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Exam Code: RDN Practice test 2023 by team
RDN Registered Dietitian

Test Detail:
The Registered Dietitian (RDN) certification is a professional credential for individuals who have completed the necessary education and training in the field of nutrition and dietetics. The certification is administered by the Commission on Dietetic Registration (CDR) and is recognized in the medical and healthcare industry. This description provides an overview of the RDN certification.

Course Outline:
The RDN certification requires completion of specific education and training requirements in the field of nutrition and dietetics. The course outline may include the following topics:

1. Nutrition Sciences:
- Biochemistry and metabolism
- Nutrient composition and analysis
- Macronutrients and micronutrients
- Food science and technology

2. Medical Nutrition Therapy:
- Clinical assessment and diagnosis
- Nutrition intervention and monitoring
- Disease-specific nutrition management
- Nutritional support and therapy

3. Foodservice Management:
- Menu planning and development
- Food production and service
- Food safety and sanitation
- Quality assurance and control

4. Community and Public Health Nutrition:
- Health promotion and education
- Public health programs and policies
- Community nutrition assessment
- Nutrition counseling and behavior change

5. Research and Evidence-Based Practice:
- Research methodology and design
- Data analysis and interpretation
- Evidence-based practice guidelines
- Research ethics and dissemination

Exam Objectives:
The RDN certification test evaluates the candidate's knowledge and competence in the field of nutrition and dietetics. The test objectives may include:

1. Understanding of nutrition sciences and their application in health and disease.
2. Ability to assess nutritional needs and develop appropriate interventions.
3. Knowledge of medical nutrition therapy for various diseases and conditions.
4. Competence in foodservice management principles and practices.
5. Understanding of community and public health nutrition concepts and strategies.
6. Familiarity with research methodologies and evidence-based practice in nutrition.

Exam Syllabus:
The test syllabus for the RDN certification may cover the following topics:

1. Nutrition Sciences and Biochemistry
2. Medical Nutrition Therapy and Clinical Assessment
3. Foodservice Management and Menu Planning
4. Community and Public Health Nutrition
5. Research Methods and Evidence-Based Practice

Registered Dietitian
Medical Registered learn
Killexams : Medical Registered learn - BingNews Search results Killexams : Medical Registered learn - BingNews Killexams : No AI Can Learn the Art of Medicine

A 49-year-old female notices new-onset vaginal bleeding over the past several days. She becomes concerned and seeks advice from her long-time family physician. When she calls, she is surprised to hear responses from an artificial intelligence (AI) platform. The longtime secretary, who knew her well and would quickly arrange appointments or connect her with the doctor, has been replaced by this expensive new AI-based system. The call begins with an extensive library of prompts. When she presses 0 to speak with a human, she is told the next available appointment is in nine weeks. She hangs up and redials to discuss her problem with a pleasant computer voice, which almost sounds like a real person and asks her to describe her problem—eventually responding with a long-winded response with possible explanations for her bleeding. It then utilizes a proprietary algorithm to make recommendations which include lifestyle changes and watchful waiting, with instructions to dial back if the problem persists.

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Eventually, she loses patience and decides to visit the office in person. After briefly seeing her in the office, her doctor is concerned and orders a CT scan with the smart scheduler that uses a complex triage algorithm to schedule her imaging in 1-2 days. She then receives the results of the CT scan in an email and again goes through the scheduler system to book her surgery, which is again triaged based on perceived medical urgency. The night before the operation, the pre-operative anesthesia system automatically calls and asks dozens of questions through various menus, ending with lengthy instructions regarding eating, drinking, and pre-operative care. The program does not offer time to address her fears of going under anesthesia.

The day of surgery, everything is increasingly efficient due to new AI-based systems. The operating room team already has her medical history in the electronic record, and she immediately goes to the operating room without needing to meet with the anesthesiologist or surgeon. All goes well, and four days later she gets an email with instructions to call a number and use a six-digit code to get information on the results of an ovarian biopsy. She won’t need to waste any time traveling to the doctor’s office or sitting in the waiting room for her appointment. Instead, a computer-generated AI voice informs her that she has high-grade serous ovarian cancer with metastasis.

The platform then automatically redirects her to a line where a compassionate, AI voice explains the prognosis and her various treatment options based on the latest research. She breaks down and drops the phone in tears. There is nobody to comfort her, let alone answer her endless questions. Is this the nightmare scenario for future patients in our rapidly evolving healthcare system, or a reality in the setting of ongoing physician shortages, skyrocketing medical costs, and “manmade medical errors”? Will this Boost patient health and reduce obstacles to accessing care, or will it create discomfort and dissatisfaction in the healthcare setting?

A exact piece by New York Times health columnist, Gina Kolata, hints that such a future, encompassing such a nightmare patient encounter with our tech-enabled and evolving AI-paradigm of care, may not be far off.

In order to preserve our sense of  compassion and humanity, healthcare providers must prioritize human-to-human communication when we deliver delicate news in order to foster caring relationships with our patients. This forms the basis of the humanity of medicine and the sacred doctor-patient relationship.

That said, no health care provider can disagree that tedious tasks currently take us away from face-to-face, direct patient care. A study in JAMA Internal Medicine found that AI assistants may hold value in composing routine notes or drafting responses to a skyrocketing number of electronic messages from patients as physician demands and burnout rise. Simply put, this work is a call to action for the medical establishment to look inward and determine how we can prioritize the human connection among doctors and patients while taking advantage of AI.

