Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

AI in health and medicine

Abstract

Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human–AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI’s potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Fig. 1: Overview of the progress, challenges and opportunities for AI in health.
Fig. 2
Fig. 3
Fig. 4: Evolving procedures for data sharing.

Similar content being viewed by others

References

  1. Gulshan, V. et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. J. Am. Med. Assoc. 316, 2402–2410 (2016).

    Article  Google Scholar 

  2. Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115–118 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Rajpurkar, P. et al. Deep learning for chest radiograph diagnosis: a retrospective comparison of the CheXNeXt algorithm to practicing radiologists. PLoS Med. 15, e1002686 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  4. Hannun, A. Y. et al. Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nat. Med. 25, 65–69 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Wiens, J. et al. Do no harm: a roadmap for responsible machine learning for health care. Nat. Med. 25, 1337–1340 (2019).

    Article  CAS  PubMed  Google Scholar 

  6. Kanagasingam, Y. et al. Evaluation of artificial intelligence-based grading of diabetic retinopathy in primary care. JAMA Netw. Open 1, e182665 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Beede, E. et al. A human-centered evaluation of a deep learning system deployed in clinics for the detection of diabetic retinopathy. in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems 1–12 (Association for Computing Machinery, 2020); https://dl.acm.org/doi/abs/10.1145/3313831.3376718

  8. Kiani, A. et al. Impact of a deep learning assistant on the histopathologic classification of liver cancer. NPJ Digit. Med. 3, 23 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  9. Lin, H. et al. Diagnostic efficacy and therapeutic decision-making capacity of an artificial intelligence platform for childhood cataracts in eye clinics: a multicentre randomized controlled trial. EClinicalMedicine 9, 52–59 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Gong, D. et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol. Hepatol. 5, 352–361 (2020).

    Article  PubMed  Google Scholar 

  11. Wang, P. et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol. Hepatol. 5, 343–351 (2020).

    Article  PubMed  Google Scholar 

  12. Hollon, T. C. et al. Near real-time intraoperative brain tumor diagnosis using stimulated Raman histology and deep neural networks. Nat. Med. 26, 52–58 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Phillips, M. et al. Assessment of accuracy of an artificial intelligence algorithm to detect melanoma in images of skin lesions. JAMA Netw. Open 2, e1913436 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Nimri, R. et al. Insulin dose optimization using an automated artificial intelligence-based decision support system in youths with type 1 diabetes. Nat. Med. 26, 1380–1384 (2020).

    Article  CAS  PubMed  Google Scholar 

  15. Wijnberge, M. et al. Effect of a machine learning-derived early warning system for intraoperative hypotension vs. standard care on depth and duration of intraoperative hypotension during elective noncardiac surgery. J. Am. Med. Assoc. 323, 1052–1060 (2020).

    Article  Google Scholar 

  16. Wismüller, A. & Stockmaster, L. A prospective randomized clinical trial for measuring radiology study reporting time on Artificial Intelligence-based detection of intracranial hemorrhage in emergent care head CT. in Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging vol. 11317, 113170M (International Society for Optics and Photonics, 2020).

  17. Liu, X. et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Br. Med. J. 370, m3164 (2020).

    Article  Google Scholar 

  18. Rivera, S. C. et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Nat. Med. 26, 1351–1363 (2020).

    Article  Google Scholar 

  19. Centers for Medicare & Medicaid Services. Medicare Program; Hospital Inpatient Prospective Payment Systems for Acute Care Hospitals and the Long-Term Care Hospital Prospective Payment System and Final Policy Changes and Fiscal Year 2021 Rates; Quality Reporting and Medicare and Medicaid Promoting Interoperability Programs Requirements for Eligible Hospitals and Critical Access Hospitals. Fed. Regist. 85, 58432–59107 (2020).

  20. Benjamens, S., Dhunnoo, P. & Meskó, B. The state of artificial intelligence-based FDA-approved medical devices and algorithms: an online database. NPJ Digit. Med. 3, 118 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  21. Wu, N. et al. Deep neural networks improve radiologists’ performance in breast cancer screening. IEEE Trans. Med. Imaging 39, 1184–1194 (2020).

    Article  PubMed  Google Scholar 

  22. McKinney, S. M. et al. International evaluation of an AI system for breast cancer screening. Nature 577, 89–94 (2020).

    Article  CAS  PubMed  Google Scholar 

  23. Ghorbani, A. et al. Deep learning interpretation of echocardiograms. NPJ Digit. Med. 3, 10 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Ouyang, D. et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature 580, 252–256 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Ardila, D. et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat. Med. 25, 954–961 (2019).

