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If you want to imagine the future of healthcare, you can do no better than to read cardiologist and bestselling author Eric Topols trilogy on the subject: The Creative Destruction of Medicine, The Patient Will See You Now, and Deep Medicine.

Deep Medicine bears a paradoxical subtitle: How Artificial Intelligence Can Make Healthcare Human Again. The book describes the growing interaction of human and machine brains. Topol envisions a symbiosis, with people and machines working together to assist patients in ways that neither can do alone. In the process, healthcare providers will shed some of the mind-numbing rote tasks they endure today, giving them more time to focus on patients.

I recorded an interview with Topol in which we discuss his books. The podcast is titled Healthcares Reluctant Revolution because one of Topols themes is that healthcare is moving too slowly to integrate AI and machine learning (ML) into medicinea sluggishness that diminishes the quality and quantity of available care.

The first of Topols books, Creative Destruction, described how technology would transform medicine by digitizing data on individual human beings in great detail. In The Patient Will See You Now, he explored how this digital revolution can allow patients to take greater control over their own health and their own care. With this democratization of care, medicines ancient paternalism could fade. (In 2017, Topol and I co-authored an essay on Anatomy and Atrophy of Medical Paternalism.)

Deep Medicine is qualitatively different from the other two books. It has an almost-mystical quality. Intelligent machines engaging in AI and ML arrive at information in ways even their programmers can barely comprehend, if at all. Topol gives a striking example.

Take retinal scans of a large number of peoplethe sort of scans that your optometrist or ophthalmologist takes. Now, show the scans to the top ophthalmologists in the world and ask for each scan, Is this person a man or a woman? The doctors will answer correctly approximately 50 percent of the time. In other words, they have no idea and could do just as well by tossing a coin. Now, run those same scans through a deep neural network (a type of AI/ML system). The machine will answer correctly around 97 percent of the timefor no known reason.

Topol explains how such technologies can improve care. Today, radiologists spend their days intuitively searching for patterns in x-rays, CT scans, and MRIs. In the future, much of the pattern-searching will be automated (and more accurate), and radiologists (who seldom interact with patients today) will have much greater contact with patients.

Today, dermatologists are relatively few in number, so much of the earlier stages of skin care are done by primary care physicians, who have less ability to determine, say, whether a mole is potentially cancerous. The result can be misdiagnosis, delayed diagnosis, and the unnecessary use of dermatologists time. In the future, primary care doctors will likely screen patients using smart diagnostic tools, thereby wasting less of patients and dermatologists time and diagnosing more accurately.

In Deep Medicine, Topol tells the story of a newborn experiencing seizures that could lead to brain damage or death. Routine diagnostics and medications werent helping. Then, a blood sample was sent to a genomics institute that combed through a vast amount of data in a short time and identified a rare genetic disorder thats treatable through dietary restrictions and vitamins. The child went home, seizure-free, in 36 hours.

Unfortunately, healthcares adoption of such technologies is unduly slow. In our conversation, Topol noted that we have around 150 medical schools, some quite new, and yet they dont have any AI or genomics essentially in their curriculum.

Topol lists some hopes that observers invest in AI: Machines outperforming doctors at all tasks, diagnosing the undiagnosable, treating the untreatable, seeing the unseeable on scans, predicting the unpredictable, classifying the unclassifiable, eliminating workflow inefficiencies, eliminating patient harm, curing cancer, and more.

A realistic sort of optimist, Topol writes: Over time, AI will help propel us toward each of these objectives, but its going to be a marathon without a finish line.

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Artificial Intelligence and the Humanization of Medicine InsideSources - InsideSources

Distribution centers provide a controlled environment that is ideal for testing and proving complex technologies like drones and robots. That's also one reason why DCs are experimenting heavily with Artificial Intelligence (AI).

An independent research survey commissioned by Lucas Systems found that the majority of companies are already using AI in their warehouses and distribution/fulfillment operations. The survey also revealed that operators view cost, complexity, and lack of understanding of how to use AI as key impediments to further investments.

In reality, AI will make it easier and less costly for DCs of all sizes to address warehouse optimization challenges like slotting and workforce planning. And successful use of AI will not require massive investments in data science departments. Here's why.

Good data is a key to effective AI, and DCs are a good environment for collecting and aggregating historical and real-time data. AI is also a natural fit for DC operational challenges that previously required highly-engineered expert systems that are costly to implement and maintain.

AI and machine learning-based solutions reduce those obstacles, and they give DCs better results than current resource and inventory management approaches that rely on Excel, inherited best practices, or simple rules-based decision-making. AI is making advanced optimization practical for smaller operations, and more flexible and cost-effective for larger facilities.

