The pace and scale of medical and scientific innovation is transforming the biopharma industry. The need for better patient engagement and experience is spurring new business models. Data generated, captured, analysed and used in real time by innovative medical devices is biopharma's new currency. A key differentiator for companies is the extent to which they are able to generate insights and evidence from multiple data sources. Consequently, digital transformation is a strategic imperative. This report outlines how artificial intelligence-enabled technologies will impact the biopharma value chain and accelerate biopharma's digital transformation. Although there is a high level of innovation in the industry, biopharma companies are facing a complex and challenging environment due to increased competition and R&D cycle times, shorter time in market, expiring patents, declining peak sales, pressure around reimbursement and mounting regulatory scrutiny. As we have shown in our series of reports on'Measuring the return from pharmaceutical innovation', these factors are contributing to an alarming decline in the projected return on investment that large biopharma companies might expect to achieve from their late-stage pipelines, threatening their long-term futures.1 Digital transformation could provide a lifeline to biopharma research and development (R&D) and help reverse this trend. Digital transformation will also impact beyond R&D, as companies look to improve their operational performance, productivity, efficiency and cost-effectiveness across the entire biopharma value chain (see figure 1). Digital transformation will also impact business models, the development of new products and services, and how companies engage with health care professionals, patients and other customers. Ultimately, digital transformation is the next step in the evolution of biopharma companies.

A vast new research survey on Future Systems from Accenture (NYSE: ACN) sheds important light on the enormous impact that technology investment and adoption have on a company's financial performance and most notably, the mindsets and behaviors of companies that are industry leaders. The new research, titled: "Full Value. How to scale innovation and achieve full value with Future Systems," provides insights on how to scale innovation and achieve full value of technology investments, builds on Accenture's initial Future Systems report launched last year, and is based on a survey of more than 8,300 organizations across 20 industries and 22 countries. It is designed to help companies understand and close the innovation achievement gap – defined as the difference between potential and realized value from technology investments. The Future Systems research is Accenture's largest enterprise IT survey ever conducted and includes measures of both mature and emerging technologies such as artificial intelligence (AI), blockchain, and extended reality.

Tokyo-based AI Medical Service Inc., which is developing endoscopic software powered by artificial intelligence, announced today that it has raised $42.9 million in a Series B round. Japan's Globis Capital Partners, World Innovation Lab (WiL) out of Palo Alto and Sony Innovation Fund by IGV (Innovation Growth Ventures), and others participated in the financing. Combined with the company's last raise of $9 million in August 2018, AI Medical Service has now brought in about $57 million in venture funding since its inception in September 2017. In its own words, the company "develops AI technology that brings together the wisdom of Japanese endoscopic specialists and supports endoscopic examinations of gastrointestinal organs, such as the esophagus, stomach, small intestine and large intestine." Its goal is to more quickly and efficiently diagnose gastric cancer.

Deal making landscape in the artificial intelligence (AI) tech space during 2014–2018 was dominated by global tech giants, according to GlobalData. Of the top five acquirers, four were based out of the US, with Ireland-based Accenture being the only exception in the list. The four US-based companies–Facebook, Microsoft, Apple and Splunk–collectively accounted for 30 acquisitions in the AI tech space during 2014–2018, whereas Accenture acquired six companies in this area during the period. Aurojyoti Bose, Financial Deals Analyst at GlobalData, said, "Technology companies have been the dominant deal makers in the AI space. However, with AI making inroads into diverse sectors, the buyer universe in expanding and the space is also attracting investments from non-technology companies."

Alteryx, a publicly traded analytics company, announced this morning that it has acquired Feature Labs, a machine learning startup that launched out of MIT in 2018. The company did not reveal the terms of the deal. Co-founder and CEO Max Kanter told TechCrunch at the time of the launch the company had been based on research at MIT that looked at how to automate the creation of machine learning algorithms. "Feature Labs is unique because we automate feature engineering, which is the process of using domain knowledge to extract new variables from raw data that make machine learning algorithms work," Kanter told TechCrunch in 2018. It is precisely this capability that appealed to Alteryx .

Alteryx, a publicly traded analytics company, announced this morning that it has acquired Feature Labs, a machine learning startup that launched out of MIT in 2018. The company did not reveal the terms of the deal. Co-founder and CEO Max Kanter told TechCrunch at the time of the launch the company had been based on research at MIT that looked at how to automate the creation of machine learning algorithms. "Feature Labs is unique because we automate feature engineering, which is the process of using domain knowledge to extract new variables from raw data that make machine learning algorithms work," Kantor told TechCrunch in 2018. It is precisely this capability that appealed to Alteryx .

After 11 autopilot engineers left Tesla Inc (NASDAQ: TSLA) in May amid department restructuring, Tesla found itself in need of new software talent. On October 1, Tesla announced its acquisition of Deepscale, a Silicon Valley, California-based startup that will help actualize Tesla CEO Elon Musk's desire for its vehicles to become self-driving robotaxis. On LinkedIn, Deepscale's CEO Forrest Iandola announced his new role as a senior staff machine learning scientist at Tesla. CNBC reported that Tesla bought Deepscale "outright," but no financial details have surfaced. Deepscale is Tesla's sixth acquisition, including Maxwell Technologies in May 2019.

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The AIBC is an Artificial Intelligence and blockchain technology based large-scale decentralized ecosystem that allows system-wide low-cost sharing of computing and storage resources. The AIBC consists of four layers: a fundamental layer, a resource layer, an application layer, and an ecosystem layer. The AIBC implements a two-consensus scheme to enforce upper-layer economic policies and achieve fundamental layer performance and robustness: the DPoEV incentive consensus on the application and resource layers, and the DABFT distributed consensus on the fundamental layer. The DABFT uses deep learning techniques to predict and select the most suitable BFT algorithm in order to achieve the best balance of performance, robustness, and security. The DPoEV uses the knowledge map algorithm to accurately assess the economic value of digital assets.