European Union

Among the many highlights of EDA’s recent Annual Conference (see other news) was certainly the high-level panel discussion on ‘Increasing European defence cooperation in times of crisis’, featuring the Greek Minister of Defence, Nikólaos Panayotópoulos, and Nathalie Loiseau, the Chair the European Parliament’s Subcommittee on Security and Defence (SEDE). The interesting and animated debate, which can be reviewed here, was moderated by EDA Chief Executive Jiří Šedivý.

Welcoming the timely focus on 'Sustaining European Defence' (the topic of this year's Annual Conference), Nikólaos Panayotópoulos recalled Member States’ commitment, reflected in the Council conclusions of November 2016, to strengthening the Union’s ability to act as a security provider and to enhance the Common Security and Defence Policy (CSDP) as an essential part of the Union’s external action. “This means, in particular, that we need a more integrated and coherent approach across our different policies, internal and external, to better pursue our strategic interests, through international cooperation”, the Greek Defence Minister said, adding: “Member States should make full use of the EU defence initiatives and deepen their embedding in the national defence planning in order to achieve a better consistency between one another and reach strategic autonomy”.

The fact that nine PESCO projects have already received financial support under the European Defence Industrial Development Programme (EDIDP) is a positive development showing that the initiatives work, he said. Greece received funding approval from EDIDP for two PESCO projects it leads (‘Cyber Threats & Incident Response Information Sharing Platform’ and ‘Upgrade of Maritime Surveillance’). “This is a clear evidence of the coherence between the initiatives”. In the Minister’s view, enhanced cooperation and joint capability development among EU countries will not only improve Europe’s defence capabilities, but also help reinforce its industrial base and technological capacity “which is a fundamental aspect of EU strategic autonomy”. “I believe that spending more on defence will also strengthen the economic recovery. As the European Defence Agency has already indicated, EU countries should spend more on defence research and technology”, Mr Panayotópoulos stated: “We should sustain the trend of increasing national defence expenditures towards 2% of the GDP. I am proud that Greece not only fulfills but is about to exceed its commitment on annual defence spending”. 

The Minister also defended the EU’s operation IRINI in the Mediterranean (enforcement of the UN arms embargo on Libya) as a “question of political will and political courage”. People throughout Europe must understand that it is not a threat against one or two Member States, but against the entire Union. “Greece, precisely for this reason, and despite the challenges and provocations at our eastern sea and land borders and the Covid-19 outbreak, is substantially contributing to the Operation in terms of personnel and assets”, he explained. The Minister called for “unity and solidarity” among all EU countries. “We cannot discuss on strengthening our operational engagement and reinforcing our resilience without ensuring solidarity between us. Through the recent threat analysis we made a first step within the process of Strategic Compass to better understand and share a perception of threats, thus moving to the progressive framing of a common Union defence policy. We have common threats and need common responses”. EU-NATO cooperation is a “key partnership” which we should reinforce, while our commitment to work with the UN in the field of security and defence should be enhanced, he said.

Nathalie Loiseau, the Chair of the SEDE committee, shared Mr Panayotópoulos’ positive assessment on the progress made on EU defence cooperation. There is indeed a “clear, common and shared ambition in the Council, the Commission and the European Parliament to enhance the efforts towards a European defence”, Ms Loiseau said, adding: “We have done more for EU defence in the last four years than we did in the previous four decades”. In a world that is becoming less predictable and more dangerous, “soft power is not enough”, she stressed (Greek Minister Panayotópoulos strongly agreed with her on this point: “In terms of soft power, the Europe is a superpower. In terms of hard power, it is lagging behind”, he said). Therefore, European countries need to enhance their defence capabilities, and cooperation is the best and most efficient way to do this. The EU’s new defence toolbox (CARD, PESCO, EDF) is in place and this is “very encouraging”, but everyone must admit that this is only the first step and that there is still some room for improvements, Ms Loiseau said. In particular, PESCO projects must become “more focused”. Improvements are also needed when it comes to EU CSDP missions and operation which often face problems during the force generation phase and whose mandates are “not always as strong as they should be”. Here, Member States need to show more commitment and “combine words with deeds”, Ms Loiseau urged. 

