Advertisement

Fulfilling the Promise of Artificial Intelligence in the Health Sector: Let’s Get Real

Published:January 13, 2022DOI:https://doi.org/10.1016/j.jval.2021.11.1369

      Highlights

      • Artificial intelligence (AI) has the potential to have profound impact on the healthcare sector in ways ranging from clinical decision making to public health, biomedical research, and system governance and administration.
      • Routine application of AI in the healthcare sector is currently nascent, with most uses still in the experimental or research phase. Moreover, developing and implementing AI tools to be used at scale are beset with risks to safety, efficiency, and equity.
      • Specific policy, governance, and regulatory frameworks are needed to manage these risks and ensure that AI can contribute to better healthcare outcomes.

      Abstract

      Objectives

      This study aimed to showcase the potential and key concerns and risks of artificial intelligence (AI) in the health sector, illustrating its application with current examples, and to provide policy guidance for the development, assessment, and adoption of AI technologies to advance policy objectives.

      Methods

      Nonsystematic scan and analysis of peer-reviewed and gray literature on AI in the health sector, focusing on key insights for policy and governance.

      Results

      The application of AI in the health sector is currently in the early stages. Most applications have not been scaled beyond the research setting. The use in real-world clinical settings is especially nascent, with more evidence in public health, biomedical research, and “back office” administration. Deploying AI in the health sector carries risks and hazards that must be managed proactively by policy makers. For AI to produce positive health and policy outcomes, 5 key areas for policy are proposed, including health data governance, operationalizing AI principles, flexible regulation, skills among health workers and patients, and strategic public investment.

      Conclusions

      AI is not a panacea, but a tool to address specific problems. Its successful development and adoption require data governance that ensures high-quality data are available and secure; relevant actors can access technical infrastructure and resources; regulatory frameworks promote trustworthy AI products; and health workers and patients have the information and skills to use AI products and services safely, effectively, and efficiently. All of this requires considerable investment and international collaboration.

      Keywords

      To read this article in full you will need to make a payment

      Purchase one-time access:

      Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online access
      One-time access price info
      • For academic or personal research use, select 'Academic and Personal'
      • For corporate R&D use, select 'Corporate R&D Professionals'

      Subscribe:

      Subscribe to Value in Health
      Already a print subscriber? Claim online access
      Already an online subscriber? Sign in
      Institutional Access: Sign in to ScienceDirect

