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  "documentTitle": "Fueling the AI transformation: Four key actions powering widespread value from AI, right now.",
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      "text": "Interestingly, in looking at both sectors, industry respondents who still find themselves in the Starters category also reported a meaningfully increased focus on use cases that are higher value, but significantly more difficult (such as research and development and computer-assisted diagnostics), relative to more AI-mature players.",
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      "text": "Within life sciences, the presence of more advanced and industry-specific use cases, such as smarter drug manufacturing, shows no signs of slowing down. AI's role within the biopharmaceutical R&D value chain manifests in the preeminent role of smart drug discovery applications.",
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      "text": "This could be slowing down progress, as they may not be reaping the rewards or faster returns from use cases that may be more low-hanging fruit.",
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      "text": "Data from the survey indicates that top use cases across life sciences and health care as an industry still tend to focus primarily on automating repetitive tasks and standard cross-industry business processes. This is seen across many industries, where the frequency of back-office functions rising to the top 10 is high. However, a deeper look at the individual subsectors reveals some interesting differences among the top use cases.",
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      "text": "That more AI-advanced organizations rely on the technology for more industry-agnostic tactical cases, relatively speaking, suggests that the real payoff to AI adoption is currently in cross-industry use cases. It also may speak to some of the hurdles that industry-specific AI use case adoption faces within LSHC.",
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      "text": "For its part, the leading industry-specific health care AI use cases focus on outcomes and monitoring in such potentially transformational areas as AI-assisted diagnoses including predictive diagnoses, patient engagement, insurance fraud detection and smarter hospitals. Recent years have also witnessed the burgeoning role of trustworthy AI in ensuring more equitable health care delivery outcomes. Key in this regard is in guarding against the kind of model bias that could result in unfair and inequitable health care delivery and insurance coverage, among other areas. Another recent development points to a health care emergency. The pandemic highlighted how AI can help hospitals and other providers respond to a health care emergency. As a result of the pandemic, a number of leading medical schools and hospitals have recently established AI-focused departments to advance how AI can better predict and optimize the delivery of health care services under such extreme global conditions in the future.",
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      "text": "This also speaks to how critical the decision-making process can be early in the AI maturity journey. It's important to develop an AI strategy that can achieve outcomes in a shorter time frame, while also identifying use cases that may require a steeper learning curve and a more significant time-to-value realization.10",
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      "text": "Two years after the world was introduced to COVID-19, the recognition of AI's importance to life sciences and health care companies has become increasingly clear. There is a stronger urgency, especially among biopharmaceutical executives, to tackle the risks associated with the technology in order to innovate and gain an edge over the competition.",
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      "kind": "paragraph",
      "text": "For more exploration into the near- and longer-term applications of AI across this industry, see Deloitte's Life Sciences and Health Care AI Dossier.",
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