Regarding the debate about EU strategic autonomy, Ms Loiseau felt it was somewhat “overstated” because “no one is denying the importance of NATO” for Europe’s defence. What Europe wants is to become a more credible transatlantic and strategic partner, which is meant to be complementary with NATO.  “I strongly believe we need to revive NATO because NATO has to adapt (…) and we need to strengthen European strategic autonomy because NATO is not enough and will never be”, Ms Loiseau said.
 

More information

• Opening speech Head of Agency HR/VP Josep Borrell
• Keynote Speech Commissioner Thierry Breton (via video message)
• Full recording of EDA Annual Conference 2020 – Day 1 / Day 2 (not edited)

Declaration by the High Representative on behalf of the European Union on the alignment of certain third countries with Council Decision (CFSP) 2020/1999 of 7 December 2020 concerning restrictive measures against serious human rights violations and abuses

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Written by Mihalis Kritikos,

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Artificial intelligence (AI) solutions can help radiologists with the triage, quantification and trend analysis of patient data. AI-powered medical imaging is already used to detect critical diseases, and medical imaging has played a significant role in the fight against Covid-19, easing the pressure on healthcare systems. Although AI imaging as a diagnostic tool is still surrounded by various challenges and uncertainties, its use in the context of Covid-19 has assisted clinicians with its faster image-processing times – as little as 10 seconds compared with up to 15 minutes for a manual reading of a computerised tomography (CT) scan.

Medical imaging has always been one of the most advanced areas of AI application showing remarkable accuracy and sensitivity in the identification of imaging abnormalities. In the context of Covid-19, medical imaging has facilitated incidental diagnosis, offering supporting evidence in clinical situations where false negative RT-PCR tests are suspected and helping evaluate treatment outcomes, disease progression and anticipated prognosis.

AI-empowered image processing can automate searches through large databases and deliver more precise demarcation of infections in X-ray and CT images, facilitating fast evaluation of CT scans and identification of Covid-19 findings. Clinicians and radiologists can use machine learning (ML) algorithms to examine information contained in medical scans or images as these provide better tools for localisation and quantification of disease features. The result can be better early detection, diagnostic performance and prognostic value while also easing the burden on laboratory testing. Could these AI systems, with their several advantages, replace human (medical) judgement in the context of the Covid-19 pandemic?

Potential impacts and developments

AI-supported medical imaging can be vital in the fast detection and classification of Covid-19, as it can immediately flag chest CT scans showing suspected Covid-19 allowing the patients concerned to be tested promptly. Image recognition algorithms can bring together and analyse chest CT scan findings, clinical symptoms, exposure patterns and other forms of testing, thus providing clinicians and clinical decision-making systems in general with essential information.

Using well-curated medical imaging data, AI algorithms can be developed, trained and validated so as to anticipate possible clinical deterioration or improvement. These evidence-based predictions could in effect help hospitals plan workflow in an emergency context such as the current one. They would provide consistent, quantifiable information to evaluate precisely the gravity of a patient’s illness, enabling medical personnel to effectively triage patients and thus alleviate the ever-growing patient backlog.

Recently, a new algorithm has been developed combining CT images of patients’ lungs with non-imaging data to identify Covid-19-positive patients who require immediate intervention; another, meanwhile, offers an automated tool for rapid identification of patients with suspicious chest imaging for isolation and further testing.

AI can reduce the time taken in the medical imaging process by examining thousands of images from a chest CT scan. It can also increase patient safety by improving X-ray exposure parameters and producing low-dose CT scans. AI-empowered visual sensors can meanwhile accelerate scanning, automate risk stratification and, in effect, reduce unnecessary radiation exposure in the clinical setting.

AI medical imaging models have been deployed in a number of hospitals around the world. The US Food and Drug Administration (FDA) has authorised the use of AI algorithms that detect Covid-19 in partially imaged lungs as an incidental finding, whereas the EU is funding the Imaging Covid-19 AI initiative, a multi-centre European project, to enhance the use of CT in the diagnosis of Covid-19 by using AI. Last but not least, a group of Belgian hospitals have recently developed the first CE-marked AI solution for CT that offers fast quantification of lung pathology on chest CT scans in Covid-19 patients. Are these initiatives sufficient to cope with the current needs for safe and accurate AI-powered diagnostic tools? Or are more multicentre and multidisciplinary clinical studies needed to address the current knowledge gaps?

