Blog posts - Thaumatec

Ultrasound is used in many different fields. Ultrasonic devices are used to detect objects and measure distances. Ultrasound imaging or sonography is most often used in medicine. In the non-destructive testing of products and structures, ultrasound is used to detect invisible flaws. Industrially, ultrasound is used for cleaning, mixing, and accelerating chemical processes.

Diagnostic ultrasound is a non-invasive diagnostic technique used to image inside the body. Ultrasound probes, called transducers, produce sound waves that have frequencies above the threshold of human hearing (above 20KHz), but most transducers in current use operate at much higher frequencies (in the megahertz (MHz) range). Most diagnostic ultrasound probes are placed on the skin. However, to optimize image quality, probes may be placed inside the body via the gastrointestinal tract, vagina, or blood vessels. In addition, ultrasound is sometimes used during surgery by placing a sterile probe into the area being operated on.

Diagnostic ultrasound can be further sub-divided into anatomical and functional ultrasound.
Anatomical ultrasound produces images of internal organs or other structures.
Functional ultrasound combines information such as the movement and velocity of tissue or blood, softness or hardness of tissue, and other physical characteristics, with anatomical images to create “information maps.”

These maps help doctors visualize changes/differences in function within a structure or organ.

Here more information and background:

https://www.nibib.nih.gov/science-education/science-topics/ultrasound

Examples for ultrasound examination in the Femtech area:

Obstetric ultrasonography, or prenatal ultrasound, is the use of medical ultrasonography in pregnancy, in which sound waves are used to create real-time visual images of the developing embryo or fetus in the uterus (womb). The procedure is a standard part of prenatal care in many countries, as it can provide a variety of information about the health of the mother, the timing and progress of the pregnancy, and the health and development of the embryo or fetus.

Breast ultrasound is a medical imaging technique that uses medical ultrasonography to perform imaging of the breast. It can be performed for either diagnostic or screening purposes[1] and can be used with or without a mammogram. In particular, breast ultrasound may be useful for younger women who have denser fibrous breast tissue that may make mammograms more challenging to interpret.

Vaginal ultrasonography is a medical ultrasonography that applies an ultrasound transducer (or “probe”) in the vagina to visualize organs within the pelvic cavity. It is also called transvaginal ultrasonography because the ultrasound waves go across the vaginal wall to study tissues beyond it.

Ovarian cysts are usually diagnosed by ultrasound, CT scan, or MRI, and correlated with clinical presentation and endocrinologic tests as appropriate.

Pelvic congestion syndrome, also known as pelvic vein incompetence, is a long-term condition believed to be due to enlarged veins in the lower abdomen. The condition may cause chronic pain, such as a constant dull ache, which can be worsened by standing or sex. Pain in the legs or lower back may also occur.

Current advances of Ultrasound in the Femtech area:

GE HealthCare designed the ultrasound systems to integrate AI, advanced tools and an ergonomic design. They speed exam time for clinicians while delivering a clearer picture of various conditions impacting women’s health. The latest systems combine high-performance hardware with flexible, scalable software to help increase confidence in diagnostic and treatment decisions.

AI-enabled ultrasound technologies support clear, quick and confident diagnoses. This latest launch helps to address patient demand and reduce staffing burdens by giving clinicians these tools.

New, improved and enhanced Features are:

Features include voice commands and the SonoLyst suite of AI tools. SonoLyst tools identify fetal anatomy and annotate and measure where applicable. This can reduce the time to complete second trimester exams.
simplifies assessments of the pelvic floor and speeds up exams as well. It automates plane alignment and measurements for high keystroke reduction.
They integrated the technology with its Vscan Air CL wireless dual probe. With a flexible, wireless workflow, users gain a wider range of motion.
FetalHS simplifies and speeds fetal heart assessments with step-by-step, AI-driven guidance for identifying normal fetal heart anatomy.

