Background: Convolutional neural network (CNN)-based melanoma classifiers face several challenges that limit their usefulness in clinical practice. Objective: To investigate the impact of multiple real-world dermoscopic views of a single lesion of interest on a CNN-based melanoma classifier. Methods: This study evaluated 656 suspected melanoma lesions. Classifier performance was measured using area under the receiver operating characteristic curve (AUROC), expected calibration error (ECE) and maximum confidence change (MCC) for (I) a single-view scenario, (II) a multiview scenario using multiple artificially modified images per lesion and (III) a multiview scenario with multiple real-world images per lesion. Results: The multiview approach with real-world images significantly increased the AUROC from 0.905 (95% CI, 0.879-0.929) in the single-view approach to 0.930 (95% CI, 0.909-0.951). ECE and MCC also improved significantly from 0.131 (95% CI, 0.105-0.159) to 0.072 (95% CI: 0.052-0.093) and from 0.149 (95% CI, 0.125-0.171) to 0.115 (95% CI: 0.099-0.131), respectively. Comparing multiview real-world to artificially modified images showed comparable diagnostic accuracy and uncertainty estimation, but significantly worse robustness for the latter. Conclusion: Using multiple real-world images is an inexpensive method to positively impact the performance of a CNN-based melanoma classifier.

While a few firms--the digital giants--are winning big from digital technology, many large firms are investing heavily in digital, by recruiting IT specialists and spending on digital technology--including artificial intelligence, cloud computing, machine learning, and algorithmic decision making, yet getting disappointing returns. As noted in a previous article, 'How To Understand Your Digital Transformation" firms must realize is that digital transformation is only partly to do with technology. The chief obstacle is the fundamental change in the way the firm is managed--from industrial-era management to digital age management. Thus, over the last two decades, it has become increasingly apparent that the most successful firms at digital transformation are being run very differently from industrial-era. They have a different set of principles, including a goal of co-creating value for customers, a business model that generates profits as a result; team-based and network structures. These principles are supported by processes that are quite different from those of industrial-era management.

Anumana, a joint venture between EHR data company nference and the Mayo Clinic, announced last week it had entered a strategic partnership with pharma giant Novartis to develop artificial intelligence tools to detect cardiovascular diseases. The collaboration will focus on deploying AI algorithms that analyze ECGs to find left ventricular dysfunction, which can lead to heart failure, and atherosclerotic cardiovascular disease, which can cause heart attack and stroke. The companies are pitching the partnership as a way to detect potentially deadly heart conditions and intervene before serious complications occur. "Many heart diseases develop for years before signs and symptoms appear, but the first event may be life threatening," Dr. Paul Friedman, chair of Anumana's Mayo Clinic board of advisors, said in a statement. "AI enables us to uncover hidden signals our bodies transmit to detect otherwise occult heart diseases, potentially years before symptoms appear. This collaboration has the potential to transform the use of a ubiquitous inexpensive test, the ECG, with the aim of democratizing disease detection and helping medical care teams to proactively manage heart disease ahead of time, and prevent some clinical events from ever happening."

Many companies struggle to reap the benefits of investments in digital transformation, while others see enormous gains. What do successful companies do differently? This article describes the five stages of digital transformation, from the traditional stage, where digital and technology are the province of the IT department, through to the platform stage, where a comprehensive software foundation enables the rapid deployment of AI-based applications. The ideal is the native stage, whose hallmarks are an operating architecture designed to deploy AI at scale across a huge, distributed spectrum of applications; a core of experts; broadly accessible, easy-to-use tools; and investment in training and capability-building among large groups of businesspeople. Over the past decade, Novartis has invested heavily in digital transformation.

