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DISS Participa de la Convención de Hospitales de Puerto Rico 2019

San Juan – 11 Noviembre 2019 – Del 29 octubre al 2 de noviembre 2019 fue celebrada la Convención de la Asociación de Hospitales de Puerto Rico, en el Hotel Sheraton, San Juan. Con la asistencia de mas de 150 profesionales de servicios de salud .

Este magno evento contó con una importante agenda educativa, con muchos temas enfocados a la administración hospitalaria y manejos administrativos, además de medidas y acciones de prevención de enfermedades y desarrollos organizacionales muy oportunas para la situación actual del país.

En esta oportunidad DISS fue uno de los patrocinadores premiados por su constancia en participación por más de 10 años en la Convención de la Asociación de Hospitales de Puerto Rico.

Para DISS es muy importante fomentar la educación continua de los profesionales relacionados con el sector salud y por esto nos mantenemos en constante participación y desarrollo de eventos científicos y académicos, siempre buscando proveer información y equipos con tecnología de punta que mejoren la práctica médica diaria y favorezcan la calidad de vida de la comunidad.

DISS es una empresa dedicada a la venta de soluciones de imágenes médicas de alta calidad, siendo los representantes exclusivos de marcas como SIEMENS para Puerto Rico, República Dominicana y Guatemala. Están comprometidos con brindar un servicio extraordinario a sus clientes, siempre buscando ser líderes en los segmentos en los que participan. Cuentan con departamentos especializados de aplicaciones clínicas, gerencia de proyectos, ingeniería y educación continua.

 

Contacto:

Emily González Izquierdo

Directora de Marketing Comunicaciones y PR

E-mail: egonzalez@diss.com

Cel: 809-519-1917

Pagina web: www.diss.com

 

Solutions for Individual Patients

In order to tailor cardiological treatment much more closely to the individual patient, scientists at the Clinic for Cardiology at Heidelberg University Hospital, Germany, are working to develop a digital twin of the heart. Professor Benjamin Meder, Deputy Medical Director of the Department and Head of the Institute for Cardiomyopathies in Heidelberg, reveals how far the work has advanced.

Professor Meder, just what exactly is a digital twin of the heart?

The term ‘twin’ is symbolic for the representation of a patient that closely reflects the structure and function of his or her heart and potential heart disease. The digital twin is an incredibly individualized approach in medicine. And we not only want to use it for diagnosis, but also for testing the safety of treatments. For example, we could test certain cardiac drugs on a digital twin’s heart to see how effective they are. Or we could digitally simulate cardiac catheter interventions and heart surgery in advance and only go ahead with them if there’s a realistic chance of success. For this to work, we need to simulate the biology of the real heart as accurately as possible, which is why molecular information, alongside clinical investigations, is certainly useful.

The digital twin is an incredibly individualized approach in medicine.Professor Benjamin Meder, Deputy Medical Director and Head of the Institute for Cardiomyopathies, Heidelberg University Hospital, Germany

So can a digital twin of the heart go beyond other models of cardiovascular risk prediction?

Cardiology uses various risk models. For example, measuring the highly sensitive troponin or the ejection fraction of the left ventricle already tells us a great deal about a person’s risk of disease. But in individual patients, this simplification to a few variables is often not meaningful enough to predict the effectiveness of a therapy. In the future, more complex models such as a digital twin of the heart could allow us to test a variety of therapy options in advance. What’s important here is that we find solutions that suit an individual and not just general statements that match a risk group.

You’ve been working on this project for around six years. What were the major challenges?

In addition to all the technical challenges, we need to ensure reliable cooperation between different areas of expertise. This includes the cooperation between industry and the university. I think that in the future we’ll need to work together much more closely when it comes to Artificial Intelligence, or AI, because each field has unique capabilities and offers its own approach to solutions. The strengths of physicians lie in the recognition of medical needs and ethical questions, in the precise path to a solution without disregarding risks. In Heidelberg, we also have decades of expertise in molecular cardiology and understand very well how diseases progress from cause to organ failure. The strengths of industry lie in knowing how to implement technology.

What specific applications for the digital twin are you working on?

First of all, thanks to AI technologies, better diagnostic methods that can integrate a huge amount of data will be available in the near future. Algorithms will also help us track the distinct progression of heart diseases and adapt those learnings to each new patient. Moreover, I think it’s important, especially in the simulation of procedures using heart catheters or surgery, to avoid risks and carry out the intervention as good as possible. When you perform a heart operation or intervention, everything must be just right. You don’t have a second and third chance like with repairing a car, so therapies with a certain risk have to be planned in advance and, ideally, simulated for outcome. Already today, an interventional cardiologist mentally simulates an upcoming intervention. But wouldn’t it be great if this ‘biological simulator’ always had the same quality? To get closer to this, Heidelberg has now announced an opening for a professorship in ‘Artificial Intelligence in Cardiovascular Medicine’ and established the ‘Informatics for Life’ program, which brings together a large number of talented cardiac and computer researchers – and hopefully also the best companies as partners.