Overall, it appears that doctors are optimistic—but also expressing caution—regarding the potential for AI and large language models becoming part of a toolkit for promoting more effective communication between patients and healthcare providers. Certainly, this technology holds promise, as science communication for years has been marred by complexity and inaccessibility to the lay public. Using AI to better communicate health advice and medical literature with the public will be valuable.

There is certainly promise of large language models to help busy health care professionals with composing emails, reviewing medical records, and answering prior authorizations. Moreover, AI may help triage the patients and the questions that reach physicians, with more routine or unnecessary items being answered by technology. The potential to reduce time spent on tasks that lead to anger, frustration, and ultimately burnout is invaluable. Additionally, administrative costs are estimated to drop by over 35% given this evolution.

As total medical knowledge grows exponentially, it is impossible for doctors to stay abreast of all medical advances and retain such detailed knowledge in our brains. On the contrary, both AI and robotics will inevitably be more effective in cataloging constantly changing medical knowledge. This can support evidence-based management for patients. However, physicians must hold onto their unique and special gifts of humanism and empathetic care for patients.

But let’s be clear—practicing medicine is an art, and no technology can take away that fact. When facing patients themselves, human interaction with a doctor is vital. Patient satisfaction and shared decision-making will continue to rely heavily on this humanism. Medicine is a profession that still requires compassion, reassurance, and most importantly, empathy. Even with the advent and ongoing evolution of AI and other large language models, empathy is best learned and communicated in the form of bedside teaching by humans—not AI or chatbots.

However, considering that AI and chatbots were supported by some experts in Kolata’s piece as an approach for teaching healthcare professionals how to express empathy and compassion to patients or families, it’s likely time for us to hit the “reset button” on how we approach conversations and communications with patients.

We feel that the most effective way to restore empathy and compassion as the cornerstone of physician communication to patients is not by modeling or a framework suggested by AI or chatbots; instead, this requires a focus on human-to-human teaching and dialogue. Education surrounding humanities, social sciences, and the science of communication are just as vital as teaching physicians about anatomy and physiology. This applies to not only medical students, but to those in residency, and all healthcare professionals. Certainly, education about emerging technologies and medical devices will also become important so providers can best incorporate these innovations to Boost care without compromising patient experiences.

In the past, bedside teaching in medical school was an art practiced by careful observation and listening, with particular attention to eyes and body language as our professors handed down the invaluable unwritten and unspoken ways to express care, concern, and empathy for our patients. This human interaction has proven implications for patient satisfaction, motivation, and adherence to treatment recommendations.

Such unique and unspoken methods of communication of human emotions and interactions cannot be taught by AI or chatbots. Granted, medicine is often criticized for being decades behind in innovation, and as AI technology grows, health professionals should certainly embrace its benefits. However, they must also remain true to their values and the oath they have taken to serve their patients. Upholding these professional standards requires a strong adherence to humanistic care and continued development of communication skills. It is in the patient’s best interest to stay attuned to these trends and understand the benefits and risks of modern innovations in care.

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Tue, 22 Aug 2023 22:00:00 -0500 en text/html
Killexams : Dr. Terry Dubrow had a ministroke. He wants people to learn from his medical emergency. No result found, try new keyword!Dr. Terry Dubrow recently had a medical emergency. When the co-host of E!'s Botched was out to dinner with his wife, Heather Dubrow, and their son Nicholas, he experienced strokelike symptoms. At ... Thu, 17 Aug 2023 11:17:51 -0500 en-us text/html Killexams : Remote learning during pandemic aids medical students with disabilities

Medical students who reported a disability to their school increased by more than 25% during the COVID-19 pandemic, a study shows.

The proportion of students reporting attention deficit hyperactivity disorder or chronic health and/or psychological disabilities has increased between 2015 and 2021.

Despite the increase in medical students reporting these conditions, the requests for more inclusive preclinical testing accommodations, like extra time for test completion or a less distracting environment, decreased during the pandemic between 2019 and 2021.

According to authors of the new research letter in JAMA Network Open, the remote curriculum delivery during the pandemic may have allowed students to create an optimal learning and testing environment, decreasing the need for accommodation.

"Medical education was at its most flexible during COVID," said Lisa Meeks, Ph.D., clinical associate professor of learning health sciences and at the University of Michigan Medical School.

She adds that this could have reduced the need for testing accommodations, but it is unclear whether the need for accommodations will rise again after the exact return to in-person lectures and testing.

Documenting the rise

The study results are part of a long-term research project led by Meeks that follows the prevalence of medical students in the United States who disclose disabilities to their respective schools.

This study on disability disclosure in medicine was the first large scale study of its kind, encompassing all types of disability, including psychological, learning, sensory, physical and chronic health conditions.

Since 2015, researchers have seen an increase of medical students reporting a disability to their institution from 2.8% in 2015 to 4.7% in 2019, and to 5.9% in 2021.

When asked to describe why we see such large increases in the population of medical students with disabilities, Meeks posited that "growth in this population could mean that we are reducing bias and stigma, and therefore people who were already in medicine are more willing to disclose."

"It could also mean that our research sparked a conversation to change policies, which then led to individuals with disabilities who didn't think they could make it in medical school choosing to apply to these schools."