Lucas has identified five key applications for AI in the warehouse today.

Proper product slotting impacts labor productivity, throughput, and accuracy, but doing it well isn't easy. Slotting is both a combinatorial optimization problem (many input factors to consider) and a multiple objective optimization problem (with many goals, sometimes competing). In addition, there are thousands of products and product locations (slots) to consider, and those products and locations may change frequently.Traditional slotting solutions require customized models and extensive engineering, measurement and data collection, both to install and maintain.

AI eliminates much of the engineering work and manual warehouse mapping and data inputs required for traditional slotting systems. AI-based software can learn the spatial characteristics and travel time predictions required for a slotting model based on activity-level data captured in the DC. And the learned model will adapt as conditions change, providing continuous optimization.

Optimal labor allocation is essential to ensuring orders get out on time while eliminating overstaffing and understaffing. In many DCs, supervisors make staff allocation decisions throughout a shift based on the volume of work, deadlines, and current and expected productivity. Good decisions require good data and accurate predictions, which today are often based on each manager's individual experience and skill.

To improve results, machine learning can be applied to predict labor requirements and work completion times. An AI solution can also run simulations to determine how to best complete the work, avoiding delays and ensuring the most efficient use of labor.

Labor management systems using Engineered Labor Standards (ELS) have been around for years. AI can eliminate much of the labor-intensive data collection process required with ELS-based performance management, using learning algorithms to predict the time required to complete tasks.

AI algorithms learn based on real-world performance data collected from within the operation, taking into account a multitude of variables (user, work type, work area, starting travel location, ending travel location, product to be handled, quantity to be handled, etc.).The predicted results and expectations are more accurate and the ML models adjust when operational changes are introduced.

Warehouse workers spend much of their workday traveling within a facility, making travel reduction a key to improved productivity. Automation and robots each eliminate travel, and AI can be used in areas where automation alone is not enough.

AI and machine learning systems use large amounts of process data to 'learn'how to balance priorities and reduce travel through intelligent order batching and pick sequencing. The systems take into account common congestion areas and slow-moving routes. Many DCs have achieved 2x productivity gains in piece picking applications using AI-based travel reduction, and even case pick to pallet operations have demonstrated 20-30 percent productivity gains.

The same tools used to optimize travel for workers can apply to orchestrating people and autonomous mobile robots (AMRs) in an order-picking process. In most pick-to-robot systems today, the robot system optimizes and directs the robots to a location, and a nearby worker delivers one or more picks to the robot based on instructions on a tablet mounted to the machine.

An AI-based execution system can orchestrate and optimize for both the robots'and the pickers'time while also providing means to direct workers independent of the AMRs (using wearable mobile devices rather than robot-mounted tablets).Machine learning algorithms predict where the robots and pickers will be located at a given time, and other algorithms provide input to intelligently organize and sequence the work among people and robots.

In the survey mentioned earlier, the cost was seen as the biggest impediment to AI adoption, and 8 in 10 of the respondents also said their organizations need a better understanding of how AI can be used in the DC.

As outlined above, AI has the potential to reduce the cost and manual engineering time and effort required to implement a range of DC optimization solutions, from slotting to labor performance management. What's more, these new AI-based solutions do not require that companies develop extensive in-house AI expertise.

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5 applications for artificial intelligence in the warehouse and distribution center - Supply Chain Dive

Introduction by Croakey: As with the COVID-19 pandemic, health authorities usually identify epidemics through public health surveillance, but could we do it earlier by being able to mine the vast un-curated public data available to us in this digital age?

Thats the hope and challenge from leading epidemiologist, Professor Raina MacIntyre, who heads the Biosecurity Program at the Kirby Institute, and Arunn Jegan, Advocacy Coordinator at Mdecins Sans Frontires (MSF).

They write below on the potential for harnessing artificial intelligence and the proliferation of the internet and social media for early detection of epidemics, saying that a signal for unusual pneumonia in China could have been detected in November 2019 and that CSIRO research showed that the Ebola epidemic in West Africa could have been detected three months before the World Health Organization was aware of it.

They ask:

Imagine if the COVID-19 pandemic had been detected well before it spread around the world, when there was only a handful of cases contained within a small geographic location?

Readers may also be interested in the series of articles published by the Croakey Conference News Service from the recent World Congress of Epidemiology, with one on innovative Victorian disease surveillance measures and their critical role in the states COVID response to be published soon.

The SARS-COV2 (COVID-19) pandemic has caused devastation around the world, and even in vaccinated populations, it continues to mutate into dangerous variants of concern. With the onset of the Delta variant, we assume the death toll will rise beyond five million in 2021.