What’s the current status of the project? Have algorithms already been developed for certain predictions or simulations?

We’ll be building a new cardiac center in Heidelberg that will not only provide excellent cardiac medicine, but also serve exactly that purpose. Data should be used in the patient’s interest, in a protected and trustworthy environment to develop cardiac medicine 4.0. Only companies with the same high standards can become partners in this set-up. One specific algorithm that we’re already evaluating concerns the prediction of cardiac resynchronization therapy (CRT). This is, so to speak, our pilot project, and we’re working on it together with Siemens Healthineers – as presented recently at a conference at the renowned Isaac Newton Institute in Cambridge. The challenge with CRT is that we haven’t been able to accurately characterize the patients who benefit from this treatment. As a result, patients receive implants without much benefit, and patients who might benefit aren’t receiving them or receive them too late. Here we want to use the digital twin to make sure we can treat patients in a more targeted way. The first study on this question is currently in data evaluation. We hope that we’ll be able to publish the results soon.

We want to use the digital twin to make sure we can treat patients in a more targeted way.Professor Benjamin Meder, Deputy Medical Director and Head of the Institute for Cardiomyopathies, Heidelberg University Hospital, Germany

Are there specific challenges to the clinical application of such algorithms? And what implications do they have for clinical trials?

Clinical trials allow the objective testing of new procedures and must precede any routine treatment. But in the age of precision medicine, the studies will look different, in particular with great importance attached to understanding the individual study participant as best as we can. The approach of the digital twin alone helps in this respect. In fact, I’d argue that there isn’t a comparable approach worldwide that can better understand the heart diseases of individual patients. Hopefully the next few years will show that this can make a significant contribution to cardiac health.

Solutions for Individual Patients

In order to tailor cardiological treatment much more closely to the individual patient, scientists at the Clinic for Cardiology at Heidelberg University Hospital, Germany, are working to develop a digital twin of the heart. Professor Benjamin Meder, Deputy Medical Director of the Department and Head of the Institute for Cardiomyopathies in Heidelberg, reveals how far the work has advanced.

Professor Meder, just what exactly is a digital twin of the heart?

The term ‘twin’ is symbolic for the representation of a patient that closely reflects the structure and function of his or her heart and potential heart disease. The digital twin is an incredibly individualized approach in medicine. And we not only want to use it for diagnosis, but also for testing the safety of treatments. For example, we could test certain cardiac drugs on a digital twin’s heart to see how effective they are. Or we could digitally simulate cardiac catheter interventions and heart surgery in advance and only go ahead with them if there’s a realistic chance of success. For this to work, we need to simulate the biology of the real heart as accurately as possible, which is why molecular information, alongside clinical investigations, is certainly useful.

The digital twin is an incredibly individualized approach in medicine.Professor Benjamin Meder, Deputy Medical Director and Head of the Institute for Cardiomyopathies, Heidelberg University Hospital, Germany

So can a digital twin of the heart go beyond other models of cardiovascular risk prediction?

Cardiology uses various risk models. For example, measuring the highly sensitive troponin or the ejection fraction of the left ventricle already tells us a great deal about a person’s risk of disease. But in individual patients, this simplification to a few variables is often not meaningful enough to predict the effectiveness of a therapy. In the future, more complex models such as a digital twin of the heart could allow us to test a variety of therapy options in advance. What’s important here is that we find solutions that suit an individual and not just general statements that match a risk group.

You’ve been working on this project for around six years. What were the major challenges?

In addition to all the technical challenges, we need to ensure reliable cooperation between different areas of expertise. This includes the cooperation between industry and the university. I think that in the future we’ll need to work together much more closely when it comes to Artificial Intelligence, or AI, because each field has unique capabilities and offers its own approach to solutions. The strengths of physicians lie in the recognition of medical needs and ethical questions, in the precise path to a solution without disregarding risks. In Heidelberg, we also have decades of expertise in molecular cardiology and understand very well how diseases progress from cause to organ failure. The strengths of industry lie in knowing how to implement technology.

What specific applications for the digital twin are you working on?

First of all, thanks to AI technologies, better diagnostic methods that can integrate a huge amount of data will be available in the near future. Algorithms will also help us track the distinct progression of heart diseases and adapt those learnings to each new patient. Moreover, I think it’s important, especially in the simulation of procedures using heart catheters or surgery, to avoid risks and carry out the intervention as good as possible. When you perform a heart operation or intervention, everything must be just right. You don’t have a second and third chance like with repairing a car, so therapies with a certain risk have to be planned in advance and, ideally, simulated for outcome. Already today, an interventional cardiologist mentally simulates an upcoming intervention. But wouldn’t it be great if this ‘biological simulator’ always had the same quality? To get closer to this, Heidelberg has now announced an opening for a professorship in ‘Artificial Intelligence in Cardiovascular Medicine’ and established the ‘Informatics for Life’ program, which brings together a large number of talented cardiac and computer researchers – and hopefully also the best companies as partners.

