While the use of artificial intelligence (AI) systems promises to bring significant economic and social benefits, it is also coupled with ethical, legal, and technical challenges. Business leaders thus face the question of how to best reap the benefits of automation whilst managing the associated risks. As a first step, many companies have committed themselves to various sets of ethics principles aimed at guiding the design and use of AI systems. So far so good. But how can well-intentioned ethical principles be translated into effective practice? And what challenges await companies that attempt to operationalize AI governance? In this article, we address these questions by drawing on our first-hand experience of shaping and driving the roll-out of AI governance within AstraZeneca, a biopharmaceutical company. The examples we discuss highlight challenges that any organization attempting to operationalize AI governance will have to face. These include questions concerning how to define the material scope of AI governance, how to harmonize standards across decentralized organizations, and how to measure the impact of specific AI governance initiatives. By showcasing how AstraZeneca managed these operational questions, we hope to provide project managers, CIOs, AI practitioners, and data privacy officers responsible for designing and implementing AI governance frameworks within other organizations with generalizable best practices. In essence, companies seeking to operationalize AI governance are encouraged to build on existing policies and governance structures, use pragmatic and action-oriented terminology, focus on risk management in development and procurement, and empower employees through continuous education and change management.

Accurate segmentation of glomerulus instances attains high clinical significance in the automated analysis of renal biopsies to aid in diagnosing and monitoring kidney disease. Analyzing real-world histopathology images often encompasses inter-observer variability and requires a labor-intensive process of data annotation. Therefore, conventional supervised learning approaches generally achieve sub-optimal performance when applied to external datasets. Considering these challenges, we present a semi-supervised learning approach for glomeruli segmentation based on the weak-to-strong consistency framework validated on multiple real-world datasets. Our experimental results on 3 independent datasets indicate superior performance of our approach as compared with existing supervised baseline models such as U-Net and SegFormer.

Generative models enable the translation from a source image domain where readily trained models are available to a target domain unseen during training. While Cycle Generative Adversarial Networks (GANs) are well established, the associated cycle consistency constrain relies on that an invertible mapping exists between the two domains. This is, however, not the case for the translation between images stained with chromogenic monoplex and duplex immunohistochemistry (IHC) assays. Focusing on the translation from the latter to the first, we propose - through the introduction of a novel training design, an alternative constrain leveraging a set of immunofluorescence (IF) images as an auxiliary unpaired image domain. Quantitative and qualitative results on a downstream segmentation task show the benefit of the proposed method in comparison to baseline approaches.

The creation of in-silico datasets can expand the utility of existing annotations to new domains with different staining patterns in computational pathology. As such, it has the potential to significantly lower the cost associated with building large and pixel precise datasets needed to train supervised deep learning models. We propose a novel approach for the generation of in-silico immunohistochemistry (IHC) images by disentangling morphology specific IHC stains into separate image channels in immunofluorescence (IF) images. The proposed approach qualitatively and quantitatively outperforms baseline methods as proven by training nucleus segmentation models on the created in-silico datasets.

AstraZeneca's Dave Fredrickson discusses how the COVID-19 pandemic helped to bolster early cancer diagnosis from lung scans. AstraZeneca is pushing forward with implementing artificial intelligence (AI) in early cancer diagnosis and drug treatment plans with the hope of significantly reducing mortality rates over the next two decades. David Fredrickson, the Executive Vice-President of the company's Oncology Business Unit, recently expressed his hope for the future of oncological work at the Milken Institute Global Conference, detailing what he expects cancer outlooks might look like in the year 2040. Speaking with Fox News Digital, Fredrickson said he expects blood-based screening to allow healthcare providers to identify cancer as early as possible and with the greatest potential for finding a cure. The rapid acceleration of AI technology could also help to pair a medicine or a combination of medicines with a specific signature that a patient has for why their cancer is growing inside them.