      References

      1. Tackling wasteful spending on health. OECD Publishing.
      2. Artificial intelligence in society. OECD Publishing.
      3. AI & health. OECD.
        https://oecd.ai/en/dashboards/policy-areas/PA11
        Date accessed: November 12, 2021
        • McKinney S.M.
        • Sieniek M.
        • Godbole V.
        • et al.
        International evaluation of an AI system for breast cancer screening [published correction appears in Nature. 2020;586(7829):E19].
        Nature. 2020; 577: 89-94
        • Kann B.H.
        • Thompson R.
        • Thomas Jr., C.R.
        • Dicker A.
        • Aneja S.
        Artificial intelligence in oncology: current applications and future directions.
        Oncol (Williston Park). 2019; 33: 46-53
        • Freeman K.
        • Geppert J.
        • Stinton C.
        • et al.
        Use of artificial intelligence for image analysis in breast cancer screening programmes: systematic review of test accuracy.
        BMJ. 2021; 374: n1872
        • Muehlematter U.J.
        • Daniore P.
        • Vokinger K.N.
        Approval of artificial intelligence and machine learning-based medical devices in the USA and Europe (2015-20): a comparative analysis.
        Lancet Digit Health. 2021; 3: e195-e203
        • Wu E.
        • Wu K.
        • Daneshjou R.
        • Ouyang D.
        • Ho D.E.
        • Zou J.
        How medical AI devices are evaluated: limitations and recommendations from an analysis of FDA approvals.
        Nat Med. 2021; 27: 582-584
        • European Society of Radiology (ESR)
        What the radiologist should know about artificial intelligence - an ESR white paper.
        Insights Imaging. 2019; 10: 44
        • Shadmi E.
        • Flaks-Manov N.
        • Hoshen M.
        • Goldman O.
        • Bitterman H.
        • Balicer R.D.
        Predicting 30-day readmissions with preadmission electronic health record data.
        Med Care. 2015; 53: 283-289
        • Skrede O.J.
        • De Raedt S.
        • Kleppe A.
        • et al.
        Deep learning for prediction of colorectal cancer outcome: a discovery and validation study.
        Lancet. 2020; 395: 350-360
        • Svoboda E.
        Your robot surgeon will see you now.
        Nature. 2019; 573: S110-S111
        • Combs V.
        South African clinics use artificial intelligence to expand HIV treatment. Tech Repub.
        • Lunit
        Releases its AI online to support healthcare professionals manage COVID-19. Lunit Newsroom.
        • Hwang E.J.
        • Park S.
        • Jin K.N.
        • et al.
        Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs [published correction appears in JAMA Netw Open. 2019;2(4):e193260].
        JAMA Netw Open. 2019; 2e191095
        • Li L.
        • Qin L.
        • Xu Z.
        • et al.
        Using artificial intelligence to detect COVID-19 and community-acquired pneumonia based on pulmonary CT: evaluation of the diagnostic accuracy.
        Radiology. 2020; 296: E65-E71
        • Zhang K.
        • Liu X.
        • Shen J.
        • et al.
        Clinically applicable AI system for accurate diagnosis, quantitative measurements, and prognosis of COVID-19 pneumonia using computed tomography [published correction appears in Cell. 2020;182(5):1360].
        Cell. 2020; 181: 1423-1433.e11
      4. FDA approves use of Aidoc’s AI algorithms for incidental CT findings associated with COVID-19. Imaging Technology News.
      5. Recommendations for the use of chest radiography and computed tomography (CT) for suspected COVID-19 infection. American College of Radiology.
        • Hope M.D.
        • Raptis C.A.
        • Shah A.
        • Hammer M.M.
        • Henry T.S.
        • six signatories
        A role for CT in COVID-19? What data really tell us so far.
        Lancet. 2020; 395: 1189-1190
        • Olson P.
        AI software gets mixed reviews for tackling coronavirus. WSJ.
        • Brzezicki M.A.
        • Bridger N.E.
        • Kobetić M.D.
        • et al.
        Artificial intelligence outperforms human students in conducting neurosurgical audits.
        Clin Neurol Neurosurg. 2020; 192105732
      6. Health in the 21st Century: Putting Data to Work for Stronger Health Systems. Paris: OECD Publishing.
        • Rahimian F.
        • Salimi-Khorshidi G.
        • Payberah A.H.
        • et al.
        Predicting the risk of emergency admission with machine learning: development and validation using linked electronic health records.
        PLoS Med. 2018; 15e1002695
        • Yan L.
        • Zhang H.T.
        • Goncalves J.
        • et al.
        An interpretable mortality prediction model for COVID-19 patients.
        Nat Mach Intell. 2020; 2: 283-288
        • Bowles J.
        How Canadian AI start-up BlueDot spotted coronavirus before anyone else had a clue. Diginomica.
        • Wim N.
        Artificial intelligence against COVID-19: an early review. IZA.
        • Eichler H.G.
        • Bloechl-Daum B.
        • Broich K.
        • et al.
        Data rich, information poor: can we use electronic health records to create a learning healthcare system for pharmaceuticals?.
        Clin Pharmacol Ther. 2019; 105: 912-922
        • Lee C.S.
        • Lee A.Y.