What’s the current status of the project? Have algorithms already been developed for certain predictions or simulations?

We’ll be building a new cardiac center in Heidelberg that will not only provide excellent cardiac medicine, but also serve exactly that purpose. Data should be used in the patient’s interest, in a protected and trustworthy environment to develop cardiac medicine 4.0. Only companies with the same high standards can become partners in this set-up. One specific algorithm that we’re already evaluating concerns the prediction of cardiac resynchronization therapy (CRT). This is, so to speak, our pilot project, and we’re working on it together with Siemens Healthineers – as presented recently at a conference at the renowned Isaac Newton Institute in Cambridge. The challenge with CRT is that we haven’t been able to accurately characterize the patients who benefit from this treatment. As a result, patients receive implants without much benefit, and patients who might benefit aren’t receiving them or receive them too late. Here we want to use the digital twin to make sure we can treat patients in a more targeted way. The first study on this question is currently in data evaluation. We hope that we’ll be able to publish the results soon.

We want to use the digital twin to make sure we can treat patients in a more targeted way.Professor Benjamin Meder, Deputy Medical Director and Head of the Institute for Cardiomyopathies, Heidelberg University Hospital, Germany

Are there specific challenges to the clinical application of such algorithms? And what implications do they have for clinical trials?

Clinical trials allow the objective testing of new procedures and must precede any routine treatment. But in the age of precision medicine, the studies will look different, in particular with great importance attached to understanding the individual study participant as best as we can. The approach of the digital twin alone helps in this respect. In fact, I’d argue that there isn’t a comparable approach worldwide that can better understand the heart diseases of individual patients. Hopefully the next few years will show that this can make a significant contribution to cardiac health.

Solutions for Individual Patients

In order to tailor cardiological treatment much more closely to the individual patient, scientists at the Clinic for Cardiology at Heidelberg University Hospital, Germany, are working to develop a digital twin of the heart. Professor Benjamin Meder, Deputy Medical Director of the Department and Head of the Institute for Cardiomyopathies in Heidelberg, reveals how far the work has advanced.

Professor Meder, just what exactly is a digital twin of the heart?

The term ‘twin’ is symbolic for the representation of a patient that closely reflects the structure and function of his or her heart and potential heart disease. The digital twin is an incredibly individualized approach in medicine. And we not only want to use it for diagnosis, but also for testing the safety of treatments. For example, we could test certain cardiac drugs on a digital twin’s heart to see how effective they are. Or we could digitally simulate cardiac catheter interventions and heart surgery in advance and only go ahead with them if there’s a realistic chance of success. For this to work, we need to simulate the biology of the real heart as accurately as possible, which is why molecular information, alongside clinical investigations, is certainly useful.

The digital twin is an incredibly individualized approach in medicine.Professor Benjamin Meder, Deputy Medical Director and Head of the Institute for Cardiomyopathies, Heidelberg University Hospital, Germany

So can a digital twin of the heart go beyond other models of cardiovascular risk prediction?

Cardiology uses various risk models. For example, measuring the highly sensitive troponin or the ejection fraction of the left ventricle already tells us a great deal about a person’s risk of disease. But in individual patients, this simplification to a few variables is often not meaningful enough to predict the effectiveness of a therapy. In the future, more complex models such as a digital twin of the heart could allow us to test a variety of therapy options in advance. What’s important here is that we find solutions that suit an individual and not just general statements that match a risk group.

You’ve been working on this project for around six years. What were the major challenges?

In addition to all the technical challenges, we need to ensure reliable cooperation between different areas of expertise. This includes the cooperation between industry and the university. I think that in the future we’ll need to work together much more closely when it comes to Artificial Intelligence, or AI, because each field has unique capabilities and offers its own approach to solutions. The strengths of physicians lie in the recognition of medical needs and ethical questions, in the precise path to a solution without disregarding risks. In Heidelberg, we also have decades of expertise in molecular cardiology and understand very well how diseases progress from cause to organ failure. The strengths of industry lie in knowing how to implement technology.

What specific applications for the digital twin are you working on?

First of all, thanks to AI technologies, better diagnostic methods that can integrate a huge amount of data will be available in the near future. Algorithms will also help us track the distinct progression of heart diseases and adapt those learnings to each new patient. Moreover, I think it’s important, especially in the simulation of procedures using heart catheters or surgery, to avoid risks and carry out the intervention as good as possible. When you perform a heart operation or intervention, everything must be just right. You don’t have a second and third chance like with repairing a car, so therapies with a certain risk have to be planned in advance and, ideally, simulated for outcome. Already today, an interventional cardiologist mentally simulates an upcoming intervention. But wouldn’t it be great if this ‘biological simulator’ always had the same quality? To get closer to this, Heidelberg has now announced an opening for a professorship in ‘Artificial Intelligence in Cardiovascular Medicine’ and established the ‘Informatics for Life’ program, which brings together a large number of talented cardiac and computer researchers – and hopefully also the best companies as partners.