Anticipatory policy-making

Along with great benefits, the introduction of AI to medical imaging also raises a significant number of legal questions and ethical considerations. The deployment of AI in the context of the current pandemic is also subject to numerous challenges that could undermine the accuracy and usefulness of its eventual clinical findings. These relate to the overall gap in knowledge of the long-term effects of Covid-19 and the lack of historical data to enable training on large-scale prognosis data. The result is the over-use of small incomprehensive public datasets and a combined lack of robustness and interpretability of AI models in clinical practice. One additional important diagnostic challenge lies with the non-specificity of Covid-19 patterns and their differentiation from non‑Covid‑19 viral pneumonia or asymptomatic patients with unaffected lungs.

The primary challenge in this context is that of accessing large volumes of data for AI development and the lack of representative data to train and validate algorithms. As the effectiveness of AI-supported devices relies on the accuracy of training data, grounding modelling and clinical decisions on sub-optimal data may compromise accuracy and reliability and result in deficient medical diagnoses. In fact, the accelerating development of AI‑based diagnostic tools in response to the current pandemic has brought to the fore the absence of standardised protocols for training and validating ML algorithms in this domain and the lack of large and diverse image datasets from a variety of certified sources, as required to train the algorithm.

The training, testing and eventual validation of AI-based algorithms for use in the current public health crisis requires access to large and curated datasets developed in accordance with existing privacy norms and data protection rules. The absence of such large datasets means AI-supported medical scans may be biased by technical factors owing to subtle differences in data from different scanning techniques or ill-curated data that train algorithms and deep networks on Covid-19. The integration of AI techniques in radiology in this particular context also raises questions about the ethics of the procedures and protocols followed for collecting and processing this medical data, including issues of informed consent, privacy and data ownership.

Under the General Data Protection Regulation, patients must give prior informed explicit consent for the use of their medical scans and imaging data in developing an AI algorithm, and this must be renewed before the design and training of each new version. Is that plausible given the time pressure to deliver fast clinical findings and solutions? The grounding of diagnostic decisions on AI-powered processing also raises liability questions: who should be held liable for an ineffective medical diagnosis? The doctor or the software developer?

Under the EU Medical Devices Regulation, a radiologist could be held liable if they depart from the AI-powered diagnostic medical imaging equipment’s diagnosis. However, the question is whether or not these AI algorithms have been subject to the same rigorous pre-market authorisation and auditing standards followed for the assessment and eventual deployment of other medical devices? Or have these regulatory procedures been fast-tracked owing to the urgent demand for Covid-19 related diagnostic solutions?

Beyond the issue of the availability, quality and representativeness of the datasets used to train algorithms, the majority of hospitals lack the technological infrastructure, manpower and knowhow to manage these complex AI systems effectively, since most of them use outdated computer-assisted diagnostics tools or only perform visual checks on medical scans.

Uptake of image recognition AI in medical diagnostics currently sits between 1 and 20 % depending on the disease area. Consequently, the use of AI-powered imaging in resource-limited settings remains a major technological and policy challenge that must be addressed as a matter of urgency, not least because of its potential benefits in boosting public health systems’ capacity to cope with the current extraordinary global health crisis.

Read this ‘at a glance’ on ‘What if artificial intelligence in medical imaging could accelerate Covid-19 treatment?‘ in the Think Tank pages of the European Parliament.

Bulgarian diplomat Nickolay Mladenov has told UN Secretary-General Antonio Guterres he will be unable to take up the role as United Nations Libya envoy next year due to “personal and family reasons,” a UN spokesman said on Tuesday (22 December).

UN humanitarian affairs coordinator Mark Lowcock said on Tuesday that European countries would be dealing with Covid-19 in 2022 and beyond, as low-income countries are only expected to gain access to vaccines late next year, The Guardian reported. "It is highly likely the places where the virus finds it is easier to survive longest will be fragile, conflict-affected states with weak institutions, but the virus can get everywhere," Lowcock said.

President Donald Trump granted a full pardon on Tuesday (22 December) to George Papadopoulos, a former campaign aide who pleaded guilty as part of the investigation into Russian meddling in the 2016 presidential election.

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Source : © European Union, 2020 - EP