Conclusions and Opinions

With proven time-saving AI-driven applications, and advanced automation features that simplify exams, the technology can help enhance ease of use and provide clearer images, helping clinicians power through demanding workflows faster while delivering greater consistency and accuracy, ultimately helping deliver better health outcomes for women.
The new Voluson Signature series features innovative tools clinicians can rely on, and the automated functions help reduce work stress and improve workflows
A new era of ultrasound scanning.

Here the link to the article of +MASS DEVICE:

https://www.massdevice.com/ge-healthcare-launches-ai-ultrasound-womens-health/

HealthTech Industry Update | Artificial Intelligence in the Medical Imaging Technology

The integration of Artificial Intelligence (AI) into medical imaging has guided in an era of transformation in healthcare.

The innovation segment explores cutting-edge developments in AI, such as deep learning algorithms, convolutional neural networks, and generative adversarial networks, which have significantly improved the accuracy and efficiency of medical image analysis.

Advancements in medical imaging and artificial intelligence (AI) have ushered in a new era of possibilities in the field of healthcare. The fusion of these two domains has revolutionized various aspects of medical practice, ranging from early disease detection and accurate diagnosis to personalized treatment planning and improved patient outcomes.

Medical imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and positron emission tomography (PET) play a pivotal role in providing clinicians with detailed and comprehensive visual information about the human body. These imaging modalities generate vast amounts of data that require efficient analysis and interpretation, and this is where AI steps in.

Technological Innovations

Mathematical models and algorithms stand at the forefront of scientific exploration, serving as powerful tools that enable us to unravel complex phenomena, make predictions, and uncover hidden patterns in vast datasets. These essential components of modern research have not only revolutionized our understanding of the natural world but have also played a pivotal role in driving technological breakthroughs that open up numerous application possibilities across various domains.

The synergy between mathematical models and algorithms has not only enhanced our understanding of the world but has also been a driving force behind technological advancements that have transformed our daily lives.

Transformers

Convolutional Neural Networks (CNNs) are well suited for grid-like data, such as images, where local patterns can be captured efficiently. However, they struggle with sequential data because they lack a mechanism for modeling dependencies between distant elements (for example, in distinct time instants or far in the image).

Also, CNNs do not inherently model the position or order of elements within the data. They rely on shared weight filters, which makes them translation invariant but can be problematic when absolute spatial relationships are important.

Generative Models

Generative models are a class of machine learning models that can generate new data based on training data. Other generative models include generative adversarial networks (GANs), variational autoencoders (VAEs), and flow-based models. Each can produce high-quality images.

Deep Learning Techniques and Performance Optimization

Medical imaging techniques are based on different physical principles, each with their benefits and limitations. The ability to deal with such diverse modalities is also an important aspect to be addressed by AI.

Applications

AI-based imaging techniques can be divided in eight distinct categories: acquisition, preprocessing, feature extraction, registration, classification, object localization, segmentation, and visualization. These can also be organized in the clinical process pipeline broadly encompassing prevention, diagnostics, planning, therapy, prognostic, and monitoring. It is also possible to focus on the human organ or physiological process under focus.

Medical Image Analysis for Disease Detection and Diagnosis

Medical image analysis for disease detection and diagnosis is a rapidly evolving field that holds immense potential for improving healthcare outcomes. By harnessing advanced computational techniques and machine learning algorithms, medical professionals are now able to extract invaluable insights from various medical imaging modalities.

Artificial intelligence is an area where great progress has been observed, and the number of techniques applicable to medical image processing has been increasing significantly. In this context of diversity, review articles where different techniques are presented and compared are useful.

The role of AI in facilitating the analysis of large-scale retinal datasets and the development of computer-aided diagnostic systems is also highlighted.

However, AI is not always a perfect solution, and the challenges and limitations of AI-based approaches are also covered, addressing issues related to data availability, model interpretability, and regulatory considerations.

Imaging and Modeling Techniques for Surgical Planning and Intervention

Imaging and 3D modeling techniques, coupled with the power of artificial intelligence (AI), have revolutionized the field of surgical planning and intervention, offering numerous advantages to both patients and healthcare professionals.