Novartis's Ciara O'Connell discusses the role of data in pharma and how the company is tapping into Ireland's'significant tech ecosystem'. Ciara O'Connell is head of the Novartis Global Service Centre (NGSC) in Ireland. This was set up in 2013 and is now one of five sites worldwide delivering a variety of services to the multinational pharma company in areas such as AI and data. O'Connell is a native of Cork, where Novartis has a strong presence, and studied commerce at University College Cork. After nearly a decade with the company, she now leads a team of more than 1,000 at its growing base in Dublin.

As AI becomes more deeply embedded in our everyday lives, it is incumbent upon all of us to be thoughtful and responsible in how we apply it to benefit people and society. A principled approach to responsible AI will be essential for every organization as this technology matures. As technical and product leaders look to adopt responsible AI practices and tools, there are several challenges including identifying the approach that is best suited to their organizations, products and market. Today, at our Azure event, Put Responsible AI into Practice, we are pleased to share new resources and tools to support customers on this journey, including guidelines for product leaders co-developed by Microsoft and Boston Consulting Group (BCG). While these guidelines are separate from Microsoft's own Responsible AI principles and processes, they are intended to provide guidance for responsible AI development through the product lifecycle.

Artificial intelligence is nothing strange now, as it is being used by almost all businesses in different industries. The rapid digital transformation and technology adoption in recent years has led many companies to extensively invest in AI to drive growth. Novartis International AG is a leading global healthcare company, based out of Switzerland, that has been efficiently capitalizing on its AI capabilities to develop medical innovations. Novartis incorporates digital and disruptive technologies to create transformative treatment and drug discoveries. Everybody is trying to adopt AI, but how many focus on an ethical approach?

Every so often, a technology with the potential to disrupt clinical practice emerges and the medical literature explodes with new studies. These seismic events present a challenge to the peer review process because many reviewers and editorial board members may be unfamiliar with how to evaluate them. Complicating matters, early adopters and thought leaders may not use consistent terminology, may not report results similarly, or may not appreciate fully the potential for inaccurate conclusions based on interpretation errors.

The COVID-19 pandemic is the greatest global healthcare crisis of our generation, presenting enormous challenges to medical research, including clinical trials. Advances in machine learning are providing an opportunity to adapt clinical trials and lay the groundwork for smarter, faster and more flexible clinical trials in the future. In an article published in Statistics in Biopharmaceutical Research, an international collaboration of data scientists and pharmaceutical industry experts – led by the Director of the Cambridge Centre for AI in Medicine, Professor Mihaela van der Schaar of the University of Cambridge – describe the impact that COVID-19 is having on clinical trials, and reveal how the latest machine learning (ML) approaches can help to overcome challenges that the pandemic presents. The paper covers three areas of clinical trials in which ML can make contributions: in trials for repurposing drugs to treat COVID-19, trials for new drugs to treat COVID-19, and ongoing clinical trials for drugs unrelated to COVID-19. The team, which includes scientists from pharmaceutical companies such as Novartis, notes that'the pandemic provides an opportunity to apply novel approaches that can be used in this challenging situation.'

The COVID-19 pandemic is the greatest global healthcare crisis of our generation, presenting enormous challenges to medical research, including clinical trials. Advances in machine learning are providing an opportunity to adapt clinical trials and lay the groundwork for smarter, faster and more flexible clinical trials in the future. In an article published in Statistics in Biopharmaceutical Research, an international collaboration of data scientists and pharmaceutical industry experts--led by the Director of the Cambridge Center for AI in Medicine, Professor Mihaela van der Schaar of the University of Cambridge--describes the impact that COVID-19 is having on clinical trials, and reveals how the latest machine learning (ML) approaches can help to overcome challenges that the pandemic presents. The paper covers three areas of clinical trials in which ML can make contributions: in trials for repurposing drugs to treat COVID-19, trials for new drugs to treat COVID-19, and ongoing clinical trials for drugs unrelated to COVID-19. The team, which includes scientists from pharmaceutical companies such as Novartis, notes that "the pandemic provides an opportunity to apply novel approaches that can be used in this challenging situation."