Doctors with disabilities Boost patient care

According to Meeks, there is still significant work to be done to increase the representation of doctors with disabilities in medicine.

Only 5.9% of medical school students report a disability, but 27% of adults in the U.S. currently live with some type of disability.

As the population ages, this number is expected to increase.

"Physicians in the U.S. and many other countries report that they do not feel confident in their ability to provide equal quality of care to patients with disabilities as they provide to patients without disabilities," said Karina Pereira-Lima, Ph.D., a research fellow in the Michigan Medicine neurology department.

"The inclusion of professionals with disabilities in medicine can greatly Boost the care for patients with disabilities and the health of the population overall."

Retaining medical trainees with disabilities

Increasing the number of physicians with disabilities requires both the recruitment and retention of medical trainees.

"Anonymous research with medical trainees with disability shows that about one in every five medical students and more than half of resident physicians do not request accommodations when they need them," said Pereira-Lima.

The two main reasons for not requesting needed accommodation were fear of stigma or bias and lack of a clear institutional process.

"Program access, or simply having the ability to access accommodations should they need them, improves medical trainees with disabilities performance in relation to testing and patient care. It also reduces the likelihood of reporting depressive symptoms or burnout," added Pereira-Lima.

Meeks advocated for "standardization in support for students with disabilities in medical education."

"Medical education strives for parity and continuity between medical schools, but when it comes to disability services and reasonable accommodations, there's no standardization whatsoever," said Meeks.

"One school could have an incredible specialized disability support services with a qualified disability resource professional running the office, while another school does not have a specialized disability support service at all."

'A wave of change'

The team notes that addressing the second common barrier to attaining needed disability accommodations and fear of stigma or bias requires a continued culture shift in medicine.

"Disability is still incredibly stigmatized, and ableism is rampant in medicine and . At the same time, I think the work from our lab, the Association of American Medical Colleges, the Accreditation Council for Graduate Medical Education and others in medicine started a wave of change that is extraordinarily strong," said Meeks.

This work is bolstered by the matriculation of individuals that Meeks calls the post Americans with Disabilities Act generation into medical .

"This generation has a lot of disability pride. They've had accommodations their entire lives, they know the law, they know their rights and they're not ashamed of being disabled," said Meeks.

Next steps

As this long term study continues, the research team plans to assess how other identities interact with the disability identity.

"People with disabilities have different racial and ethnic backgrounds, sexual orientations and socio-economic statuses. We want to learn more about how the interaction between these different identities impacts the performance and mental health of with disabilities," said Pereira-Lima.

"We're also developing methods to measure the efficacy of accommodations. We need to do more research on the quality of received accommodations and how easy the process was for them to receive the accommodations they needed" added Pereira-Lima.

"Investing in a culture that acknowledges disability as a valuable form of diversity will Boost patient care."

More information: Karina Pereira-Lima et al, Prevalence of Disability and Use of Accommodation Among US Allopathic Medical School Students Before and During the COVID-19 Pandemic, JAMA Network Open (2023). DOI: 10.1001/jamanetworkopen.2023.18310

Citation: Remote learning during pandemic aids medical students with disabilities (2023, August 19) retrieved 23 August 2023 from

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Sat, 19 Aug 2023 03:42:00 -0500 en text/html
Killexams : Medical forms are littered with jargon that nobody understands. Can ChatGPT help? No result found, try new keyword!Doctors at Brown University and Mass. General are using artificial intelligence to create simplified versions of complicated medical consent forms. Wed, 23 Aug 2023 05:15:39 -0500 en-us text/html Killexams : Learn the Basics of Medical Device Packaging

There is a lot to learn if you are making a career transition into or entering the medical packaging field.

Thankfully, the Medical Device Packaging Technical Committee (MDPTC) of the Institute of Packaging Professionals is here to help. This committee invested in developing Fundamentals of Medical Device Packaging to enable rapid learning about medical device packaging.

The course was first offered at Pack Expo International 2022 and featured classroom and expo floor instruction.

If you are considering the course, listen in as five medical device packaging leaders debriefed the merits of this course in a SPOT Radio podcast, by Charlie Webb, Founder and President of Van der Stahl Scientific and CPPL (“lifetime” Certified Packaging Professional).SPOT stands for Sterile Packaging on Track.

The five leaders interviewed are myself, Dannette Casper, Package Engineer, Edward LifeSciences; Sarah Rosenblum, Senior Director of Sales & Marketing, and Cassandra Ladd, Senior Marketing Manager, both at Packaging Compliance Labs; Art Castronovo, Director Package Engineering and Product Labeling at Johnson and Johnson; and Jennifer Benolken, MDM & Regulatory Specialist, Packaging Engineering, DuPont Tyvek, Healthcare Packaging.

Highlights of their conversation include:

1. Synopsis of the new Medical Device Packaging Fundamental’s class, which covers all the steps/functions of packaging a medical device, from design to launch.

2. Review of the premier offering at Pack Expo. It was sold out! And attendees were highly engaged. Their peer-to-peer participation added to the richness of the content presented by the experts.

3. Expectations for the second Medical Device Packaging Fundamental’s class at MD&M West in Anaheim, Calif., The show runs from February 7-9, but the Fundamental’s class starts on the afternoon of February 6 and runs until February 8.

Registration is now open for this second class and you can sign up here. MD&M West is co-located with WestPack.