By leveraging the capabilities of AI, medical imaging data, such as CT scans and MRI images, can be transformed into detailed three-dimensional models that provide an enhanced understanding of a patient’s anatomy. This newfound precision and depth of information allow surgeons to plan complex procedures with greater accuracy, improving patient outcomes and minimizing risks and AI-powered algorithms can analyse vast amounts of medical data, assisting surgeons in real-time during procedures, guiding them with valuable insights, and enabling personalized surgical interventions.

Image and Model Enhancement for Improved Analysis

Decision-making and diagnosis are important purposes for clinical applications, but AI can also play an important role in other applications of the clinical process.

In complex healthcare scenarios, it is crucial for clinicians and practitioners to understand the reasoning behind AI models’ predictions and recommendations.

Medical images often suffer from noise, artifacts, and limited resolution due to the physical constraints of the imaging devices. Therefore, developing effective and efficient methods for medical image super-resolution is a challenging and promising research topic, searching to obtain previously unachievable details and resolution.

Medical Imaging Datasets

Numerous advancements outlined above have arisen through machine learning public challenges. These initiatives provided supporting materials in the form of datasets (which are often expensive and time consuming to collect) and, at times, baseline algorithms, contributing to the facilitation of various research studies aimed at the development and evaluation of novel algorithms.

Conclusions

Cutting-edge techniques that push the limits of current knowledge have been covered in this editorial. For those focused on the AI aspects of technology, evolutions have been reported in all stages of the medical imaging machine learning pipeline.

The field of medical imaging and AI is evolving rapidly, driven by ongoing research and technological advancements. Researchers are continuously exploring novel algorithms, architectures, and methodologies to further enhance the capabilities of AI in medical imaging. Additionally, collaborations between clinicians, computer scientists, and industry professionals are vital in translating research findings into practical applications that can benefit patients worldwide.

By combining cutting-edge AI techniques and their practical applications, it is clear that AI will continue shaping the future of healthcare in profound and positive ways.

Here the link to the full article in Bioengineering (Basel) from Luís Pinto-Coelho

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10740686

Thaumatec HealthTech Industry Update | How Can Healthcare Ensure Responsible AI Use?

Executives from across the industry shared their thoughts on how the healthcare sector can ensure its use of AI is ethical and responsible during the HIMSS24 conference, which took place in Orlando. Below are some of the most notable ideas they shared.

Collaboration is a must

While the healthcare industry lacks a shared definition for what responsible AI use looks like, there are plenty of health systems, start-ups and other healthcare organizations that have their own set of rules to guide their ethical AI strategy. Healthcare organizations from all corners of the industry must come together and bring these frameworks to the table in order to come to a shared consensus for the industry as a whole, he explained.

Start with use cases that have low risks and high rewards

Currently, there are still many unknowns when it comes to some of the new large language models hitting the market. That is why it is essential for healthcare organizations to begin deploying generative AI models in areas that pose low risks and high rewards. Using generative AI to generate a summary of a patient’s hospital stay and prior medical history isn’t very risky, but it can save nurses a lot of time and therefore be an important tool for combating burnout.

Trust is key

Generative AI tools can only be successful in healthcare if their users have trust in them. Because of this, AI developers should make sure that their tools offer explain-ability. For example, if a tool generates patient summaries based on medical records and radiology data, the summaries should link back to the original documents and data sources. That way, users can see where the information came from.

Conclusions

Healthcare leaders “sometimes expect that the technology will do more than it is actually able to do” but AI is not a silver bullet for healthcare’s problems. To not get stuck in this trap, think of AI as something that serves a supplementary or augmenting function. AI can be a part of the solution to major problems like clinical burnout or revenue cycle challenges, but it’s unwise to think AI will eliminate these issues by itself.

Here the full Article by MedCity News:

https://medcitynews.com/2024/04/healthcare-ai-2/

HealthTech Industry Update | Are Healthcare Professionals Ready for AI ?

The modern healthcare environment will require ambidextrous experts in healthcare and AI.