    Article  CAS  PubMed  Google Scholar 

  26. Huynh, E. et al. Artificial intelligence in radiation oncology. Nat. Rev. Clin. Oncol. 17, 771–781 (2020).

    Article  PubMed  Google Scholar 

  27. Huang, P. et al. Prediction of lung cancer risk at follow-up screening with low-dose CT: a training and validation study of a deep learning method. Lancet Digit. Health 1, e353–e362 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Kather, J. N. et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat. Med. 25, 1054–1056 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Jackson, H. W. et al. The single-cell pathology landscape of breast cancer. Nature 578, 615–620 (2020).

    Article  CAS  PubMed  Google Scholar 

  30. Campanella, G. et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat. Med. 25, 1301–1309 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Fu, Y. et al. Pan-cancer computational histopathology reveals mutations, tumor composition and prognosis. Nat. Cancer 1, 800–810 (2020).

    Article  PubMed  Google Scholar 

  32. Courtiol, P. et al. Deep learning-based classification of mesothelioma improves prediction of patient outcome. Nat. Med. 25, 1519–1525 (2019).

    Article  CAS  PubMed  Google Scholar 

  33. Bera, K., Schalper, K. A., Rimm, D. L., Velcheti, V. & Madabhushi, A. Artificial intelligence in digital pathology: new tools for diagnosis and precision oncology. Nat. Rev. Clin. Oncol. 16, 703–715 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  34. Zhou, D. et al. Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer. Nat. Commun. 11, 2961 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Zhao, S. et al. Magnitude, risk factors, and factors associated with adenoma miss rate of tandem colonoscopy: a systematic review and meta-analysis. Gastroenterology 156, 1661–1674 (2019).

    Article  PubMed  Google Scholar 

  36. Freedman, D. et al. Detecting deficient coverage in colonoscopies. IEEE Trans. Med. Imaging 39, 3451–3462 (2020).

    Article  PubMed  Google Scholar 

  37. Liu, H. et al. Development and validation of a deep learning system to detect glaucomatous optic neuropathy using fundus photographs. JAMA Ophthalmol. 137, 1353–1360 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  38. Milea, D. et al. Artificial intelligence to detect papilledema from ocular fundus photographs. N. Engl. J. Med. 382, 1687–1695 (2020).

    Article  PubMed  Google Scholar 

  39. Wolf, R. M., Channa, R., Abramoff, M. D. & Lehmann, H. P. Cost-effectiveness of autonomous point-of-care diabetic retinopathy screening for pediatric patients with diabetes. JAMA Ophthalmol. 138, 1063–1069 (2020).

    Article  PubMed  Google Scholar 

  40. Xie, Y. et al. Artificial intelligence for teleophthalmology-based diabetic retinopathy screening in a national programme: an economic analysis modelling study. Lancet Digit. Health 2, e240–e249 (2020).

    Article  PubMed  Google Scholar 

  41. Arcadu, F. et al. Deep learning algorithm predicts diabetic retinopathy progression in individual patients. NPJ Digit. Med. 2, 92 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  42. Senior, A. W. et al. Improved protein structure prediction using potentials from deep learning. Nature 577, 706–710 (2020).

    Article  CAS  PubMed  Google Scholar 

  43. Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M. & Church, G. M. Unified rational protein engineering with sequence-based deep representation learning. Nat. Methods 16, 1315–1322 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Gainza, P. et al. Deciphering interaction fingerprints from protein molecular surfaces using geometric deep learning. Nat. Methods 17, 184–192 (2020).

    Article  CAS  PubMed  Google Scholar 

  45. Greener, J.G. et al. Deep learning extends de novo protein modelling coverage of genomes using iteratively predicted structural constraints. Nat. Commun. 10, 3977 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  46. Chabon, J. J. et al. Integrating genomic features for non-invasive early lung cancer detection. Nature 580, 245–251 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Luo, H. et al. Circulating tumor DNA methylation profiles enable early diagnosis, prognosis prediction, and screening for colorectal cancer. Sci. Transl. Med. 12, eaax7533 (2020).

    Article  CAS  PubMed  Google Scholar 

  48. Cristiano, S. et al. Genome-wide cell-free DNA fragmentation in patients with cancer. Nature 570, 385–389 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Gussow, A. B. et al. Machine-learning approach expands the repertoire of anti-CRISPR protein families. Nat. Commun. 11, 3784 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Wang, D. et al. Optimized CRISPR guide RNA design for two high-fidelity Cas9 variants by deep learning. Nat. Commun. 10, 4284 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Bhattacharyya, R. P. et al. Simultaneous detection of genotype and phenotype enables rapid and accurate antibiotic susceptibility determination. Nat. Med. 25, 1858–1864 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Stokes, J. M. et al. A deep learning approach to antibiotic discovery. Cell 181, 475–483 (2020).