Wed, 02 Aug 2023 12:00:00 -0500 en text/html
Killexams : Medical residents learn about mindfulness through Crim partnership

FLINT, MI. (WJRT) - A pilot mindfulness program through the Crim Fitness Foundation in Flint is helping medical residents at Hurley Hospital and McLaren Hospital.

The Search Inside Yourself training program is proven to reduce stress, Boost focus and peak performance, and Boost interpersonal relationships.

Crim Fitness Foundation Associate Program Director Marie Jones-Watts said the program was created by an engineer at Google.

“The goal was to increase emotional intelligence, develop empathy, compassion so that the employees could thrive.”

The Crim Fitness Foundation was able to adopt and implement the program and pass the training on to other community leaders.

Crim Fellows Dr. Barbara Wolf and Dr. Anju Sawni said mindfulness is more important than ever for healthcare workers struggling to cope.

“It’s always related to work. You know, you can say I'm burnout at home, but for our definition, it's always related to work. It's a personalization, a level of cynicism, emotional exhaustion, as well as a disconnection from joy or meaningfulness in your work,” said Wolf.

She said the healthcare industry is seeing stress scales jump since the COVID-19 pandemic.

“It has to do with the systems that we live and work in that cause distress. So extra hours, a primary care doctor spends another 30 hours a week besides the four weeks in a month on paperwork, which is not computer work, which they do when they're not at work."

And it hits home for Sawni. She said several years ago, she was seriously considering leaving medicine.

“After 38 years and seeing the change in the health system, I was feeling down and burnt out I really was not having any joy coming to work and I love what I do. I love being a pediatrician. I love taking care of kids my whole life. I love teaching residents. I love teaching medicine. I mean that's really... been my joy in medicine.”

In 2021, Sawni and Wolf brought their mindfulness training to pediatric residents, and then medicine and pediatric residents. Their program involves simple kinds of skill sets and feedback.

“So they're not sitting down for 20 minutes and being quiet. They're maybe putting their hand on the doorknob, where they go into the next patient and breathing in and breathing out kind of separating from the last patient and being ready to face the next one" said Wolf.

Dr. Rashee Gupta was part of the pilot program. She told us she still uses the training as she begins her third year of pediatric residency.

“The body scan is really helpful. So like when I feel tension in my body... you just scan through your body from top to down. It can be done sitting, standing, laying down, and just sort of relax as you move through. So by the end, you are completely relaxed.”

Looking ahead, Swani said the goal is to expand the training to include more resident physicians, and also frontline nurses.

Tue, 15 Aug 2023 07:01:00 -0500 en text/html
Killexams : Deep learning improves protein design by 10x

The key to understanding proteins -; such as those that govern cancer, COVID-19, and other diseases -; is quite simple. Identify their chemical structure and find which other proteins can bind to them. But there's a catch.

"The search space for proteins is enormous," said Brian Coventry, a research scientist with the Institute for Protein Design, University of Washington and The Howard Hughes Medical Institute.

A protein studied by his lab typically is made of 65 amino acids, and with 20 different amino acid choices at each position, there are 65 to the 20th power binding combinations, a number bigger than the estimated number of atoms there are in the universe.

Coventry is the co-author of a study published May 2023 in the journal Nature Communications.

In it his team used deep learning methods to augment existing energy-based physical models in 'do novo' or from-scratch computational protein design, resulting in a 10-fold increase in success rates Tested in the lab for binding a designed protein with its target protein.

We showed that you can have a significantly improved pipeline by incorporating deep learning methods to evaluate the quality of the interfaces where hydrogen bonds form or from hydrophobic interactions."

Nathaniel Bennett, study co-author, post-doctoral scholar at the Institute for Protein Design, University of Washington

"This is as opposed to trying to exactly enumerate all of these energies by themselves," he added.

Readers might be familiar with popular examples of deep learning applications such as the language model ChatGPT or the image generator DALL-E.

Deep learning uses computer algorithms to analyze and draw inferences from patterns in data, layering the algorithms to progressively extract higher-level features from the raw input. In the study, deep learning methods were used to learn iterative transformations of representation of the protein sequence and possible structure that very rapidly converge on models that turn out to be very accurate.

The deep learning-augmented de novo protein binder design protocol developed by the authors included the machine learning software tools AlphaFold 2 and also RoseTTA fold, which was developed by the Institute for Protein Design.

Study co-author David Baker, the director of the Institute for Protein Design and an investigator with the Howard Hughes Medical Institute, was awarded a Pathways allocation on the Texas Advanced Computing Center's (TACC) Frontera supercomputer, which is funded by the National Science Foundation.

The study problem was well-suited for parallelization on Frontera because the protein design trajectories are all independent of one another, meaning that information didn't need to pass between design trajectories as the compute jobs were running.

"We just split up this problem, which has 2 to 6 million designs in it, and run all of those in parallel on the massive computing resources of Frontera. It has a large amount of CPU nodes on it. And we assigned each of these CPUS to do one of these design trajectories, which let us complete an extremely large number of design trajectories in a feasible time," said Bennett.

The authors used the RifDock docking program to generate six million protein 'docks,' or interactions between potentially bound protein structures, split them into chunks of about 100,000, and assign each chunk to one of Frontera's 8000+ compute nodes using Linux utilities.