As the artificial intelligence (AI) market grows to over $400 billion by 2027, the demand for professionals with expertise in machine learning is increasing as AI technology rapidly evolves. Within the healthcare sector, a deep knowledge of the clinical sciences and healthcare skills will no longer be enough.

Moving ahead, healthcare corporations and academia must empower ambidextrous healthcare professionals (HCPs) with expertise in both machine learning and health science to stay innovative and competitive.

What to do ?

Develop ambidextrous skills in ongoing study and research

Large healthcare companies are exploring ways to integrate AI within their businesses to drive greater efficiencies across different R&D and commercial processes and workflows. With a shift towards corporations placing outsized value on an ambidextrous skillset, HCP graduates are dedicating more focus on AI in their study and research.

Empower ambidextrous professionals to lead interdisciplinary healthcare teams

Interdisciplinary teams that empower ambidextrous AI and healthcare professionals as leaders and project owners will realize their full potential. Their leadership can help fill the gaps in knowledge and communication between team members and drive significant efficiencies.

For HCPs, their lack of technical expertise may lead to an overestimation of AI capabilities, causing a mismatch between expectations and technical realities. For machine learning experts, insufficient knowledge of healthcare can be a barrier to identifying the right problems to solve with AI, resulting in misdirected initiatives and misallocated resources.

Find and cultivate ambidextrous talent in corporate and academic environments

Identifying ambidextrous expertise in health medicine and AI is not easy. Healthcare professionals are specialized experts that take years to train. Few universities have programs that offer training in AI. This is a challenge that the life sciences industry and academia can solve together.

Ambidextrous professionals will drive AI innovation in healthcare

With the right ambidextrous people in the right roles, corporations can further innovation in AI and healthcare. Companies must place a higher emphasis on recruiting and developing ambidextrous experts. The ability to empower and retain such experts and leaders will not only alter the course of their business, but also increase their relevance as AI rapidly transforms our modern economy.

Here the full Article of the MedCity News: AI Disruption is Coming. Are Healthcare Professionals Ready?

https://medcitynews.com/2024/04/ai-disruption-is-coming-are-healthcare-professionals-ready/

‍Thaumatec Knowledge Guide | Flex PCBs in Medical Device Applications

Flexible printed circuit boards can be considered the gymnasts of electronic components. Unlike their rigid counterparts, they possess a remarkable ability to bend and adapt to different shapes, enabling more versatile designs in electronic devices. These boards are made from electrically conductive materials, allowing seamless connections between various electronic components. The flexibility of these PCBs opens the door to a new era of design possibilities.

In essence, flexible PCBs offer a dynamic and adaptable foundation for the intricate electronic systems that power our modern world.

Advantages of Flexible PCBs

Flexible printed circuit boards bring a myriad of advantages to the table, making them a preferred choice in modern electronics.

Flexibility and Adaptability
Space-saving Marvels
Durability and Reliability
Seamless Connectivity

What Are Flexible PCBs Used For?

In the changing world of medical devices, the role of flexible rigid printed circuit boards is crucial. These small electronic powerhouses help make devices smaller, meeting the evolving needs of the industry.

Medical Wearables: Flex PCBs find application in a variety of wearable health-tracking devices, such as blood glucose monitors, body temperature monitors, blood pressure monitors, heart monitors, etc.
Implantable Medical Devices: These refer to devices designed to be inserted into the human body, benefiting from the flexibility of PCBs for integration. Flex PCBs are used in various implantable medical devices such as pacemakers, neurostimulators, implantable cardiac defibrillators, and cochlear implants.
Hearing Aid Devices: Flex PCB designs enable the integration of microphone, digital signal processing (DSP), and battery components into a compact unit that fits discreetly behind the ear.
Diagnostic and Medical Imaging Equipment: such as ultrasound machines, MRI scanners, CT scanners, X-ray machines, and radiation treatment. The flexibility of Flex PCBs allows for compact and lightweight designs, which are crucial for portable and handheld diagnostic devices.
Remote Patient Monitoring Devices: Notable instances of remote patient monitoring devices leveraging flex PCBs comprise wireless blood glucose monitors and wearable ECG sensors.
Endoscopic and Minimally Invasive Surgery Devices: Iin endoscopic cameras, catheters, and other minimally invasive surgical instruments the flexibility enables the creation of small, lightweight, and highly manoeuvrable devices that can navigate through the body’s intricate pathways with ease.

