of onboarding processes and the payroll system. Thus, poorly managed sales forecasting can result in the drastic diminishing of a company’s credibility. To avoid such scenarios, leading business industries maximize the use of AIs in increasing sales forecast accuracy and handling pipeline management.

These highly efficient systems pave the way for more opportunities in providing realistic solutions and assisting sales managers to generate and close deals. Such AI-driven insights allow the sales team to tune up their forecasting processes and create little tweaks in their standard work quality to efficiently deliver accurate forecasts.

3. Time-Saving

One of the primary responsibilities of sales teams is to focus on improving their sales revenues and achieving quotas. However, they often have a hard time dealing with time-consuming manual tasks, which significantly decreases their focus on their sales. Generating more leads and handling concerns regarding customer satisfaction scores—one of the key metrics businesses should track—can impede them from accomplishing their responsibilities.

In that aspect, AI serves as a beneficial tool in relieving sales teams of tedious manual work by managing appointments, communication channels, and other fundamental sales activities. Through this alternative, sales teams can effectively focus on selling and establishing good relationships with customers instead of doing manual work. This also makes them more motivated and goal-driven.

4. Enhances Pricing

Aside from helping sales teams promote quality work and boost sales revenue, another notable benefit of AI in the business and marketing industry is its role in the price optimization of items. Particularly, machine-learning technology tracks all sales data, including size, location, and previous deals to come up with a reasonable price.

When it comes to sales marketing, pricing is an integral factor that primarily garners the attention of potential customers and increases the possibilities of closing deals. In that aspect, AI guarantees full protection of corporate margins by incorporating pre-authorized discount limits. Subsequently, optimal pricing boosts customer experience by reducing alternating business negotiations that can slacken the processes and disrupt the smooth flow of business.

Bottom Line

Improving sales revenue and attending to other work responsibilities aren’t as easy as they may seem. While some people can multitask, there’s no guarantee of optimal sales volume for business owners. Hence, the growing need for artificial intelligence. Machine learning technologies can significantly help sales teams in bolstering customer experience, improving sales forecasting, reducing human error, and enhancing pricing.

Through these expert systems, enhancing sales revenue has never been easier.

The post 4 Ways Artificial Intelligence Helps Sales Teams appeared first on Artificial Intelligence.

Many organizations have begun their data science journeys by starting “centers of excellence,” hiring the best data scientists they can and focusing their efforts where there is lots of data. In some respects, this makes good sense — after all, they don’t want to be late to the artificial intelligence or machine learning party. Plus, data scientists want to show off their latest tools.

But is this the best way to deploy this rare resource? For most companies, we think it unlikely. Rather, we advise companies to see data science both more strategically and broadly.

Consider strategic data science. While organizations have relatively few strategic problems, they are of special importance to the company. Even though there may be relatively little data to analyze for strategic problems and “big swing” decisions, companies should bring everything they can to such issues. Data science provides much more value than just big data algorithms — from more clearly formulating the problem, to analyzing what “small data” is available, to experimenting, to creating great graphics. The potential to come up with better insights using data science is enormous. Further, since senior managers must ultimately lead the data science transformation, engaging them in the data helps them more clearly see the benefits and better understand what they must contribute to the transformation.

But data science also must be democratized broadly. If data science is to be truly transformational, everyone must get in on the fun. Restricting data science to only the experts is a limiting proposition. Data science programs that focus on professional data scientists ignore the vast majority of people and business opportunities. For instance, organizations are loaded with problems and data-driven decisions that can be solved and made by small teams of knowledge workers, middle managers, and partners using small amounts of data in two to three months. These individuals, being at the front lines of the organization, already understand the business and don’t need to be taught it as data scientists do. And vendors of various types are now offering a variety of new tools that ease or automate many aspects of data science, including massaging data, creating algorithms, and creating code to deploy a model into production.

While the idea of an organization-wide data science transformation sounds overwhelming, there are ways you can get started. Based on our consulting, conversations with senior leaders, and research, we recommend the following interrelated steps to make data science more strategic and democratic in your company.

Focus on problems with the highest level of strategic benefit.

As previously noted, most organizations focus their data science efforts where they have the most data — even if they don’t mean to. Companies should consider a full range of other criteria, two of which are most important.