Each of those 100,000 docks would be split into 100 jobs of a thousand proteins each. A thousand proteins go into the computational design software Rosetta, where the 1,000 are first screened at the tenth of the second scale, and the ones that survive are screened at the few-minutes scale.

What's more, the authors used the software tool ProteinMPNN developed by the Institute for Protein Design to further increase the computational efficiency of generating protein sequences neural networks to over 200 times faster than the previous best software.

The data used in their modeling is yeast surface display binding data, all publicly available and collected by the Institute for Protein Design. In it, tens of thousands of different strands of DNA were ordered to encode a different protein, which the scientists designed.

The DNA was then combined with yeast such that each yeast cell expresses one of the designed proteins on its surface. The yeast cells were then sorted into cells that bind and cells that don't. In turn, they used tools from the human genome sequencing project to figure out which DNA worked and which DNA didn't work.

Despite the study results that showed a 10-fold increase in the success rate for designed structures to bind on their target protein, there is still a long way to go, according to Coventry.

"We went up an order of magnitude, but we still have three more to go. The future of the research is to increase that success rate even more, and move on to a new class of even harder targets," he said. Viruses and cancer T-cell receptors are prime examples.

The ways to Boost the computationally designed proteins are to make the software tools even more optimized, or to trial more.

Said Coventry: "The bigger the computer we can find, the better the proteins we can make. We are building the tools to make the cancer-fighting drugs of tomorrow. Many of the individual binders that we make could go on to become the drugs that save people's lives. We are making the process to make those drugs better."


Journal reference:

Bennett, N. R., et al. (2023) Improving de novo protein binder design with deep learning. Nature Communications.

Wed, 02 Aug 2023 12:00:00 -0500 en text/html
Killexams : GigXR and DICOM Director Debut Three-Dimensional Medical Imagery Learning Using Holograms of CT Scans and MRIs No result found, try new keyword!To demo DICOM XR Library or to learn about licensing, email About GigXR GigXR is a global provider of holographic training solutions for medical and nursing schools, hospitals and ... Thu, 03 Aug 2023 23:43:00 -0500 Killexams : AI (Artificial Intelligence) in Medical Imaging Market to Surpass USD 14,423.15 Million by 2032

Ottawa, Aug. 23, 2023 (GLOBE NEWSWIRE) -- The global AI (Artificial Intelligence) in medical imaging market size is projected to reach around USD 5,448.17 million by 2029, a study published by Towards Healthcare a sister firm of Precedence Research, as a result of the rising number of cross-industry collaborations, and extensive adoption of big data.

AI is Unlocking New Insights and Improving Patient Outcomes with the Power of Big Data, and Machine Learning

Artificial Intelligence (AI) has been rapidly emerging as a revolutionary technology in various industries. In healthcare, AI has the potential to revolutionize medical diagnosis and treatment, particularly in the field of diagnostic medical imaging. Diagnostic imaging, including X-rays, MRI scans, and CT scans, generates vast amounts of data that can be difficult for healthcare professionals to analyze accurately and efficiently. The integration of AI in diagnostic imaging can help overcome this challenge and Boost the accuracy and efficiency of medical diagnosis.

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AI exclusively analyses complex data using computerized algorithms. Diagnostic imaging is one of the most potential clinical applications of AI, and increasing focus is being paid to establishing and optimizing its performance to make it easier to detect and quantify a variety of clinical disorders. Studies utilizing computer-aided diagnostics have demonstrated great specificity, sensitivity, and accuracy for the detection of minor radiographic abnormalities, with the potential to enhance public health. A lot of research is being done on the application of artificial intelligence in diagnostic medical imaging. AI has demonstrated outstanding sensitivity and accuracy in identifying imaging abnormalities, and it has the potential to Boost tissue-based diagnosis and characterization.

AI can help radiation therapy and medical imaging employees work more efficiently by automating repetitive operations. Shorter examination periods and more time for patient care and engagement could be the results of this automation, which would increase patient satisfaction and speed up the time it takes to diagnose an emergency. There are no disputing AI's current and future effects on patient care. By evaluating data that promotes improved public and individual health, AI technologies are proven beneficial in-patient care. One of AI's advantages in medicine is its capacity to handle thousands more data points more quickly than a human ever could. People somewhat rely on learned experience while making these important decisions. Consumer health tracking data can be used by internet-based health indicators to conduct public health research that goes above and beyond what is often possible.

Social indicators can close any remaining gaps in patient health and well-being improvement with data not available from medical sources. AI may collect population data to combine knowledge about communities and populations for better epidemiology, better chronic illness prevention, and better management. Early results on separating benign from malignant nodules on chest CT scans, coupled with neurologic and psychiatric uses, suggest that applying AI to advanced imaging modalities such as CT and MR is already showing significant promise. The use of AI in MR to predict survival in patients with cervical cancer and Amyotrophic Lateral Sclerosis has shown potential, subsequently impacting the market growth.

Transforming Medical Imaging with Big Data and AI

Big data (huge and complex data) is produced at various phases of the care delivery process as a result of the industry's growing digitization and adoption of information systems. Big data in the field of medical imaging includes information derived from clickstream and web & social media interactions, readings from medical devices like sensors, ECGs, X-rays, and other billing records, as well as biometric data and information from other sources. With the increasing acceptance of EHRs, digitized laboratory slides, and high-resolution radiological images among healthcare practitioners over the past ten years, big data and analytical solutions have become exponentially more sophisticated and widely used. Particularly in the US, one of the top five big data businesses in healthcare. In the future years, the volume of big data in medical imaging is expected to rise due to the usage of bidirectional patient portals, which allow patients to upload their digitized medical records, data, and images (EMRs).

