[{"innerBlocks":[],"name":"core\/freeform","postId":181,"blockType":{"name":"core\/freeform","keywords":[],"attributes":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"providesContext":[],"usesContext":[],"selectors":[],"supports":{"className":false,"customClassName":false,"reusable":false},"styles":[],"variations":[],"apiVersion":3,"title":"Classic","description":"Use the classic WordPress editor.","category":"text"},"originalContent":"Artificial intelligence has the potential to transform and disrupt healthcare. However, the industry must beware of unintended consequences and not give in to marketing hype and profit motives.That\u2019s the contention of a new National Academy of Medicine report<\/a>, the authors of which describe the document as a \u201csober and balanced\u201d assessment of the accomplishments, possibilities and pitfalls of AI in healthcare.

\u201cWhile there have been a number of promising examples of AI applications in healthcare, it is imperative to proceed with caution or risk the potential of user disillusionment, another AI winter, or further exacerbation of existing health- and technology-driven disparities,\u201d warn the study\u2019s authors.\u201cThough there is much upside in the potential for the use of AI in medicine, like all technologies, implementation does not come without certain risks,\u201d they add.NAM study co-author Eneida Mendonca, MD, vice president for research development at the Regenstrief Institute, contends that AI has the potential to create unintended consequences and, as a result, must be subject to regulation and be ethically implemented. \u201cA regulatory framework would be better established proactively, rather than in response to specific issues,\u201d says Mendonca. \u201cHealth systems must take steps to ensure the technology is enhancing care for all patients. System leaders must make efforts to avoid introducing social bias into the use of AI applications, which includes demanding transparency in the data collection and algorithm evaluation process.\u201dShe adds that \u201cgeneral IT governance structures must be adapted to manage AI and, if possible, the technology should be used in the context of a learning health system so its impact can be constantly evaluated and adjusted to maximize benefit.\u201dThe authors of the NAM study point out that AI tools are only as good as the data used to develop and maintain them, noting that there are many limitations with current data sources which are critical for delivering evidence-based healthcare and developing AI algorithms.\u201cThe implementation of electronic health records and other health information systems has provided scientists with rich longitudinal, multidimensional and detailed records about an individual\u2019s health data,\u201d the authors contend. \u201cHowever, these data are noisy and biased because they are produced for different purposes in the process of documenting care.\u201dAs a result, the NAM study warns that bad data will only result in bad models. \u201cThere is a tendency to hype AI as something magical that can learn no matter what the inputs are,\u201d observe the authors. \u201cIn practice, the choice of data always trumps the choice of the specific mathematical formulation of the model.\u201dNonetheless, the expressed hope of the study\u2019s authors is that AI will be the \u201cpayback\u201d for the healthcare industry\u2019s\u2014and federal government\u2019s\u2014massive investment in widely adopted EHRs.Still, Regenstrief\u2019s Mendonca advises that the industry must focus on clinical safety and carefully monitor uses and outcomes as AI is integrated within EHRs.\u201cAs we wrote in the National Academy report, \u2018Virtually none of the more than 320,000 health apps currently available, and which have been downloaded nearly 4 billion times, has actually been shown to improve health,\u2019\u201d says Mendonca.\u201cThe wisest guidance for AI is to start with real problems in healthcare, explore the best solutions by engaging relevant stakeholders, frontline users, patients and their families\u2014including AI and non-AI options\u2014and implement and scale the ones that meet our Quintuple Aim: better health, improved care experience, clinician well-being, lower cost and health equity throughout,\u201d conclude the study\u2019s authors.","saveContent":"Artificial intelligence has the potential to transform and disrupt healthcare. However, the industry must beware of unintended consequences and not give in to marketing hype and profit motives.That\u2019s the contention of a new National Academy of Medicine report<\/a>, the authors of which describe the document as a \u201csober and balanced\u201d assessment of the accomplishments, possibilities and pitfalls of AI in healthcare.

\u201cWhile there have been a number of promising examples of AI applications in healthcare, it is imperative to proceed with caution or risk the potential of user disillusionment, another AI winter, or further exacerbation of existing health- and technology-driven disparities,\u201d warn the study\u2019s authors.\u201cThough there is much upside in the potential for the use of AI in medicine, like all technologies, implementation does not come without certain risks,\u201d they add.NAM study co-author Eneida Mendonca, MD, vice president for research development at the Regenstrief Institute, contends that AI has the potential to create unintended consequences and, as a result, must be subject to regulation and be ethically implemented. \u201cA regulatory framework would be better established proactively, rather than in response to specific issues,\u201d says Mendonca. \u201cHealth systems must take steps to ensure the technology is enhancing care for all patients. System leaders must make efforts to avoid introducing social bias into the use of AI applications, which includes demanding transparency in the data collection and algorithm evaluation process.\u201dShe adds that \u201cgeneral IT governance structures must be adapted to manage AI and, if possible, the technology should be used in the context of a learning health system so its impact can be constantly evaluated and adjusted to maximize benefit.\u201dThe authors of the NAM study point out that AI tools are only as good as the data used to develop and maintain them, noting that there are many limitations with current data sources which are critical for delivering evidence-based healthcare and developing AI algorithms.\u201cThe implementation of electronic health records and other health information systems has provided scientists with rich longitudinal, multidimensional and detailed records about an individual\u2019s health data,\u201d the authors contend. \u201cHowever, these data are noisy and biased because they are produced for different purposes in the process of documenting care.\u201dAs a result, the NAM study warns that bad data will only result in bad models. \u201cThere is a tendency to hype AI as something magical that can learn no matter what the inputs are,\u201d observe the authors. \u201cIn practice, the choice of data always trumps the choice of the specific mathematical formulation of the model.\u201dNonetheless, the expressed hope of the study\u2019s authors is that AI will be the \u201cpayback\u201d for the healthcare industry\u2019s\u2014and federal government\u2019s\u2014massive investment in widely adopted EHRs.Still, Regenstrief\u2019s Mendonca advises that the industry must focus on clinical safety and carefully monitor uses and outcomes as AI is integrated within EHRs.\u201cAs we wrote in the National Academy report, \u2018Virtually none of the more than 320,000 health apps currently available, and which have been downloaded nearly 4 billion times, has actually been shown to improve health,\u2019\u201d says Mendonca.\u201cThe wisest guidance for AI is to start with real problems in healthcare, explore the best solutions by engaging relevant stakeholders, frontline users, patients and their families\u2014including AI and non-AI options\u2014and implement and scale the ones that meet our Quintuple Aim: better health, improved care experience, clinician well-being, lower cost and health equity throughout,\u201d conclude the study\u2019s authors.","order":0,"get_parent":{},"attributes":[],"attributesType":{"content":{"type":"string","source":"raw"},"lock":{"type":"object"}},"dynamicContent":""}]

NAM: Healthcare must proceed with caution in adopting AI

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