Overcoming Design Challenges

Flexible Printed Circuit Boards navigate and conquer unique design challenges, offering solutions that traditional rigid PCBs struggle to address.

Compact Design Challenge
Environmental Challenges
Integration in Small Devices
Reducing Complex Wiring
Conforming to Unique Shapes

Future Trends and Innovations

As technology advances, the trajectory of Flexible Printed Circuit Boards (Flexible PCBs) points towards exciting trends and innovations that will shape the future of electronic devices.

Internet of Things (IoT) Connectivity
Miniaturization and Integration
Advanced Materials
Biocompatible Applications
3D Flexible PCBs
Stretchable Electronics

Conclusion

Flexible printed circuit boards emerged as the key to revolutionizing electronic design. Their adaptability addresses real-world challenges, making them indispensable in medical IoT applications.

Future trends forecast even smaller, more versatile boards, promising a dynamic landscape in electronics.

Flexible PCBs are not merely components but architects of a connected, flexible future.

As we navigate this ongoing journey, the potential for innovation remains boundless, ensuring that flexible PCBs continue to play a transformative role in the ever-evolving realm of electronic devices.

Here the full article from TechnoTronix:

https://www.technotronix.us/pcbblog/flex-pcbs-for-medical-applications/

HealthTech knowledge guide | How Embedded IoT Medical Devices work

With the proliferation of the Internet of Things (IoT) devices, there has been a huge transformation in terms of smart cities, connected manufacturing, wireless communication, and connected healthcare.

Embedded medical devices reduce the time to diagnose and treat patients effectively since these systems run on a high-speed processor with rich operating system interface.

These devices store data of each patient on the cloud and use them for different analysis and diagnosis purpose on a repetitive basis, decreasing the overall treatment turnaround.

How Embedded IoT Medical Devices Work

IoT medical devices work by connecting to different hardware for the diseases examination purpose. The device system has a touchscreen interface for users to input data for analysis and processing.

As a user inputs data related to the diseases, the system looks for symptoms pre-loaded into the file and tries to match with the provided input. If the match is found with the pre-loaded symptoms, the system responses with the disease name and generates a prescription for general medicine.

In case of a partial or no match, the system notifies to undergo a different test based on the input given by the user and pre-loaded file matching to identify the exact disease and provide prescription accordingly.

Prescriptions and other important details are stored on the cloud-based database management solution which can be used for future analysis. This patient information stored in the cloud can be also used for different analysis.

If proper disease information cannot be found by the given input and other tests performed, the system contacts the Doctor with the given information.

Workflow of IoT Medical Devices

User:

The user will provide the input via a touchscreen panel for the symptoms into Embedded Medical Device. The user also needs to provide all the personal information such as name, contact number, age, etc. Then, the embedded device will return generic medicine prescription for the disease found based on the input or contact the Doctor if the disease is not found.

Embedded Medical Device with IoT:

The embedded medical device receives inputs from the user to match the symptoms with a pre-loaded symptom file and tries to find the matching disease for same. It performs tests suggested based on the pre-loaded symptom file to get the exact match for the disease if the disease is not found by examining the symptoms. If the disease information is not found the system involves the doctor with the given information, who will consult the user, diagnose the disease and accordingly update the symptom file and disease file in the system.

Sensors used in Connected Healthcare Device:

Glucometer
Temperature Sensor
Blood Pressure Sensor
Airflow Sensor
ECG Sensor
EMG Sensor

Prescription for General Medicines:

Based on the input provided by the user, once the disease is found by the embedded medical device, it will look for generic medicine information in the pre-loaded prescription file, mapping the disease and medication.