What’s the current status of the project? Have algorithms already been developed for certain predictions or simulations?

We’ll be building a new cardiac center in Heidelberg that will not only provide excellent cardiac medicine, but also serve exactly that purpose. Data should be used in the patient’s interest, in a protected and trustworthy environment to develop cardiac medicine 4.0. Only companies with the same high standards can become partners in this set-up. One specific algorithm that we’re already evaluating concerns the prediction of cardiac resynchronization therapy (CRT). This is, so to speak, our pilot project, and we’re working on it together with Siemens Healthineers – as presented recently at a conference at the renowned Isaac Newton Institute in Cambridge. The challenge with CRT is that we haven’t been able to accurately characterize the patients who benefit from this treatment. As a result, patients receive implants without much benefit, and patients who might benefit aren’t receiving them or receive them too late. Here we want to use the digital twin to make sure we can treat patients in a more targeted way. The first study on this question is currently in data evaluation. We hope that we’ll be able to publish the results soon.

We want to use the digital twin to make sure we can treat patients in a more targeted way.Professor Benjamin Meder, Deputy Medical Director and Head of the Institute for Cardiomyopathies, Heidelberg University Hospital, Germany

Are there specific challenges to the clinical application of such algorithms? And what implications do they have for clinical trials?

Clinical trials allow the objective testing of new procedures and must precede any routine treatment. But in the age of precision medicine, the studies will look different, in particular with great importance attached to understanding the individual study participant as best as we can. The approach of the digital twin alone helps in this respect. In fact, I’d argue that there isn’t a comparable approach worldwide that can better understand the heart diseases of individual patients. Hopefully the next few years will show that this can make a significant contribution to cardiac health.

Why I Stopped Worrying And Love The Intelligent Machine

Greg Freiherr

The future will always be just that…until it becomes the present. That is especially true when the future involves science. What if science fiction has a way of turning – suddenly – into science fact. Like flying machines and spacecraft. One day they were theoretical. The next they were happening.

At the annual meeting of the Healthcare Information and Management Systems Society, or HIMSS, in February, companies told me how they had for years been building smart algorithms. One called them the secret sauce of automation. Their software, they told me, had been running for more than a decade on the graphical processing units, or GPUs, that are now recognized as indispensable to artificially intelligent machines.

The “term du jour”

Only in the last couple years has it been OK for these companies to say they were building artificial intelligence, widely known simply as AI, into their machines. Not because AI is scary, although fear is one of the emotions stirred by thinking machines. But because it would not have been credible to say so. That has changed. Today, if you want your company to be seen as a technology leader, you’d better at least be looking into AI. AI has become the term du jour. But it won’t be for long. Why? Because the term is steeped in hype.  Make no mistake. Artificial intelligence will continue, but under a different name. Many equipment developers are already opting for the more precise term “Machine Learning,” or ML. This denotes the processing of data in decidedly nonhuman ways. Typically, ML is used when the technology is being applied not as an end but as a means to something greater. Machine Learning is part of automation, which itself has been used for many years by developers of imaging equipment. As such, it is the way to achieve this cornerstone of value-based medicine with its trifecta of raison d’etres – efficiency, cost effectiveness and patient benefit.

How Machines Learn

Through a process called deep learning, algorithms discovered what distinguishes a dog from a cat. In the world of medical imaging, they are learning to distinguish healthy from abnormal…what scan slices to load for a physician to make a diagnosis…how to position a patient to get the best image with the least radiation.

Because unsupervised deep learning allows machines to figure out the rules on their own, they might “see” things that people don’t. This has given some people pause. Questions arise as to whether physicians or patients should trust them. But we’ve gone down a similar path before.
I used to tune my own car using a strobe light and a timing chain. I couldn’t do that with modern cars, which must be taken to mechanics who tune them using computers. Yet these cars go further between tune-ups; get better mileage; cost less to run; and make us safer. In other words, we have learned to have faith in the machines we create.

But – importantly – the goals behind machine learning must be determined by people. People – whether they are physicians or patients – must hold the reins.Greg Freiherr

With the ability to learn, machines can do what humans cannot – they can make sense of volumes of data so enormous, so complex, that humans have no chance of understanding them. From machine learning may come ways to achieve efficiencies and solutions that people alone could not fathom. But – importantly – the goals behind machine learning must be determined by people. People – whether they are physicians or patients – must hold the reins. Under such a condition, intelligent machines would not, by their own volition, present an existential risk to humanity. It won’t matter how much data intelligent machines process or how fast they do it. They will be directed to apply their incredible processing power to do what people cannot.

Why Docs And Patients Have To Be On Their Toes

Like cars that go many times faster than humans can run; like airplanes that travel many times faster than cars; thinking machines will think of things that are beyond the capability of people. And, like cars and airplanes, they will sometimes crash. But, if past experience is a guide, designers will become more careful in their direction. And the flaws will be fixed. Thinking machines will continue to think – and they will do so more wisely.Physicians should not be asked to – nor should they ever – blindly accept the conclusions of thinking machines. But they must know enough about smart machines to spot the signs of trouble – and know when adjustments are needed.

