Academia and industry in the age of artificial intelligence: a narrative review

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Abstract
Recent advances in artificial intelligence (AI) have fuelled a boom phase and promise to revolutionise many domains, including the traditional activities of academic medical centres (AMCs): biomedical research, education and healthcare services. Progress in AI is driven by three factors: (1) faster and cheaper processors (compute), (2) increasingly efficient training algorithms and neural network architectures and (3) an increase in electronically readable data. Outside of healthcare, industry investment dwarfs AI investment by governments. The largest computer clusters and datasets are now managed by industry. Similarly, most PhDs specialising in AI research are employed by industry. Therefore, it is not surprising that most of the largest and best-performing models are now developed by industry. Despite the industry lead in the general domain, we argue that AMCs have a key advantage in healthcare; they steward high-value biomedical data, a key driver of AI progress. Data are a strategic asset for AMCs. However, to capitalise on that asset, AMCs must invest in data ecosystems that improve data quality and utility, including the processes that produce data as well as data management. AMCs must also implement policies and procedures that lower the barriers to data use and reuse. With burgeoning interest in AI, AMCs should take on the role of careful, yet efficient, stewards of healthcare data to maximise its potential, while acting in the public’s best interests.
Funding Information
  • Cancer Prevention and Research Institute of Texas (RP250630)
  • The National Patient-Centered Clinical Research Network - Phase 4 (CDRN-1306-04631)
  • National Center for Advancing Translational Sciences (Reynolds and Reynolds Professorship)
  • National Center for Advancing Translational Sciences (UM1TR004539)
  • National Center for Advancing Translational Sciences (UM1TR004906)
  • Reynolds and Reynolds Professorship (N/A)

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