    Article  CAS  PubMed  Google Scholar 

  53. Zhavoronkov, A. et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat. Biotechnol. 37, 1038–1040 (2019).

    Article  CAS  PubMed  Google Scholar 

  54. Lee, J. et al. BioBERT: a pre-trained biomedical language representation model for biomedical text mining. Bioinformatics 36, 1234–1240 (2020).

    CAS  PubMed  Google Scholar 

  55. Zhu, Y., Li, L., Lu, H., Zhou, A. & Qin, X. Extracting drug-drug interactions from texts with BioBERT and multiple entity-aware attentions. J. Biomed. Inform. 106, 103451 (2020).

    Article  PubMed  Google Scholar 

  56. Smit, A. et al. CheXbert: Combining automatic labelers and expert annotations for accurate radiology report labeling using BERT. in Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing 1500–1519 (2020).

  57. Sarker, A., Gonzalez-Hernandez, G., Ruan, Y. & Perrone, J. Machine learning and natural language processing for geolocation-centric monitoring and characterization of opioid-related social media chatter. JAMA Netw. Open 2, e1914672 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  58. Claassen, J. et al. Detection of brain activation in unresponsive patients with acute brain injury. N. Engl. J. Med. 380, 2497–2505 (2019).

    Article  PubMed  Google Scholar 

  59. Porumb, M., Stranges, S., Pescapè, A. & Pecchia, L. Precision medicine and artificial intelligence: a pilot study on deep learning for hypoglycemic events detection based on ECG. Sci. Rep. 10, 170 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Attia, Z. I. et al. An artificial intelligence-enabled ECG algorithm for the identification of patients with atrial fibrillation during sinus rhythm: a retrospective analysis of outcome prediction. Lancet 394, 861–867 (2019).

    Article  PubMed  Google Scholar 

  61. Chan, J., Raju, S., Nandakumar, R., Bly, R. & Gollakota, S. Detecting middle ear fluid using smartphones. Sci. Transl. Med. 11, eaav1102 (2019).

    Article  PubMed  Google Scholar 

  62. Willett, F. R., Avansino, D. T., Hochberg, L. R., Henderson, J. M. & Shenoy, K. V. High-performance brain-to-text communication via handwriting. Nature 593, 249–254 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Green, E. M. et al. Machine learning detection of obstructive hypertrophic cardiomyopathy using a wearable biosensor. NPJ Digit. Med. 2, 57 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  64. Thorsen-Meyer, H.-C. et al. Dynamic and explainable machine learning prediction of mortality in patients in the intensive care unit: a retrospective study of high-frequency data in electronic patient records. Lancet Digit. Health 2, e179–e191 (2020).

    Article  PubMed  Google Scholar 

  65. Porter, P. et al. A prospective multicentre study testing the diagnostic accuracy of an automated cough sound centred analytic system for the identification of common respiratory disorders in children. Respir. Res. 20, 81 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  66. Tomašev, N. et al. A clinically applicable approach to continuous prediction of future acute kidney injury. Nature 572, 116–119 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Kehl, K. L. et al. Assessment of deep natural language processing in ascertaining oncologic outcomes from radiology reports. JAMA Oncol. 5, 1421–1429 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  68. Huang, S.-C., Pareek, A., Seyyedi, S., Banerjee, I. & Lungren, M. P. Fusion of medical imaging and electronic health records using deep learning: a systematic review and implementation guidelines. NPJ Digit. Med. 3, 136 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  69. Wang, C. et al. Quantitating the epigenetic transformation contributing to cholesterol homeostasis using Gaussian process. Nat. Commun. 10, 5052 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  70. Li, Y. et al. Inferring multimodal latent topics from electronic health records. Nat. Commun. 11, 2536 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Tshitoyan, V. et al. Unsupervised word embeddings capture latent knowledge from materials science literature. Nature 571, 95–98 (2019).

    Article  CAS  PubMed  Google Scholar 

  72. Li, X. et al. Deep learning enables accurate clustering with batch effect removal in single-cell RNA-seq analysis. Nat. Commun. 11, 2338 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Amodio, M. et al. Exploring single-cell data with deep multitasking neural networks. Nat. Methods 16, 1139–1145 (2019).

    Article  CAS  PubMed  Google Scholar 

  74. Urteaga, I., McKillop, M. & Elhadad, N. Learning endometriosis phenotypes from patient-generated data. NPJ Digit. Med. 3, 88 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  75. Brbić, M. et al. MARS: discovering novel cell types across heterogeneous single-cell experiments. Nat. Methods 17, 1200–1206 (2020).

    Article  PubMed  Google Scholar 

  76. Seymour, C. W. et al. Derivation, validation, and potential treatment implications of novel clinical phenotypes for sepsis. J. Am. Med. Assoc. 321, 2003–2017 (2019).