The mics were live and tape was rolling in the studio where the Miles Davis Quintet was recording dozens of tunes in 1956 for Prestige Records. When an engineer asked for the next song's title, Davis shot back, "I'll play it, and tell you what it is later." Like the prolific jazz trumpeter and composer, researchers have been generating AI models at a feverish pace, exploring new architectures and use cases. Focused on plowing new ground, they sometimes leave to others the job of categorizing their work. A team of more than a hundred Stanford researchers collaborated to do just that in a 214-page paper released in the summer of 2021.

Artificial intelligence that could reduce the cost and speed-up the discovery of new medicines has been developed as part of a collaboration between researchers at the University of Sheffield and AstraZeneca. The new technology, developed by Professor Haiping Lu and his Ph.D. student Peizhen Bai from Sheffield's Department of Computer Science, with Dr. Filip Miljković and Dr. Bino John from AstraZeneca, is described in a new study published in Nature Machine Intelligence. The study demonstrates that the AI, called DrugBAN, can predict whether a candidate drug will interact with its intended target protein molecules inside the human body. AI that can predict whether drugs will reach their intended targets already exists, but the technology developed by the researchers at Sheffield and AstraZeneca can do this with greater accuracy and also provide useful insights to help scientists understand how drugs engage with their protein partners at a molecular level, according to the paper published on February 2, 2023. AI has the potential to inform whether a drug will successfully engage an intended cancer-related protein, or whether a candidate drug will bind to unintended targets in the body and lead to undesirable side-effects for patients.

Drug combination therapy is a well-established strategy for disease treatment with better effectiveness and less safety degradation. However, identifying novel drug combinations through wet-lab experiments is resource intensive due to the vast combinatorial search space. Recently, computational approaches, specifically deep learning models have emerged as an efficient way to discover synergistic combinations. While previous methods reported fair performance, their models usually do not take advantage of multi-modal data and they are unable to handle new drugs or cell lines. In this study, we collected data from various datasets covering various drug-related aspects. Then, we take advantage of large-scale pre-training models to generate informative representations and features for drugs, proteins, and diseases. Based on that, a message-passing graph is built on top to propagate information together with graph structure learning flexibility. This is first introduced in the biological networks and enables us to generate pseudo-relations in the graph. Our framework achieves state-of-the-art results in comparison with other deep learning-based methods on synergistic prediction benchmark datasets. We are also capable of inferencing new drug combination data in a test on an independent set released by AstraZeneca, where 10% of improvement over previous methods is observed. In addition, we're robust against unseen drugs and surpass almost 15% AU ROC compared to the second-best model. We believe our framework contributes to both the future wet-lab discovery of novel drugs and the building of promising guidance for precise combination medicine.

Tempus, a leader in artificial intelligence and precision medicine, announced a prospective study, in collaboration with AstraZeneca, that aims to identify biomarkers of response in patients with small cell lung cancer (SCLC). The study, titled Sculptor, is co-sponsored by Tempus and AstraZeneca's Personalize SCLC Initiative and is currently open for enrollment. "This type of early-stage, prospective study is only possible when combining Tempus' comprehensive sequencing capabilities, multimodal database, and just-in-time clinical trial network." In the United States, lung cancer is the second most common cancer, and approximately 13% of people diagnosed with lung cancer have SCLC, according to the American Cancer Society. SCLC is an aggressive disease characterized by rapid growth, early metastasis, and acquired therapeutic resistance in which there is a high unmet need for therapeutic targets.

Make a more meaningful impact to patients' lives around the globe Here you'll have the opportunity to make a meaningful difference to patients' lives. With science at its heart, this is the place where breakthroughs born in the lab become transformative medicines – for the world's most complex diseases. With our ground-breaking pipeline, the outlook is forward-thinking. Be proud to be part of a place that has achieved so much, yet is still moving forward. There's no better time to join our global, growing enterprise as we lead the way for healthcare and society The Center for Artificial Intelligence (CAI) is a lab focused on applying machine learning research to the toughest challenges at AstraZeneca.