The healthcare sector is turning its attention to a variety of artificial intelligence (Al)-based solutions in order to effectively manage the volume of huge and complex medical diagnostic data that is constantly growing. For instance, a senior radiologist would need at least 20 minutes to examine a tomography (CT) scan that has 200–400 images, whereas an AI-based CT scan studying just needs 20 seconds. In 2020, Kang Zhang's team, led by a professor at MUST's Faculty of Medicine, created an AI imaging-assisted detection method for COVID-19 pneumonia and published their findings in Cell. The method was able to distinguish COVID-19 from other viral pneumonia within 20 seconds with an accuracy rate of more than 90%, based on the 500,000 copies of CT images that the team evaluated.

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Adoption Of Ai In Medical Imaging To Reduce Errors

One of the most promising areas of health and medical innovation is the use of AI in medical imaging. Applications for object detection and picture categorization are getting more reliable and accurate with the advancement of machine learning algorithms. Machine learning-based techniques are therefore being used in the healthcare industry. Deep learning techniques are being used, in particular, in a variety of medical applications, including the automatic segmentation of the left ventricle in cardiac MRI and the diagnosis of diabetic retinopathy in retinal fundus images. It is therefore worthwhile to investigate deep learning-based techniques that can be used to locate and count the blood cells in smear images. Medical imaging also uses AI in a variety of ways, including image acquisition, processing for assisted reporting, follow-up planning, data storage, data mining, and more. AI has demonstrated impressive sensitivity and precision in the classification of imaging abnormalities in exact years, and it assures to enhance tissue-based detection and characterization.

Machine Learning (ML) uses computational models and algorithms that mimic the structure of the brain's biological neural networks. Layers of interconnected nodes make up the architecture of neural networks. The input values are weighted summarized at each node in the network and then passed on to an activation function. The adoption of AI in healthcare and medical imaging altered the diagnostic process, which fueled the expansion of the market for AI in medical imaging on a global scale. Additionally, AI plays a crucial role in the processing of a large volume of medical photos, which reveals disease symptoms that are otherwise missed. As a result, it is anticipated that the market for AI in medical imaging will expand during the coming years.

Cross-Industry Collaborations: Driving Innovation and Growth Across Sectors

in June 2021, VUNO Inc., a South Korean AI company, announced a strategic agreement with Samsung Electronics for the integration of the AI-powered VUNO Med-Chest X-ray into the Samsung GM85 mobile digital X-ray system

The use of AI in healthcare and medical imaging has been increasing due to the benefits provided by AI techniques, such as improved accuracy, efficiency, and reduced workload. To develop innovative AI-based solutions for healthcare applications, various companies in the AI medical imaging market have started collaborating with top AI technology providers. By doing so, they can offer advanced solutions to their customers and strengthen their position in the highly competitive market. This trend is expected to continue as the demand for AI-based medical imaging solutions grows. Achieving improvements are motivating businesses to gain momentum in terms of generating cutting-edge products and tools due to the growing demand for AI-based solutions. However, there are a number of sectors where the impact of AI technology is anticipated to be significant. Enhanced image diagnosis, predictive image analysis, workflow efficiency with data analysis, report production, and prioritizing of important discoveries are some examples of this. 

The market players in the field of AI in medical imaging are likely to benefit from this by creating new opportunities. A cooperation between AccentCare and Swift Medical to supply technologies to thousands of healthcare practitioners was announced in June 2020 by the American healthcare corporation. This will help the authorities decide on treatments quickly. A multi-year strategic collaboration to conduct research with Mayo Clinic was signed, according to Visage Imaging, a division of Pro Medicus Limited, an Australian business, in June 2020.

Breaking Down Barriers: Addressing Medical Practitioners' Concerns about AI in Healthcare

Healthcare professionals can now help patients through cutting-edge treatment modalities due to the extensive rise of digital health. With the use of modern technology, doctors can better diagnose and treat their patients. However, it has been noted that doctors are reluctant to adopt new technologies. For instance, doctors mistakenly believe that Al will take the place of doctors in the near future. Radiologists and doctors concur that human abilities like empathy and persuasion exist. Therefore, the use of technology cannot entirely replace the need for a doctor. Furthermore, it is feared that patients may have an unhealthy preference for these technologies and avoid crucial in-person therapies, which could strain long-term doctor-patient relationships. To persuade providers that the Al-based solutions are affordable, efficient, and secure alternatives that allow the doctors with convenience and better patient care, the argument is evolving.

However, healthcare organizations are beginning to recognize the range of uses and potential advantages of Al-based solutions. As a result, it is possible that in the years to come, medical professionals like radiologists will be more likely to use Al-based technology. Since radiologists and practitioners view the AI technology cannot completely replace the need for a specialist. However, many medical professionals are also currently skeptical about the capabilities of AI systems to accurately diagnose patient diseases. Therefore, it is difficult to convince the providers that AI-based solutions are affordable, useful, and secure solutions that support physicians and Boost patient care. Thus, it can hamper the growth of the market.