Cloud Database Management System:

In this stage, the embedded device will store all the user details in the cloud database. This cloud-based solution can store the following information for future analysis:

User’s personal information
Information about symptoms
Information about tests performed and their results
Information about disease(s) diagnosedInformation about prescription and medication
Information about Doctor’s consultation if any
Device information from where all data is getting logged
Device health information on cloud just to make sure that device is working fine that includes all the sensor status and other basic information

Different analysis reports can be generated based on above information stored in the cloud for future use and preventive actions.

Conclusion

The embedded IoT medical devices are helpful in the area where basic healthcare facilities are not available.

Based on the disease analysis and data stored in the cloud, it helps users cure the diseases on time and also take preventive actions.

However, it is important to know that a continuous network connectivity is required to integrate the medical device with the cloud.

Medical devices must be compliant according MDR, IEC 60601-1/2/6, IEC 62304, 510K and ISO 13485.

Here the full article from einfochips:

https://www.einfochips.com/blog/understanding-the-working-of-embedded-iot-medical-devices/

Thaumatec HealthTech Industry Update | Blogpost Collection of Artificial Intelligence and Robotics in HealthTech

AI and Robotics technology for applications and solutions are bringing high expectations to:

medical devices
wellbeing applications and devices
digital health platforms
diagnosis and analytics tools
surgery robots and control solutions
medical research analytics and results
healthcare processes and administration
Patient monitoring & management
IOMT Solutions
and a lot of more solutions and products

Generative AI tools and chatbots, for example, are cutting the time doctors and nurses spend on paperwork. Every day, we learn more about the potential of AI to improve healthcare.

One recent study by the UK’s Royal Marsden NHS Foundation Trust and the Institute of Cancer Research showed that AI was “almost twice as accurate as a biopsy at judging the aggressiveness of some cancers.” This translates into different treatments and, ultimately, more lives saved.

Today’s surgical robots extend surgeon’s capacities, they filter out hand tremors and allow manoeuvres that even the best surgeon couldn’t pull off with laparoscopic surgery’s typical long-handled tools. What will bring the future, will AI substitute surgeons?

Will robots run in future the tests and experiments inside the laboratory and be controlled via WEB page access?

Will AI accelerate drug discovery and boost personalized medicine and patient engagement?

Here some insight what we published in our Thaumatec Knowledge Database during the last years:

We will stay on the ball and further follow up periodically the further AI journey in HealthTech.

HealthTech Knowledge Guide | ‍What is RIS, PACS, DICOM AND MIP?

PACS, RIS, DICOM AND MIP are systems and standards of a digital, connected, data managing and analysing of Imaging technology systems and platforms. The goal is to collect, administrate, distribute, store, track and interpret health related Images from Radiology.

Additional to the Article in our Knowledge Database:

https://thaumatec.com/knowledge/blog-posts/healthtech-industry-update-imaging-industry-trends-2023-and-2024-outlook/

here some technology details of this systems with highlights and as well videos which explain more.

PACS means Picture archiving and communication system and is a medical imaging technology used primarily in healthcare organizations to securely store and digitally transmit electronic images and clinically-relevant reports.

It is used to store, retrieve, present and share images produced by various medical hardware modalities, such as X-ray machines, computed tomography (CT) scans, magnetic resonance imaging (MRI) scans and ultrasound machines. The storage media for PACS can be online (cloud storage) or offline (on-premises).

Every PACS has four major components:

Hardware imaging machines.
Secure network for the distribution and exchange of patient images.
Workstation or mobile device for viewing, processing and interpreting images.
Electronic archive for storing and retrieving images and related documentation and reports.

More insight:

RIS is a Radiology Information System with software that medics can use to keep better track of each patient being treated. It is the core system for the electronic management of imaging departments. The major functions of the RIS can include patient scheduling, resource management, examination performance tracking, reporting, results distribution, and procedure billing. RIS complements HIS (hospital information systems) and PACS (picture archiving and communication system), and is critical to efficient workflow to radiology practices.