This is an epidemic disease, which means it grows exponentially. One case today will be five cases in a few days and then 25 cases and so on. So, time is of the essence, and the sooner you can identify epidemics, the better the prospect of stamping it out and preventing global spread.

Imagine if the COVID-19 pandemic had been detected well before it spread around the world, when there was only a handful of cases contained within a small geographic location?

Isolating cases and tracing and quarantining their contacts may have been enough to stop it spreading.

Exponential growth and time are the enemies we face with epidemic diseases the longer we take to act, the larger the epidemic will become and over a very short period. Just look at the Sydney outbreak which started in Bondi in June 2021.

Recall the West African Ebola epidemic in 2014. It was 67 times the size of the largest previously recorded Ebola outbreak, it reached urban areas, and killed more than 11,300 people.

Ebola outbreaks can kill 25 to 90 percent of those infected. In 2014-15, hundreds of health workers died, decimating the already-struggling healthcare systems of Liberia, Guinea, and Sierra Leone. Medecins Sans Frontieres (MSF) responded in each of these contexts.

In the Ebola outbreak, with fears of a pandemic on the horizon, organisations like the World Health Organization (WHO), MSF, and others supported national health systems by treating and isolating patients; tracing and follow up of patient contacts; raising community awareness of the disease such as how to prevent it and where to seek care; conducting safe burials; proactively detecting new cases; and supporting existing health structures.

When WHO was first notified of Ebola in March 2014, it may have comprised a few 100 cases, but it grew exponentially. By August 2014, the case numbers were in the thousands, and by October over 20,000 cases had occurred.

Furthermore, until only very recently, there were no tools to prevent or treat Ebola. Today a preventive vaccine and curative drugs are available. Imagine how many lives could have been saved if the epidemic had been detected when there were only a handful of cases.

Prior to COVID-19 in 2019, the Ebola epidemic saw the fastest trajectory to development of a vaccine, with Phase 1 trials in Oct 2014 to the approval of this vaccine in Nov 2019. Indeed, the average time was 10-15 years prior to both COVID-19 and Ebola vaccine developments.

For COVID-19, vaccines were developed and ready for use in less than 12 months, but after devastating global consequences of the pandemic and the hundreds of thousands killed in the global north.

In short, it is unwise to rely solely upon vaccines and or/ their development to manage an epidemic, especially in low-resource settings. Non-pharmaceutical interventions such as testing, tracing and measures to reduce contact between people are also important.

We have had a measles vaccine since the 1960s, however the disease rages through the world in epidemic proportions in over 41 countries such the Democratic Republic of Congo and Central African Republic.

The primary reason behind this is a deeply inequitable, and unfair global biomedical system which has unfairly provided for wealthier countries but not low-income countries.

We are seeing it play out with COVID-19, where only 1.8 per cent of people in low-income countries have received one dose, out of 5.4 billion doses administered globally.

With COVID-19, the general public have had a taste of what epidemiologists have known for decades, that strong health surveillance is essential to getting on top of outbreaks and to have any chance of zero elimination strategies, or any suppression strategy for that matter, working.

While the global disparity of vaccination rates persists, what new technologies is Australia investing in to helping communities get on-top of outbreaks and bolster health surveillance?

How are we harnessing artificial intelligence together with the proliferation of the internet and social media for early detection of epidemics?

The usual way we identify epidemics is through public health surveillance which is when labs or doctors notify health authorities of unusual, serious, or notifiable infections.

When lots of these notifications start piling up, or a trend is seen of higher case numbers than usual, the health official may investigate a possible outbreak.

But people talk about illness in their communities, and local news agencies report on unusual outbreaks, long before health officials know about it.

What if we could mine the vast, un-curated public data available to us in this digital age and detect signals of epidemics early?

At UNSW, the EpiWatch observatory does just that, tapping into news reports from around the world, in many different languages, using algorithms and artificial intelligence (AI) to detect early outbreak signals.

We showed that a signal for unusual pneumonia in China could have been detected in November 2019; and CSIRO research showed that the Ebola epidemic in West Africa could have been detected three months before WHO was aware of it.

This is in no way a replacement for in-country based data collection or existing Early Warning, and Alert Response Systems (EWARS). Using AI in epidemiology is an additional tool that uses innovative technologies and has the potential to reach communities who do not have the strongest national health surveillance systems.

It can also overcome censorship of information to detect signals in countries that are withholding outbreak information from the world. Reasons for censorship include fear of impacts on tourism, trade, or other parts of the economy, or political reasons.