Building Trust in AI: The Role of Human-Awareness

The actual goals during the development of Al technologies were to make them human-aware or to create models that resembled how people actually think. A system that combines human intelligence and an artificial intelligence (AI) approach is known as human-aware technology or human-aware augmented intelligence. The concept is that it can perform better than either a human or a piece of technology can by itself. Human-aware technology incorporates elements of intelligence that support human collaboration, such as emotional and social intelligence, and works in harmony with humans. As human-aware technology progressively permeates our daily lives, there are no limits to what it can do. Human-aware technology can Boost human capacities and pave the road for human achievement, from intelligent prostheses to intelligent coaching.

However, building interactive and scalable AI machines that model human mental states in the loop, recognize their desires and intentions, offer proactive support, exhibit explicable behavior, provide cogent explanations upon request, and inspire trust continue to be challenges for AI machine developers. Additionally, growth in human involvement with AI approaches and interest in learning about machine learning has created new research obstacles, such as those related to interpretation and presentation, issues with automated components, and intelligent crowdsourced control. Al machines have trouble understanding particular instructions and information when it comes to interpretation. Presentation challenges include issues related to delivering the AI system's output and feedback mechanisms Thus, developing the human-aware AI systems remains the foremost opportunity for the AI developers.

The Promise of AI in Medical Imaging: How Hospitals and Clinics Are Leveraging This Technology

AI has the potential to revolutionize medical imaging, and hospitals and clinics are already beginning to adopt the technology. The use of AI in medical imaging has several potential benefits, including increased accuracy, improved efficiency, and reduced costs. One of the primary ways that AI is being used in medical imaging is through computer-aided diagnosis (CAD) systems. These systems use machine learning algorithms to analyze medical images and identify potential areas of concern. For example, in mammography, AI can be used to identify potential breast cancer lesions, helping radiologists make more accurate diagnoses. Similarly, in lung imaging, AI can be used to identify potential nodules or tumors.


As there are numerous scenarios in which a patient can require medical imaging, hospitals and healthcare organizations are implementing AI-powered imaging systems. Whether it is a heart event, fracture, neurological disorder, or chest problem, AI can instantly assess the condition and offer therapeutic choices. The majority, if not all, of the resources used by nearly one-third of all AI SaaS businesses in the healthcare sector, are allocated to diagnostics. In diagnostic medical imaging, artificial intelligence (AI) is being carefully examined. AI has demonstrated outstanding sensitivity and accuracy in detecting picture anomalies, and it has the potential to enhance tissue-based detection and characterization. AI in hospitals is now able to assist clinicians as a thorough diagnosis tool that will alter the results of many cases. Many different types of cancer are notoriously difficult to diagnose and treat. As a result, to carefully check the tissue for cancer cells, hospitals, and other healthcare facilities are implementing machine-learning software and AI imaging techniques. A hospital must also have a patient flow that is optimized in order to diagnose as many patients as feasible quickly. Each patient receives quick care owing to the use of AI in imaging.

Related Reports:

  • AI In MRI Market: The global artificial intelligence (AI) in magnetic imaging (MRI) market size was estimated at USD 5.77 billion in 2022 and is expected to hit around USD 9.8 billion by 2030 with a registered CAGR of 6.85% from 2022 to 2030.
  • AI In Life Sciences Market: The global artificial intelligence in life sciences market size was estimated at US$ 1.3 billion in 2021 and it is expected to reach around US$ 6.7 billion by 2030 with a registered CAGR of 20% from 2022 to 2030.
  • AI in Drug Discovery Market: The global AI in drug discovery market size was estimated at US$ 1153.6 million in 2021 and is projected to hit around US$ 11,914 million by 2030, registering growth at a CAGR of 29.62% from 2022 to 2030.
  • AI in Genomics Market: The global AI in genomics market is estimated to grow from $397.64 million in 2022 at 40.31% CAGR (2022-2030) to reach an estimated $ 5,972 million by 2030.
  • Generative AI in Healthcare Market: The global generative AI in healthcare market is estimated to grow from USD 1.07 billion in 2022 at 35.1% CAGR (2023-2032) to reach an estimated USD 21.74 billion by 2032.

CT Imaging in the Age of AI: Balancing Promise and Pitfalls

Computed tomography (CT) imaging is a widely used diagnostic tool that provides detailed images of the inside of the body. It involves the use of X-rays and computer algorithms to create 3D images of the body, which can be used to diagnose a wide range of medical conditions.

In exact years, the use of artificial intelligence (AI) in CT imaging has become increasingly common, with the potential to Boost accuracy and efficiency in diagnosis. AI algorithms can be trained to identify subtle changes in the CT images that might be missed by a human radiologist. This can lead to earlier detection of disease, improved accuracy in diagnosis, and better treatment outcomes for patients.

However, there are also potential pitfalls associated with the use of AI in CT imaging. One concern is that relying too heavily on AI algorithms could lead to a decrease in the quality of care, as radiologists may become less skilled at interpreting images without the help of AI. Additionally, there is a risk of overdiagnosis and overtreatment if AI algorithms are too sensitive, leading to unnecessary medical procedures and increased healthcare costs.

To balance the promise and pitfalls of AI in CT imaging, it is important to consider several factors. First, radiologists must be properly trained in the use of AI algorithms to ensure that they are used effectively and appropriately. Second, AI algorithms must be validated in large-scale studies to ensure their accuracy and reliability. Third, there must be clear guidelines in place for how AI should be used in CT imaging, to prevent overdiagnosis and overtreatment.