Radiological information systems commonly support the following features:

Patient registration and scheduling
Patient list management
Modality interface using worklists
Workflow management within a department of radiology
Request and document scanning
Result entry
Digital reporting (usually using Voice Recognition (VR))[4]
Printables like patient letters and printed reports
Result transmission via HL7 integration or e-mailing of clinical reports[5]
Patient tracking
Interactive documents
Creation of technical files
Modality and material management
Consent management

More insight:

DICOM® means Digital Imaging and Communications in Medicine and is the international standard for medical images and related information. It defines the formats for medical images that can be exchanged with the data and quality necessary for clinical use.

This standard has been widely adopted by hospitals and the medical software industry, and is sometimes used in smaller-scale applications, such as dentists’ and doctors’ offices.

It is implemented in almost every radiology, cardiology imaging, and radiotherapy device (X-ray, CT, MRI, ultrasound, etc.), and increasingly in devices in other medical domains such as ophthalmology and dentistry. With hundreds of thousands of medical imaging devices in use, DICOM® is one of the most widely deployed healthcare messaging Standards in the world. There are literally billions of DICOM® images currently in use for clinical care.

DICOM® is recognized by the International Organization for Standardization as the ISO 12052 standard.

More insight:

MIP means Maximum intensity projection is a simple three-dimensional visualization tool and simple ray-tracing technique where the maximum intensity encountered along each ray within volumetric data is projected onto the screen.

That can be used to display computed tomographic angiography data sets.

MIP images are not threshold dependent and preserve attenuation information. is a simple ray-tracing technique where the maximum intensity encountered along each ray within volumetric data is projected onto the screen.

More insight:

https://svi.nl/MaximumIntensityProjection

HealthTech Industry Update | AI in healthcare | Options, Decisions and Goals

Imagine you’re tasked with urgently improving healthcare for people everywhere, and artificial intelligence (AI) promises to make a significant difference on a scale not seen in our lifetime. With the three options presented below, you can either:

1. Try to halt the development of AI until the risks and benefits are fully understood.
2. Go blazing into the unknown, deploying AI at every turn and dealing with issues as they arise.
3. Or fully embrace AI but take considered steps to limit the risks while unlocking the immediate benefits.

The general problems to be solved are

Many healthcare organizations are struggling to consistently deliver high-quality care.

There are more patients to look after and significant pockets of staff shortages, with doctors and nurses burdened by increasing clerical demands and complexity.

Costs are rising too.

Although many of us have received excellent care and great treatment, others struggle to access the care they need, especially people who live in under-resourced areas of the world

but also, increasingly, people living in wealthy urban areas.

We believe this crisis will only worsen unless we urgently deploy solutions that make a difference.

The good message is

AI can help, and it already has.

Generative AI tools and chatbots, for example, are cutting the time doctors and nurses spend on paperwork. Every day, we learn more about the potential of AI to improve healthcare. One recent study by the UK’s Royal Marsden NHS Foundation Trust and the Institute of Cancer Research showed that AI was “almost twice as accurate as a biopsy at judging the aggressiveness of some cancers.” This translates into different treatments and, ultimately, more lives saved. To learn about other developments on the horizon, consider some of the many predictions for what’s in store for 2024, from accelerating drug discovery to boosting personalized medicine and patient engagement.

But the goals are to be

(1) Harmonize the many sets of AI principles/frameworks/blueprints for healthcare and biomedical science. Identify and fill the gaps to create a best practice AI Code of Conduct with ‘guideline interoperability.’
(2) Align the field in advancing broad adoption and embedding of the harmonized AI Code of Conduct.
(3) Identify the roles and responsibilities of each stakeholder at each stage of the AI lifecycle.
(4) Describe the architecture needed to support responsible AI in healthcare.
(5) Define the identified ways to increase the speed of learning about how to govern AI in healthcare in service of a learning health system.

Conclusion

To achieve these goals and realize the full benefits of AI in healthcare while mitigating risks, it will take cross-sector collaboration and coalition building every step of the way – because we have a collective industry-wide responsibility to get this right.

Here the full Article by the World Economic Forum:

https://www.weforum.org/agenda/2024/01/ai-in-healthcare-buckle-up-for-big-change-but-read-this-before-takeoff/

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