        How artificial intelligence can transform randomized controlled trials.
        Transl Vis Sci Technol. 2020; 9: 9
        • Stokes J.M.
        • Yang K.
        • Swanson K.
        • et al.
        A deep learning approach to antibiotic discovery [published correction appears in Cell. 2020;181(2):475-483].
        Cell. 2020; 180: 688-702.e13
        • Zhavoronkov A.
        • Ivanenkov Y.A.
        • Aliper A.
        • et al.
        Deep learning enables rapid identification of potent DDR1 kinase inhibitors.
        Nat Biotechnol. 2019; 37: 1038-1040
        • Schmider J.
        • Kumar K.
        • LaForest C.
        • Swankoski B.
        • Naim K.
        • Caubel P.M.
        Innovation in pharmacovigilance: use of artificial intelligence in adverse event case processing.
        Clin Pharmacol Ther. 2019; 105: 954-961
      7. The Intelligent Payer: a survival guide. Accenture.
      8. Humber River hospital and GE Healthcare building first hospital command Centre for Quality Health Care in Canada. GE Healthcare Partners.
      9. Lean Lab. European Commission.
        • Matheny M.
        • Israni S.T.
        • Ahmed M.
        • Whicher D.
        Artificial Intelligence in Health Care: the Hope, the Hype, the Promise, the Peril. National Academy of Medicine.
        • Reardon S.
        Rise of robot radiologists.
        Nature. 2019; 576: S54-S58
        • Strickland E.
        • Watson H.I.B.M.
        • Overpromised and Underdelivered on AI Health Care, IEEE, Spectrum
        IEEE spectrum.
        • Colombo F.
        • Oderkirk J.
        • Slawomirski L.
        Health information systems, electronic medical records, and big data in global healthcare: progress and challenges in OECD countries.
        in: Haring R. Kickbusch I. Ganten D. Moeti M. Handbook of Global Health. Springer, Cham, Switzerland2020: 1-31
        • Obermeyer Z.
        • Powers B.
        • Vogeli C.
        • Mullainathan S.
        Dissecting racial bias in an algorithm used to manage the health of populations.
        Science. 2019; 366: 447-453
        • Crawford K.
        • Dobbe R.
        • Dryer T.
        • et al.
        AI Now 2019 Report.
        AI Now Institute, New York, NJ2019
        • Adamson A.S.
        • Welch H.G.
        Machine learning and the cancer-diagnosis problem - no gold standard.
        N Engl J Med. 2019; 381: 2285-2287
        • Lehne M.
        • Sass J.
        • Essenwanger A.
        • Schepers J.
        • Thun S.
        Why digital medicine depends on interoperability.
        NPJ Digit Med. 2019; 2: 79
        • Oderkirk J.
        Readiness of Electronic Health Record Systems to Contribute to National Health Information and Research. OECD Publishing.
        • Willyard C.
        Can AI fix medical records?.
        Nature. 2019; 576: S59-S62
      10. Artificial intelligence for health. ITU Focus Group.
        • Dyrbye L.N.
        • Shanafelt T.D.
        • Sinsky C.A.
        • et al.
        Burnout among health care professionals: a call to explore and address this underrecognized threat to safe, high-quality care. National Academy of Medicine.
        • Bathaee Y.
        The artificial intelligence black box and the failure of intent and causation.
        Harv J Law & Tech. 2018; 31: 889
      11. OECD recommendation of the Council on Health Data Governance. OECD.
        • Oderkirk J.
        Survey results: national health data infrastructure and governance. OECD Publishing.
      12. OECD employment outlook 2019: the future of work. OECD Publishing.
      13. OECD science, Technology and Innovation Outlook 2021. OECD. OECD Publishing.
      14. European High Performance Computer Joint Undertaking. EuroHPC JU.
        https://eurohpc-ju.europa.eu/
        Date accessed: August 1, 2020
      15. OECD Council recommendation on artificial intelligence. OECD.
      16. Liability for artificial intelligence and other emerging digital technologies: report from the expert group on liability and new technologies – new technologies formation. Publications Office of the European Union.
      17. Health data as a global public good. WHO.
        • Jobin A.
        • Ienca M.
        • Vayena E.
        The global landscape of AI ethics guidelines.
        Nat Mach Intell. 2019; 1: 389-399
        • Socha-Dietrich K.
        Empowering the health workforce to make the most of the digital revolution. OECD Publishing.
        • Wilson H.
        • Daugherty P.
        • Bianzino N.
        The jobs that artificial intelligence will create. MIT Sloan Management Review.
      18. Private Equity Investment in Artificial Intelligence: OECD Going Digital Policy Note. OECD. OECD Publishing.
        https://www.oecd.org/digital/
        Date accessed: November 12, 2021
        • Wolff J.
        • Pauling J.
        • Keck A.
        • Baumbach J.
        The economic impact of artificial intelligence in health care: systematic review.
        J Med Internet Res. 2020; 22e16866
      19. Voets MM, Veltman J, Slump CH, et al. Systematic Review of health economic evaluations focused on Artificial Intelligence in healthcare: the tortoise and the cheetah [published online December 16, 2021]. Value Health. https://doi.org/10.1016/j.jval.2021.11.1362.