Thus, the use of AI in CT imaging holds great promise for improving patient outcomes and advancing the field of medical imaging. However, it is important to approach this technology with caution and to carefully balance its potential benefits with the potential risks and pitfalls. By doing so, we can ensure that AI in CT imaging is used to its fullest potential, while still providing high-quality care to patients.

The Global Impact of AI in Medical Imaging: Insights from Key Regions

North America is currently the leading region in the global AI in medical imaging market, due to the high adoption of advanced healthcare technologies, increasing prevalence of chronic diseases, and significant investments in research and development. The United States is the largest market in this region, with several leading players such as GE Healthcare, IBM Watson Health, and Philips Healthcare.

The Asia-Pacific region is expected to witness significant growth in the AI in medical imaging market due to the increasing demand for advanced medical imaging technologies, growing geriatric population, and rising healthcare expenditure in countries such as China, India, and Japan. The major players in this region include Hitachi, Ltd., Fujifilm Holdings Corporation, and Koninklijke Philips N.V.

Market Key Players:

  • AI
  • AZmed
  • Butterfly Network
  • Agfa-Gevaert Group/Agfa HealthCare
  • Arterys
  • Caption Health
  • CellmatiQ
  • dentalXrai
  • Digital Diagnostics
  • EchoNous
  • Paige AI
  • Perimeter Medical Imaging AI
  • HeartVista
  • iCAD
  • Lunit
  • Mediaire
  • MEDO
  • Nanox Imaging
  • Predible Health
  • 1QB Information Technology
  • Quantib
  • Quibim
  • Renalytix
  • Therapixel
  • Ultromics
  • VUNO

Market Segmentation:

By AI Technology

  • Deep Learning
  • Natural Language Processing (NLP)
  • Others

By Solution

  • Software Tools/ Platform
  • Services

By Modality

  • CT Scan
  • MRI
  • X-rays
  • Ultrasound Imaging
  • Nuclear Imaging

By Application

  • Digital Pathology
  • Oncology
  • Cardiovascular
  • Neurology
  • Lung (Respiratory System)
  • Breast (Mammography)
  • Liver (GI)
  • Oral Diagnostics
  • Other

By End Use

  • Hospital and Healthcare Providers
  • Patients
  • Pharmaceuticals and Biotechnology Companies
  • Healthcare Payers
  • Others

By Geography

  • North America
  • Europe
  • Asia-Pacific
  • Latin America
  • Middle East & Africa (MEA)

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Wed, 23 Aug 2023 03:30:00 -0500 text/html
Killexams : Obesity impairs brain's ability to learn associations

To control our behavior, the brain must be able to form associations. This involves, for example, associating a neutral external stimulus with a consequence following the stimulus (e.g., the hotplate glows red - you can burn your hand). In this way, the brain learns what the implication of our handling of the first stimulus are. Associative learning is the basis for forming neural connections and gives stimuli their motivational force. It is essentially controlled by a brain region called the dopaminergic midbrain. This region has many receptors for the body's signaling molecules, such as insulin, and can thus adapt our behavior to the physiological needs of our body.

But what happens when the body's insulin sensitivity is reduced due to obesity? Does this change our brain activity, our ability to learn associations and thus our behavior? Researchers at the Max Planck Institute for Metabolism Research have now measured how well the learning of associations works in participants with normal body weight (high insulin sensitivity, 30 volunteers) and in participants with obesity (reduced insulin sensitivity, 24 volunteers), and if this learning process is influenced by the anti-obesity drug liraglutide.

In the evening, they injected the participants with either the drug liraglutide or a placebo in the evening. Liraglutide is a so-called GLP-1 agonist, which activates the GLP-1 receptor in the body, stimulating insulin production and producing a feeling of satiety. It is often used to treat obesity and type 2 diabetes and is given once a day. The next morning, the subjects were given a learning task that allowed the researchers to measure how well associative learning works. They found that the ability to associate sensory stimuli was less pronounced in participants with obesity than in those of normal weight, and that brain activity was reduced in the areas encoding this learning behavior.

After just one dose of liraglutide, participants with obesity no longer showed these impairments, and no difference in brain activity was seen between participants with normal weight and obesity. In other words, the drug returned the brain activity to the state of normal-weight subjects.

"These findings are of fundamental importance. We show here that basic behaviors such as associative learning depend not only on external environmental conditions but also on the body's metabolic state. So, whether someone has overweight or not also determines how the brain learns to associate sensory signals and what motivation is generated. The normalization we achieved with the drug in subjects with obesity, therefore, fits with studies showing that these drugs restore a normal feeling of satiety, causing people to eat less and therefore lose weight," says study leader Marc Tittgemeyer from the Max Planck Institute for Metabolism Research.

"While it is encouraging that available drugs have a positive effect on brain activity in obesity, it is alarming that changes in brain performance occur even in young people with obesity without other medical conditions. Obesity prevention should play a much greater role in our healthcare system in the future. Lifelong medication is the less preferred option in comparison primary prevention of obesity and associated complications," says Ruth Hanßen, first author of the study and a physician at the University Hospital of Cologne.

Wed, 16 Aug 2023 11:59:00 -0500 en text/html
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