First, they must think of the long-term strategic importance of the problem or opportunity. Consider two options at a mid-sized media company: Option 1 involves looking for insights that deepen the user experience using data generated by engagement with its apps; Option 2 involves using data to inform a bid for certain licensing rights, something that comes up every couple of years. There is plenty of data in support of Option 1 — it is certainly important. But even as there is relatively little data in support of Option 2, it is strategic. Bidding too low and losing can do immediate and long-term harm; bidding too high takes away from profit.

Second, they also must consider the probability of project success. By “success” we mean delivering business benefits of equal or greater value than its proponents promised. It takes a lot to meet this standard, from developing a new insight or algorithm to convincing people to act on or use it to building it into the company’s processes and IT systems. Indeed, developing the insight or algorithm is often the easy step, and many such models are never deployed. Sponsors of potential data science projects should make a stone-cold sober evaluation of these factors. While there are no set answers, we think that evaluating projects in this way will lead them to do more small data projects and more-carefully chosen “moonshots.” DBS, Southeast Asia’s largest bank, has largely given up on moonshots after an early failure but is pursuing other small data projects aggressively throughout the bank. Moderna Therapeutics, the creator of a Covid-19 vaccine, has also eschewed moonshots in favor of less ambitious AI and digital projects.

Democratize data science in the organization.

We sometimes ask companies, “Which would you rather have: a newly-minted PhD data scientist or 20 people who can conduct basic analyses in their current jobs?” Almost all opt for the latter. It leads to our second recommendation — namely, develop “citizen data scientists.” There are plenty of good business intelligence tools and, increasingly, automated machine learning tools make it possible for good business analysts to perform quite sophisticated analyses. Royal Bank of Canada, for example has had great success in this regard.

Some companies, such as Eli Lilly and Travelers, take this advice even further. They provide data and analytical literacy programs for all their employees — and much of the content is tailored to the employee’s level and business function. They view it as an essential capability of their employees to understand different types of data, what can be done with them, and how analytics and AI can enable competitive advantage with data. Finally, of course, companies should look for basic data science skills in all their new hires, for all positions.

Re-prioritize data science efforts and reassign data scientists.

A company’s most senior and seasoned data scientists should be redeployed to strategic data science. One function of a center of excellence can be to assess whether scarce data science resources are working on the organization’s most important problems. Other data scientists may be tasked with helping other employees in the company resolve issues as they come up, assisting in the selection of analytical methods and graphics, reviewing projects to make sure results on sound footings, and training large numbers of people. We find the biggest barrier to taking these steps is perspectives that are too narrow. It simply doesn’t occur to most senior leaders that a data scientist might add value in a strategic context. Lower-level business managers may be reluctant to seek help. Finally, data scientists themselves are drawn to problems where there is lots of data.

Develop and communicate a broad vision of data science.

Think about five years from now: How will the company become, as the strategy consultant Ram Charan puts it, a “math house”? How will data and data science be employed throughout the organization? Is it:

• Something you are still exploring
• A tool that is useful from time to time
• A source of competitive advantage
• A fundamental capability deployed throughout the business
• Something in between

There is no right answer — each industry sector and company are different. Still, we think too many companies have kicked the can down the road on this question for far too long. It is time to take it up in earnest.

Managers interested in sports may find that the Dallas Mavericks and Houston Rockets of the National Basketball Association may be role models here — employing data science in everything from player selection, to game-day tactics, to ticket pricing. Both teams not only employed data scientists earlier and in larger numbers than other NBA teams, but they also integrated them into key personnel and on-court decisions. In baseball, the Houston Astros, the Tampa Bay Rays, and now the LA Dodgers are analytically focused across the organization (though somewhat unethically in the case of the Astros).

It may seem obvious that companies should assign their best data scientists to strategic opportunities — even if there is relatively little data — but many don’t. Similarly, it seems highly reasonable to get everyone involved with data science, rather than letting scarce and highly paid data scientists do it all. Our long experience in working with organizations convinces us that, more than anything else, data science is about people and the more strategically and broadly you bring these people and data together, the better results you’ll see.

The post 4 Ways to Democratize Data Science in Your Organization appeared first on Artificial Intelligence.