About the Author

Greg Freiherr is a contributing editor to Imaging Technology News, or ITN. Over the past three decades, he has served as a business and technology editor for publications in medical imaging, as well as a consultant to industry, academia, and financial institutions.

Why I Stopped Worrying And Love The Intelligent Machine

Greg Freiherr

The future will always be just that…until it becomes the present. That is especially true when the future involves science. What if science fiction has a way of turning – suddenly – into science fact. Like flying machines and spacecraft. One day they were theoretical. The next they were happening.

At the annual meeting of the Healthcare Information and Management Systems Society, or HIMSS, in February, companies told me how they had for years been building smart algorithms. One called them the secret sauce of automation. Their software, they told me, had been running for more than a decade on the graphical processing units, or GPUs, that are now recognized as indispensable to artificially intelligent machines.

The “term du jour”

Only in the last couple years has it been OK for these companies to say they were building artificial intelligence, widely known simply as AI, into their machines. Not because AI is scary, although fear is one of the emotions stirred by thinking machines. But because it would not have been credible to say so. That has changed. Today, if you want your company to be seen as a technology leader, you’d better at least be looking into AI. AI has become the term du jour. But it won’t be for long. Why? Because the term is steeped in hype.  Make no mistake. Artificial intelligence will continue, but under a different name. Many equipment developers are already opting for the more precise term “Machine Learning,” or ML. This denotes the processing of data in decidedly nonhuman ways. Typically, ML is used when the technology is being applied not as an end but as a means to something greater. Machine Learning is part of automation, which itself has been used for many years by developers of imaging equipment. As such, it is the way to achieve this cornerstone of value-based medicine with its trifecta of raison d’etres – efficiency, cost effectiveness and patient benefit.

How Machines Learn

Through a process called deep learning, algorithms discovered what distinguishes a dog from a cat. In the world of medical imaging, they are learning to distinguish healthy from abnormal…what scan slices to load for a physician to make a diagnosis…how to position a patient to get the best image with the least radiation.

Because unsupervised deep learning allows machines to figure out the rules on their own, they might “see” things that people don’t. This has given some people pause. Questions arise as to whether physicians or patients should trust them. But we’ve gone down a similar path before.
I used to tune my own car using a strobe light and a timing chain. I couldn’t do that with modern cars, which must be taken to mechanics who tune them using computers. Yet these cars go further between tune-ups; get better mileage; cost less to run; and make us safer. In other words, we have learned to have faith in the machines we create.

But – importantly – the goals behind machine learning must be determined by people. People – whether they are physicians or patients – must hold the reins.Greg Freiherr

With the ability to learn, machines can do what humans cannot – they can make sense of volumes of data so enormous, so complex, that humans have no chance of understanding them. From machine learning may come ways to achieve efficiencies and solutions that people alone could not fathom. But – importantly – the goals behind machine learning must be determined by people. People – whether they are physicians or patients – must hold the reins. Under such a condition, intelligent machines would not, by their own volition, present an existential risk to humanity. It won’t matter how much data intelligent machines process or how fast they do it. They will be directed to apply their incredible processing power to do what people cannot.

Why Docs And Patients Have To Be On Their Toes

Like cars that go many times faster than humans can run; like airplanes that travel many times faster than cars; thinking machines will think of things that are beyond the capability of people. And, like cars and airplanes, they will sometimes crash. But, if past experience is a guide, designers will become more careful in their direction. And the flaws will be fixed. Thinking machines will continue to think – and they will do so more wisely.Physicians should not be asked to – nor should they ever – blindly accept the conclusions of thinking machines. But they must know enough about smart machines to spot the signs of trouble – and know when adjustments are needed.

About the Author

Greg Freiherr is a contributing editor to Imaging Technology News, or ITN. Over the past three decades, he has served as a business and technology editor for publications in medical imaging, as well as a consultant to industry, academia, and financial institutions.

Why I Stopped Worrying And Love The Intelligent Machine

Greg Freiherr

The future will always be just that…until it becomes the present. That is especially true when the future involves science. What if science fiction has a way of turning – suddenly – into science fact. Like flying machines and spacecraft. One day they were theoretical. The next they were happening.

At the annual meeting of the Healthcare Information and Management Systems Society, or HIMSS, in February, companies told me how they had for years been building smart algorithms. One called them the secret sauce of automation. Their software, they told me, had been running for more than a decade on the graphical processing units, or GPUs, that are now recognized as indispensable to artificially intelligent machines.