Traditionally, declaring epidemics rest solely on the responsibility of governments, but never in human history has there been more attention on virology and epidemiology from the public. Therefore, ensuring that data-collected from the internet follows scientific modelling and surveying has never been more important.

As with most emergent technology using data and information to inform a product, the ethics over use of open-source data and safe-guards will need to be in place on who this empowers. Generally, however, methods such as used by Epiwatch do not utilise identifying or private information.

Moving forward the Kirby Institute at UNSW, with CSIRO Data 61 is exploring with MSF on how best AI can be utilised to detect epidemics as fast as possible and give vulnerable communities in low-income countries a fighting chance when epidemics strike.

Applying our lessons learnt from the COVID-19 pandemic and Ebola, now is the right time for Australia and the humanitarian community to invest in innovative health surveillance systems, and to keep potential epidemics isolated to save lives.

Professor Raina MacIntyre is NHMRC Principal Research Fellow and Professor of Global Biosecurity. She heads the Biosecurity Program at the Kirby Institute, which conducts research in epidemiology, vaccinology, bioterrorism prevention, mathematical modelling, genetic epidemiology, public health and clinical trials in infectious diseases.

Arunn Jegan is Advocacy Coordinator at Mdecins Sans Frontires (MSF) Australia. He is also the Permanent Facilitator for the emergency public health course at Epicentre in Paris. Arunn has worked as Head of Mission and Emergency Coordinator and has worked in Yemen, Syria, Venezuela, Bangladesh for MSF and in Afghanistan, Iraq, Jordan, Lebanon, and Turkey in senior management positions for other international NGOs. He specialises in social research, conflict/political analysis, complex project management, and humanitarian crisis coordination of public health emergencies.

See the Croakey Conference News Service coverage from the World Congress of Epidemiology.

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Harnessing artificial intelligence to help prevent epidemics before they spread - Croakey Health Media

Biobest, a global leader in biocontrol and pollination, has expanded its partnership with ecoation, a pioneer in robotics and artificial intelligence technologies for horticulture. Along with Biobests $10 million investment in ecoation, the companies are also joining forces to develop new integrated pest management (IPM)-related technologies and go global with a commercial alliance.

The Biobest investment follows on the heels of $10 million in public funding for ecoation from the Canadian government and an earlier infusion of $2.5 million from existing investors through a round led by Pallasite Ventures. This combined funding will support a commercial roll-out of ecoations cutting-edge dynamic data harvesting platform based on a combination of deep biology, computer vision, sensor technology, AI, and robotics.

Jean-Marc Vandoorne, Biobest CEO, says ecoations capacity to deploy a broad array of new technologies to deliver practical and economical solutions for the worlds most ambitious greenhouse growers is outstanding.

Their team is deeply committed to make a contribution to sustainability in horticulture and realizes the immense potential for further data-driven advances in biological control and integrated pest management, Vandoorne says. Together we have all it takes to be the best-in-class in IPM in the upcoming era of data-driven greenhouse crop production. Our teams are excited about these joint developments and committed to show growers the path from artificial intelligence to additional income.

Dr. Saber Miresmailli, founder and CEO of ecoation, says the announcement is the beginning of a new chapter.

Besides our shared vision for the future of agriculture, we also share the same values. We are passionate about sustainability and have a relentless drive to provide world class service to our customers, Miresmailli says. We nurture our colleagues to grow and to use their talent to make the world a better place. We believe in collaboration and have the humility to constantly and continuously improve our offerings. This shared vision is a strong foundation for success and Biobests worldwide network will help us make a difference on a global scale.

Continue reading here.

Ecoation is a grower-centric platform that merges deep biology, artificial intelligence, intelligence augmentation, and robotics to create technology that change the way growers produce and protect food. See all author stories here.

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Biobest Makes Investment in Robotics and Artificial Intelligence - Greenhouse Grower

COPENHAGEN, Denmark--(BUSINESS WIRE)--Corti.ai, one of the leading SaaS companies in the fast-growing category of Artificial Intelligence for healthcare, announces a $27 Million Series A round.

The investment was led by Vaekstfonden -The Danish Growth Fund and Chr. Augustinus Fabrik, who joins existing investors Hearcore, Id Invest, and byFounders. The company was founded by Lars Maale and Andreas Cleve in 2016.

Unlike the majority of Artificial Intelligence startups that are pursuing image recognition use cases, Corti has focused on improving the workflow around patient consultations. Corti's machine learning platform can listen in during patient consultations and help to document, code, and quality assure the interaction in real-time, saving time and reducing risk. Corti started working within emergency medicine, supporting emergency calls focused on cardiac arrest and COVID-19 cases but has since then moved into supporting medical staff conducting consultations across healthcare.

