Did you know: Representative Data We'd like to point out some key statistics from our last post on small study sizes. First of all, the question this article is trying to respond is about the prevalence and extent of small study effects in diagnostic imaging. Reach out to us to know how you can have quick access to millions of diverse medical imaging data and avoid data bias: https://lnkd.in/gVwPPXUB

Artificial intelligence (AI) has managed to attract the attention of physicians and doctors. The field of medicine is now upgrading to another level with AI that is helpful for diagnostic imaging of patients' conditions. Previously, doctors were faced with the challenge of analyzing overloads of patients' information. AI is currently proofing to be beneficial because it is helping doctors by boosting their ability to find relevant data required to treat a patient more easily. When a radiologist performs a chest computed tomography (CT); the AI will review the patient's image and recognize potential results immediately.

If you hear those terms without context, what does your mind first go to? Do you think of robots dominating humanity in the next 10, 100, or 1000 years? Do you think of Siri, Alexa, or Google? Or do you think of detecting illness or conditions that can be observed through medical imaging, like cancer? Although not always recognized by mainstream media, AI is extremely versatile in its applications and has significant potential in areas that could impact human and veterinary medicine, such as diagnostic imaging.

The Confederation of Laboratories for Artificial Intelligence Research in Europe (CLAIRE) taskforce on AI & COVID-19 supported the creation of a research group focused on AI-assisted diagnosis of COVID-19 pneumonia. The first results demonstrate the great potential of AI-assisted diagnostic imaging. Furthermore, the impact of the taskforce work is much larger, and it embraces the cross-fertilisation of artificial intelligence (AI) and high-performance computing (HPC): a partnership with rocketing potential for many scientific domains. Through several initiatives aimed at improving the knowledge of COVID-19, containing its diffusion, and limiting its effects, CLAIRE's COVID-19 taskforce was able to organise 150 volunteer scientists, divided into seven groups covering different aspects of how AI could be used to tackle the pandemic. Emanuela Girardi, the co-coordinator of the CLAIRE taskforce on AI & COVID-19, supported the setup of a novel European group to study the diagnosis of COVID-19 pneumonia assisted by artificial intelligence.

It is no secret that mammography services faced a significant set-back this year when the COVID-19 pandemic erupted. Eventually, the service line was able to rebound from the catastrophic 92-percent plummet it experienced during the summer months – but, that was not all that happened. That recovery was, without a doubt, a success, but it was by no means the only positive development with mammography during 2020. This was the year to watch advances in artificial intelligence tools in breast imaging. To pinpoint the ones that will be most impactful, Diagnostic Imaging spoke with Randy Miles, M.D., MPH, assistant professor of radiology at Harvard Medical School.

That was really interesting, thank you Patrick for joining us. Patrick Brennan: It was a pleasure, thank you. Norman Swan: Professor Patrick Brennan, who is Professor of Diagnostic Imaging at the University of Sydney. I'm Norman Swan, this has been the Health Report on RN. And don't forget the Coronacast, our daily podcast on all things to do with the coronavirus that Tegan Taylor and I present. You can download it by going to Apple Podcasts, the ABC Listen app, or wherever you get your podcasts. I'll see you next week.

The arrival of artificially intelligent systems into the domain of medical imaging has focused attention and sparked much debate on the role and responsibilities of the radiologist. However, discussion about the impact of such technology on the radiographer role is lacking. We also highlight the opportunities that AI brings including enhancing patient-facing care, increased cross-modality education and working, increased technological expertise and expansion of radiographer responsibility into AI-supported image reporting and auditing roles.

AI is rocking medical diagnosis with its potential to incite drastic improvements to hospital processes. AI can process images and patient health records with more accuracy and expediency than humans are capable of, lessening physician workload, reducing misdiagnosis, and empowering clinical staff to provide more value. While early moving hospitals are already extracting value from AI in medical diagnosis, most US hospitals are at the very early stage of the AI transformation curve -- and they risk falling behind if they don't move now. In this report, Business Insider Intelligence examines the value of AI applications in three high-value areas of medical diagnosis -- imaging, clinical decision support, and personalized medicine -- to illustrate how the tech can drastically improve patient outcomes, lower costs, and increase productivity. We look at US health systems that have effectively applied AI in these use cases to illustrate where and how providers should implement AI.

Earlier this week, a consensus draft document dealing with the ethics of AI in medical imaging was posted on the ACR website. I would like to congratulate the authors, listed with their affiliations below, on a collaborative effort to address this important topic. This was a multi-society effort including the American College of Radiology (ACR), American Association of Physicists in Medicine (AAPM), Canadian Association of Radiologists (CAR), European Society of Radiology (ESR), Radiological Society of North American (RSNA), Society for Imaging Informatics in Medicine (SIIM) and European Society of Medical Imaging Informatics (EuSoMII). Importantly, the group included trainees, patients and other stakeholders such as an ethicist from MIT. But despite the wide ranging backgrounds and expert input that created this draft, the writing group and our Societies' leaders are very clear that this is just that: a draft.

To say artificial intelligence (AI) is transforming healthcare would be an understatement. Thanks to enormous advancements in computer processing power, as well as the increase of data collection at the patient, clinician and institutional level, AI is now driving the digital healthcare revolution. This transformation has been visibly apparent within the fields of medical diagnoses, drug discovery, e-health, and electronic health records. In particular, the field of medical diagnoses is where AI and deep learning have shown the most use cases. This is largely due to the recent developments in computer vision and object recognition, especially from 2012 when AlexNet, a convolutional network, won the ImageNet Large Scale Visual Recognition Challenge.