The “term du jour”

Only in the last couple years has it been OK for these companies to say they were building artificial intelligence, widely known simply as AI, into their machines. Not because AI is scary, although fear is one of the emotions stirred by thinking machines. But because it would not have been credible to say so. That has changed. Today, if you want your company to be seen as a technology leader, you’d better at least be looking into AI. AI has become the term du jour. But it won’t be for long. Why? Because the term is steeped in hype.  Make no mistake. Artificial intelligence will continue, but under a different name. Many equipment developers are already opting for the more precise term “Machine Learning,” or ML. This denotes the processing of data in decidedly nonhuman ways. Typically, ML is used when the technology is being applied not as an end but as a means to something greater. Machine Learning is part of automation, which itself has been used for many years by developers of imaging equipment. As such, it is the way to achieve this cornerstone of value-based medicine with its trifecta of raison d’etres – efficiency, cost effectiveness and patient benefit.

How Machines Learn

Through a process called deep learning, algorithms discovered what distinguishes a dog from a cat. In the world of medical imaging, they are learning to distinguish healthy from abnormal…what scan slices to load for a physician to make a diagnosis…how to position a patient to get the best image with the least radiation.

Because unsupervised deep learning allows machines to figure out the rules on their own, they might “see” things that people don’t. This has given some people pause. Questions arise as to whether physicians or patients should trust them. But we’ve gone down a similar path before.
I used to tune my own car using a strobe light and a timing chain. I couldn’t do that with modern cars, which must be taken to mechanics who tune them using computers. Yet these cars go further between tune-ups; get better mileage; cost less to run; and make us safer. In other words, we have learned to have faith in the machines we create.

But – importantly – the goals behind machine learning must be determined by people. People – whether they are physicians or patients – must hold the reins.Greg Freiherr

With the ability to learn, machines can do what humans cannot – they can make sense of volumes of data so enormous, so complex, that humans have no chance of understanding them. From machine learning may come ways to achieve efficiencies and solutions that people alone could not fathom. But – importantly – the goals behind machine learning must be determined by people. People – whether they are physicians or patients – must hold the reins. Under such a condition, intelligent machines would not, by their own volition, present an existential risk to humanity. It won’t matter how much data intelligent machines process or how fast they do it. They will be directed to apply their incredible processing power to do what people cannot.

Why Docs And Patients Have To Be On Their Toes

Like cars that go many times faster than humans can run; like airplanes that travel many times faster than cars; thinking machines will think of things that are beyond the capability of people. And, like cars and airplanes, they will sometimes crash. But, if past experience is a guide, designers will become more careful in their direction. And the flaws will be fixed. Thinking machines will continue to think – and they will do so more wisely.Physicians should not be asked to – nor should they ever – blindly accept the conclusions of thinking machines. But they must know enough about smart machines to spot the signs of trouble – and know when adjustments are needed.

About the Author

Greg Freiherr is a contributing editor to Imaging Technology News, or ITN. Over the past three decades, he has served as a business and technology editor for publications in medical imaging, as well as a consultant to industry, academia, and financial institutions.

Siemens Healthineers to acquire forerunner in robotic-assisted vascular interventions

  • Acquisition of Corindus is a strategically significant extension of Siemens Healthineers’ Advanced Therapies business
  • Siemens Healthineers combines its cardiovascular and neuro-interventional therapy systems with Corindus’ innovative technology, driving procedure optimization for image-based minimally invasive therapies
  • Under the terms of the agreement, Siemens Healthineers will acquire all fully diluted shares of Corindus for $1.1 billion

“Together with Corindus, Siemens Healthineers is well-positioned to be one of the leading players in the field of robotic vascular interventions and to perform minimally invasive procedures more accurately, more quickly and more effectively. With this acquisition, we are opening up a new field for our image-guided therapies business. Together with our strong portfolio in imaging, digitalization and artificial intelligence, we are creating significant synergies to advance therapy outcomes“, said Bernd Montag, CEO of Siemens Healthineers AG.

“The collaboration with Siemens Healthineers is a unique opportunity to take our business to the next level and continue our success story. Together we plan to develop next-generation solutions that further improve patient care“, said Mark Toland, President and CEO at Corindus.

Siemens Healthineers AG has entered into a merger agreement with U.S.-based Corindus Vascular Robotics, Inc. (NYSE American: CVRS), a global technology leader for robotic-assisted vascular interventions. Under the terms of the agreement, Siemens Healthineers will acquire all fully diluted shares of Corindus for $4.28 per share in cash or
$1.1 billion in total. The transaction is expected to be closed by end of calendar year 2019, subject to Corindus shareholder approval, receipt of regulatory approvals and other customary closing conditions. The Corindus board fully supports the acquisition proposal. Corindus is headquartered in Waltham, Boston, Massachusetts, and currently has approximately 100 employees.

Corindus develops, produces and sells robotic systems for minimally invasive procedures. These systems help doctors to precisely control guide catheters, guide wires, balloon or stent implants via integrated imaging. The physician does not have to stand at the angiography table as usual but can control the procedure with a separate controlling module and is therefore less exposed to radiation. Corindus is currently one of the leading companies offering a robotic treatment platform for major vascular therapeutic markets, meaning coronary, peripheral vascular and neurovascular interventions. For example, heart disease is the most common cause of death in the U.S. Every year, more than four million percutaneous coronary interventions are carried out worldwide.

