Moderna and IBM announced their intentions to explore technologies, including artificial intelligence, blockchain and hybrid cloud, that could help support smarter Covid-19 vaccine management. Central to the effort will be a pilot of open, standardized, technology-enabled vaccine distribution approaches aimed to improve supply chain visibility and foster near real-time tracking of vaccine…
Parkinson’s disease (PD) usually causes facial abnormalities that affect a person’s self-esteem and relationships. However, these abnormalities are difficult to analyze quantitatively with current methods. To address this issue, scientists from Okayama University, Japan, explored whether commercial AI-based facial analysis software could be useful in this context. Their results show that…
Systematic review finds that machine learning models for detecting and diagnosing Covid-19 from medical images have major flaws and biases, making them unsuitable for use in patients. However, researchers have suggested ways to remedy the problem. Researchers have found that out of the more than 300 Covid-19 machine learning models described in scientific papers in 2020, none of them is suitable…
The results of a large-scale study indicate that the introduction of an AI-powered solution by healthcare software company Radlogics into radiology workflow to analyze Chest-CT scans during the Covid-19 pandemic reduced report turnaround time by an average of 30 percent, which is equivalent to 7 minutes per case.
In the move to evidence-based medicine, healthcare budgets put more pressure on efficiency, while quality of care has to meet ever increasing standards. Agfa has chosen to direct its development of artificial intelligence (AI) solutions towards helping radiology departments meet these challenges. Agfa’s SmartXR AI upgrades for its digital radiography portfolio focus on supporting operational…
AI is revamping workflows and experts showed how radiologists can integrate it into their department to improve daily practice and healthcare at ECR. The panel also discussed the health economics side of AI to help radiologists define which products make more economic sense for their department. The session tackled automated organ segmentation, an interesting application for AI in radiology.
Smart speakers, such as Amazon Echo and Google Home, have proven adept at monitoring certain health care issues at home. For example, researchers at the University of Washington have shown that these devices can detect cardiac arrests or monitor babies breathing. But what about tracking something even smaller: the minute motion of individual heartbeats in a person sitting in front of a smart…
Researchers at TU Berlin and Charité – Universitätsmedizin Berlin as well as the University of Oslo have developed a new tissue-section analysis system for diagnosing breast cancer based on artificial intelligence (AI). Two further developments make this system unique: For the first time, morphological, molecular and histological data are integrated in a single analysis. Secondly, the system…
One of the leading figures in global radiology has highlighted the importance of fostering a strong eco-system to advance the safe and effective implementation of AI in medical imaging. Dr Geraldine McGinty said that to fully leverage the power of AI, all stakeholders must work together but underlined the unique responsibility physicians have to ensure patient interests are best served.
With a growing number of Artificial Intelligence (AI) algorithms to support medical imaging analysis, finding the best solution for specific radiology tasks is not always straightforward. Many are cloaked in secrecy and commercial sensitivity and while an algorithm may perform well in one area, evidence of performance and adaptability in another environment is not always available.
Implementation of artificial intelligence into Scotland’s national breast screening service is moving closer following an initial success with a trial project. While Scotland’s breast screening trial has delivered highs and lows, significant hurdles have been overcome in terms of approvals, governance and patient acceptance.
A new deep learning system to help radiologists improve their workflow efficiency when reading high volumes of screening mammograms is being developed at Johns Hopkins University’s Radiology Artificial Intelligence Lab (RAIL) in Baltimore, MD. DeepCAT (Deep Computer-Aided Triage) is focused on workflow prioritization.
The impact of Covid-19 has been devastating; countless lives lost, millions of infections worldwide, health systems overwhelmed and economies rapidly shrinking. Screening, such as for breast or cervical cancer, is an area particularly affected, with numerous appointments delayed.
The management of biopsied breast lesions that are diagnosed as abnormal but are not definitively malignant is challenging and controversial. Treatment ranges from diligent follow-up, with imaging and subsequent biopsy, to surgical excision. Researchers at the Medical University of Vienna (Medizinische Universität Wien), Austria, have developed and validated a software algorithm designed to…
Improving workflow is one of the major challenges that radiology departments face. The need to be more efficient, deliver timely and effective patient care, and keep an eye on costs are all factors that seem to be ever-present in the modern imaging department. With the added demands of the coronavirus pandemic as radiology departments continue to play a critical role in the fight against…
Advances offered by Big Data and Artificial Intelligence within the healthcare environment are opening up new opportunities for radiographers. Roles in systems development, and being part of the safeguarding aspect by ensuring machine-based bias does not take over patient management, may be performed by radiographers alongside other contributions as AI plays an increasing role in healthcare.…
Carestream Health will highlight cutting-edge medical imaging technologies at the largest radiology meeting in Europe—the virtual European Congress of Radiology (ECR) in Vienna, Austria, beginning on March 3. The company will feature a wide range of products that demonstrates its leadership in digital medical imaging capture and processing, and improved user and workflow experiences.
Doctors and healthcare workers may one day use a machine learning model, called deep learning, to guide their treatment decisions for lung cancer patients, according to a team of Penn State Great Valley researchers. In a study, the researchers report that they developed a deep learning model that, in certain conditions, was more than 71% accurate in predicting survival expectancy of lung cancer…
"Given the rapid transition towards digitisation, digital pathology is now unquestionably the future", reports pathologist Dr Talat Zehra from Pakistan. "However, some pathologists, particularly in underdeveloped countries, are still reluctant to accept its place in their labs. Among their many reasons, some feel that histopathology is a very complex and subjective field and…
Researchers in Oxford have developed a machine learning algorithm that could significantly improve clinicians’ ability to identify hospitalised patients whose condition is deteriorating to the extent that they need intensive care. The HAVEN system (Hospital-wide Alerting Via Electronic Noticeboard) was developed as part of a collaboration between the University of Oxford’s Institute of…
A way of using machine learning to more accurately identify patients with a mix of psychotic and depressive symptoms has been developed by researchers at the University of Birmingham. Patients with depression or psychosis rarely experience symptoms of purely one or the other illness. Historically, this has meant that mental health clinicians give a diagnosis of a ‘primary’ illness, but with…
USC researchers have developed a new method to counter emergent mutations of the coronavirus and hasten vaccine development to stop the pathogen responsible for killing thousands of people and ruining the economy.
In order to detect the Corona virus SARS-CoV-2, there are further methods of diagnosis apart from the globally used PCR tests (Polymerase chain reaction): The infection can also be recognised on CT scans – for which Artificial Intelligence (AI) can be used as well. An AI system can not only filter CT scan of Covid-19 patients from a data set, but also estimate, which areas of the image are of…
Coronavirus-associated concerns dominate the Top 10 list of important technology hazard risks for hospitals, in an annual report published by ECRI, a nonprofit technology Pennsylvania research firm. The list is derived from ECRI’s team of technology experts who monitor hospital and healthcare organizations, and published to inform healthcare facilities about important safety issues involving…
A new EU-wide repository for health-related imaging data could boost development and marketing of AI tools for better cancer management. The open-source database will collect and harmonise images acquired from 40,000 patients, spanning different countries, modalities and equipment. This approach could eliminate one of the major bottlenecks in the clinical adoption of AI today: Data bias.