Andreas Cleve CEO:Healthcare professionals only have a few minutes with each patient, and these encounters are compromised by keyboards and screens, hated by patients and doctors alike. What we've been able to prove at Corti is that machine learning can be a life-saving tool by offering a new kind of deep listening that will not only improve patient outcomes but also save time and money.

The company's patented technology automatically listens in during patient consultations on phone or video. Here it uses machine learning models to transcribe and analyze thousands of variables within each consultation.

Lars Maale CTO:"Not only is our technology able to document consultations, it automatically compares each patient's symptom description to millions of other patients to offer real-time decision support during the engagement, like nothing else available in the market today."

Since its inception several studies have validated the efficacy of Cortis human-computer partnership and found that Corti can help medical professionals deliver best-in-class results. Research from Copenhagen Emergency Services found that Corti could help reduce the amount of undetected out-of-hospital cardiac arrest cases by more than 40%, with almost no training of the personnel needed.

The new funding comes on top of the $5m seed round raised in 2019, and the company plans to use the money to fuel its expansion into primary care in the US. We will use this $27m raise to accelerate Cortis growth plans further in the coming years, new products will be launched, and we have plans to enter primary care and win the US market for consultations intelligence.

We are very proud of our work in the field of emergency medicine, but already today we are analyzing roughly 250.000 low acuity consultations per month, proving that the technology can be a massive value-add for both telehealth companies, clinical call-centers, and GPs around the world, Lars Maale explains.

The company has won several accolades for its innovations within applied artificial intelligence, including VentureBeat's Best Global AI Innovation 2018" and the "Future Unicorn Award 2020" award by the European Commission for being the most likely next unicorn from the European continent.

"Although we are humbled by the overwhelming feedback we have received, we have a dauntingly ambitious roadmap ahead, and as long as there are patients who need Corti to listen in to get the help they need, we have work to do'', CEO Andreas Cleve explains.

Several investors commented about their decision to back Corti.ai and its consultation intelligence platform:

"The global healthcare system has been tested over the last 18 months, and it has shown some fundamental challenges around availability and access to expertise that needs to be addressed. Robust and ethical technology can help solve some of these critical problems, and we believe Corti is a shining example of a revolutionary technology that can help define the market for artificial intelligence in healthcare.- Rolf Kjrsgaard, CEO Vaekstfonden

We have been following the company for a long time, and we are pleased to contribute to the continued rapid growth. To be able to build a category-defining product with world-class technology that can save human lives is not only commendable, it's also a fantastic opportunity that we are proud to back.- Claus Gregersen, CEO Chr. Augustinus Fabrikker:

About CortiCorti is a Danish health-tech company that has developed a software platform leveraging artificial intelligence to help healthcare personnel during patient consultations. As the consultation progresses, Corti's artificial intelligence is listening in to write notes, search databases, and compare symptom descriptions with millions of historical cases to ensure each patient gets the optimal treatment. The company is among the global leaders in applied artificial intelligence and has been recognized with several awards.

About Chr. Augustinus FabrikkerChr. Augustinus Fabrikker is a more than 270-year-old company that has positioned itself as a long-term and professional owner of Danish companies. This is done with great respect for the fact that ownership and day-to-day management are different disciplines. Chr. Augustinus Fabrikkers philosophy is that of being a committed and loyal owner with a focus on dialogue, trust, and long-term value creation in companies with an international outlook. The ownerships include Tivoli, Gyldendal, Jeudan, Royal Unibrew, Fritz Hansen and Podimo. Chr. Augustinus Fabrikker is a subsidiary of the Augustinus Foundation, which is one of Denmark's major cultural foundations. With a balance of more than DKK40 billion Chr. Augustinus Fabrikker's investments make it possible to reinvest actively in the Danish business community in parallel with contributing to the Augustinus Foundation's philanthropic activities. See more at: http://www.augustinusfabrikker.dk

About VaekstfondenVaeksfonden is the Danish states investment fund. Working in close collaboration with banks and domestic and international investors, Vaekstfonden discovers and develops the companies that Denmark cannot afford to miss out on. The power of innovation, yield to society and responsibility are the three signposts that guide Vaekstfonden in finding and choosing new projects. Since 1992, Vaekstfonden has contributed more than DKK 38 billion to help develop and grow more than 10.000 companies. See more on: https://vf.dk/.

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Corti.ai Raises $27 Million in Series A Funding to Transform Patient Consultations With Artificial Intelligence - Business Wire