The acquisition of Corindus meets the objective of simplifying today’s challenges in everyday hospital life. Robotic assisted minimally invasive procedures have the potential to reduce treatment times, increase precision during treatment, raise standardization levels in clinical procedures and ultimately improve clinical outcomes, which is the strategic focus of the Advanced Therapies business segment.

“The interplay of exact imaging and robotic-assisted interventions will enhance both the eyes and hands of the physician, metaphorically speaking. With the addition of Corindus to our strong therapies portfolio we sharpen our procedural focus and will grow by expanding precision medicine and improving clinical outcomes. In the future, our digital and artificial intelligence-based tools will help to integrate the aspects of image-guidance and therapy even further”, said Michel Therin, President Advanced Therapies at Siemens Healthineers.

The CorPath systems developed by Corindus will be used together with angiography systems that Siemens Healthineers sells as one of the leading suppliers. The Siemens Healthineers products make minimally invasive treatment possible by using high-quality imaging before and during medical interventions. The company’s leading role in image-based minimally invasive procedures is now complemented by robotic-assisted precision medicine. This expansion strengthens the therapy position of Siemens Healthineers and underlines its role as one of the leading providers of solutions along the entire treatment path. This makes the acquisition of Corindus a strategically significant extension of Siemens Healthineers’ therapy business.

The future integration of Siemens Healthineers digitization and artificial intelligence solutions with Corindus’ robotic systems offers further promising possibilities. The aim is to further increase procedure optimization in order to enable the greatest possible degree of efficiency and clinical reproducibility. In addition, Corindus is driving forward the approval procedure for remote robotic treatment in vascular interventions. Due to the limited availability of specialists for minimally invasive procedures in many regions and the limited number of corresponding clinical facilities, remote treatment could significantly improve patients’ access to treatment in the future.

Siemens Healthineers to acquire forerunner in robotic-assisted vascular interventions

  • Acquisition of Corindus is a strategically significant extension of Siemens Healthineers’ Advanced Therapies business
  • Siemens Healthineers combines its cardiovascular and neuro-interventional therapy systems with Corindus’ innovative technology, driving procedure optimization for image-based minimally invasive therapies
  • Under the terms of the agreement, Siemens Healthineers will acquire all fully diluted shares of Corindus for $1.1 billion

“Together with Corindus, Siemens Healthineers is well-positioned to be one of the leading players in the field of robotic vascular interventions and to perform minimally invasive procedures more accurately, more quickly and more effectively. With this acquisition, we are opening up a new field for our image-guided therapies business. Together with our strong portfolio in imaging, digitalization and artificial intelligence, we are creating significant synergies to advance therapy outcomes“, said Bernd Montag, CEO of Siemens Healthineers AG.

“The collaboration with Siemens Healthineers is a unique opportunity to take our business to the next level and continue our success story. Together we plan to develop next-generation solutions that further improve patient care“, said Mark Toland, President and CEO at Corindus.

Siemens Healthineers AG has entered into a merger agreement with U.S.-based Corindus Vascular Robotics, Inc. (NYSE American: CVRS), a global technology leader for robotic-assisted vascular interventions. Under the terms of the agreement, Siemens Healthineers will acquire all fully diluted shares of Corindus for $4.28 per share in cash or
$1.1 billion in total. The transaction is expected to be closed by end of calendar year 2019, subject to Corindus shareholder approval, receipt of regulatory approvals and other customary closing conditions. The Corindus board fully supports the acquisition proposal. Corindus is headquartered in Waltham, Boston, Massachusetts, and currently has approximately 100 employees.

Corindus develops, produces and sells robotic systems for minimally invasive procedures. These systems help doctors to precisely control guide catheters, guide wires, balloon or stent implants via integrated imaging. The physician does not have to stand at the angiography table as usual but can control the procedure with a separate controlling module and is therefore less exposed to radiation. Corindus is currently one of the leading companies offering a robotic treatment platform for major vascular therapeutic markets, meaning coronary, peripheral vascular and neurovascular interventions. For example, heart disease is the most common cause of death in the U.S. Every year, more than four million percutaneous coronary interventions are carried out worldwide.

The acquisition of Corindus meets the objective of simplifying today’s challenges in everyday hospital life. Robotic assisted minimally invasive procedures have the potential to reduce treatment times, increase precision during treatment, raise standardization levels in clinical procedures and ultimately improve clinical outcomes, which is the strategic focus of the Advanced Therapies business segment.

“The interplay of exact imaging and robotic-assisted interventions will enhance both the eyes and hands of the physician, metaphorically speaking. With the addition of Corindus to our strong therapies portfolio we sharpen our procedural focus and will grow by expanding precision medicine and improving clinical outcomes. In the future, our digital and artificial intelligence-based tools will help to integrate the aspects of image-guidance and therapy even further”, said Michel Therin, President Advanced Therapies at Siemens Healthineers.

The CorPath systems developed by Corindus will be used together with angiography systems that Siemens Healthineers sells as one of the leading suppliers. The Siemens Healthineers products make minimally invasive treatment possible by using high-quality imaging before and during medical interventions. The company’s leading role in image-based minimally invasive procedures is now complemented by robotic-assisted precision medicine. This expansion strengthens the therapy position of Siemens Healthineers and underlines its role as one of the leading providers of solutions along the entire treatment path. This makes the acquisition of Corindus a strategically significant extension of Siemens Healthineers’ therapy business.

The future integration of Siemens Healthineers digitization and artificial intelligence solutions with Corindus’ robotic systems offers further promising possibilities. The aim is to further increase procedure optimization in order to enable the greatest possible degree of efficiency and clinical reproducibility. In addition, Corindus is driving forward the approval procedure for remote robotic treatment in vascular interventions. Due to the limited availability of specialists for minimally invasive procedures in many regions and the limited number of corresponding clinical facilities, remote treatment could significantly improve patients’ access to treatment in the future.

Siemens Healthineers to acquire forerunner in robotic-assisted vascular interventions

  • Acquisition of Corindus is a strategically significant extension of Siemens Healthineers’ Advanced Therapies business
  • Siemens Healthineers combines its cardiovascular and neuro-interventional therapy systems with Corindus’ innovative technology, driving procedure optimization for image-based minimally invasive therapies
  • Under the terms of the agreement, Siemens Healthineers will acquire all fully diluted shares of Corindus for $1.1 billion

“Together with Corindus, Siemens Healthineers is well-positioned to be one of the leading players in the field of robotic vascular interventions and to perform minimally invasive procedures more accurately, more quickly and more effectively. With this acquisition, we are opening up a new field for our image-guided therapies business. Together with our strong portfolio in imaging, digitalization and artificial intelligence, we are creating significant synergies to advance therapy outcomes“, said Bernd Montag, CEO of Siemens Healthineers AG.

“The collaboration with Siemens Healthineers is a unique opportunity to take our business to the next level and continue our success story. Together we plan to develop next-generation solutions that further improve patient care“, said Mark Toland, President and CEO at Corindus.

Siemens Healthineers AG has entered into a merger agreement with U.S.-based Corindus Vascular Robotics, Inc. (NYSE American: CVRS), a global technology leader for robotic-assisted vascular interventions. Under the terms of the agreement, Siemens Healthineers will acquire all fully diluted shares of Corindus for $4.28 per share in cash or
$1.1 billion in total. The transaction is expected to be closed by end of calendar year 2019, subject to Corindus shareholder approval, receipt of regulatory approvals and other customary closing conditions. The Corindus board fully supports the acquisition proposal. Corindus is headquartered in Waltham, Boston, Massachusetts, and currently has approximately 100 employees.

Corindus develops, produces and sells robotic systems for minimally invasive procedures. These systems help doctors to precisely control guide catheters, guide wires, balloon or stent implants via integrated imaging. The physician does not have to stand at the angiography table as usual but can control the procedure with a separate controlling module and is therefore less exposed to radiation. Corindus is currently one of the leading companies offering a robotic treatment platform for major vascular therapeutic markets, meaning coronary, peripheral vascular and neurovascular interventions. For example, heart disease is the most common cause of death in the U.S. Every year, more than four million percutaneous coronary interventions are carried out worldwide.

The acquisition of Corindus meets the objective of simplifying today’s challenges in everyday hospital life. Robotic assisted minimally invasive procedures have the potential to reduce treatment times, increase precision during treatment, raise standardization levels in clinical procedures and ultimately improve clinical outcomes, which is the strategic focus of the Advanced Therapies business segment.

“The interplay of exact imaging and robotic-assisted interventions will enhance both the eyes and hands of the physician, metaphorically speaking. With the addition of Corindus to our strong therapies portfolio we sharpen our procedural focus and will grow by expanding precision medicine and improving clinical outcomes. In the future, our digital and artificial intelligence-based tools will help to integrate the aspects of image-guidance and therapy even further”, said Michel Therin, President Advanced Therapies at Siemens Healthineers.

The CorPath systems developed by Corindus will be used together with angiography systems that Siemens Healthineers sells as one of the leading suppliers. The Siemens Healthineers products make minimally invasive treatment possible by using high-quality imaging before and during medical interventions. The company’s leading role in image-based minimally invasive procedures is now complemented by robotic-assisted precision medicine. This expansion strengthens the therapy position of Siemens Healthineers and underlines its role as one of the leading providers of solutions along the entire treatment path. This makes the acquisition of Corindus a strategically significant extension of Siemens Healthineers’ therapy business.

The future integration of Siemens Healthineers digitization and artificial intelligence solutions with Corindus’ robotic systems offers further promising possibilities. The aim is to further increase procedure optimization in order to enable the greatest possible degree of efficiency and clinical reproducibility. In addition, Corindus is driving forward the approval procedure for remote robotic treatment in vascular interventions. Due to the limited availability of specialists for minimally invasive procedures in many regions and the limited number of corresponding clinical facilities, remote treatment could significantly improve patients’ access to treatment in the future.