    Article  CAS  Google Scholar 

  77. Fries, J. A. et al. Weakly supervised classification of aortic valve malformations using unlabeled cardiac MRI sequences. Nat. Commun. 10, 3111 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Jin, L. et al. Deep learning enables structured illumination microscopy with low light levels and enhanced speed. Nat. Commun. 11, 1934 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Vishnevskiy, V. et al. Deep variational network for rapid 4D flow MRI reconstruction. Nat. Mach. Intell. 2, 228–235 (2020).

    Article  Google Scholar 

  80. Masutani, E. M., Bahrami, N. & Hsiao, A. Deep learning single-frame and multiframe super-resolution for cardiac MRI. Radiology 295, 552–561 (2020).

    Article  PubMed  Google Scholar 

  81. Rana, A. et al. Use of deep learning to develop and analyze computational hematoxylin and eosin staining of prostate core biopsy images for tumor diagnosis. JAMA Netw. Open 3, e205111 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  82. Liu, X. et al. A comparison of deep learning performance against health-care professionals in detecting diseases from medical imaging: a systematic review and meta-analysis. Lancet Digit. Health 1, e271–e297 (2019).

    Article  PubMed  Google Scholar 

  83. Chen, P.-H. C. et al. An augmented reality microscope with real-time artificial intelligence integration for cancer diagnosis. Nat. Med. 25, 1453–1457 (2019).

    Article  CAS  PubMed  Google Scholar 

  84. Patel, B. N. et al. Human–machine partnership with artificial intelligence for chest radiograph diagnosis. NPJ Digit. Med. 2, 111 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  85. Sim, Y. et al. Deep convolutional neural network–based software improves radiologist detection of malignant lung nodules on chest radiographs. Radiology 294, 199–209 (2020).

    Article  PubMed  Google Scholar 

  86. Park, A. et al. Deep learning–assisted diagnosis of cerebral aneurysms using the HeadXNet model. JAMA Netw. Open 2, e195600 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  87. Steiner, D. F. et al. Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am. J. Surg. Pathol. 42, 1636–1646 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  88. Jain, A. et al. Development and assessment of an artificial intelligence-based tool for skin condition diagnosis by primary care physicians and nurse practitioners in teledermatology practices. JAMA Netw. Open 4, e217249 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  89. Seah, J. C. Y. et al. Effect of a comprehensive deep-learning model on the accuracy of chest x-ray interpretation by radiologists: a retrospective, multireader multicase study. Lancet Digit. Health 3, e496–e506 (2021).

    Article  PubMed  Google Scholar 

  90. Rajpurkar, P. et al. CheXaid: deep learning assistance for physician diagnosis of tuberculosis using chest x-rays in patients with HIV. NPJ Digit. Med. 3, 115 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  91. Kim, H.-E. et al. Changes in cancer detection and false-positive recall in mammography using artificial intelligence: a retrospective, multireader study. Lancet Digit. Health 2, e138–e148 (2020).

    Article  PubMed  Google Scholar 

  92. Tschandl, P. et al. Human–computer collaboration for skin cancer recognition. Nat. Med. 26, 1229–1234 (2020).

    Article  CAS  PubMed  Google Scholar 

  93. van der Laak, J., Litjens, G. & Ciompi, F. Deep learning in histopathology: the path to the clinic. Nat. Med. 27, 775–784 (2021).

    Article  PubMed  Google Scholar 

  94. Willemink, M. J. et al. Preparing medical imaging data for machine learning. Radiology 295, 4–15 (2020).

    Article  PubMed  Google Scholar 

  95. Irvin, J. et al. CheXpert: a large chest radiograph dataset with uncertainty labels and expert comparison. in Proceedings of the AAAI Conference on Artificial Intelligence vol. 33, 590–597 (2019).

  96. Kelly, C. J., Karthikesalingam, A., Suleyman, M., Corrado, G. & King, D. Key challenges for delivering clinical impact with artificial intelligence. BMC Med. 17, 195 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  97. DeGrave, A. J., Janizek, J. D. & Lee, S.-I. AI for radiographic COVID-19 detection selects shortcuts over signal. Nat. Mach. Intell. 3, 610–619 (2021).

    Article  Google Scholar 

  98. Cutillo, C. M. et al. Machine intelligence in healthcare: perspectives on trustworthiness, explainability, usability, and transparency. NPJ Digit. Med. 3, 47 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Sendak, M. P., Gao, M., Brajer, N. & Balu, S. Presenting machine learning model information to clinical end users with model facts labels. NPJ Digit. Med. 3, 41 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  100. Saporta, A. et al. Deep learning saliency maps do not accurately highlight diagnostically relevant regions for medical image interpretation. Preprint at medRxiv https://doi.org/10.1101/2021.02.28.21252634 (2021).

  101. Ehsan, U. et al. The who in explainable AI: how AI background shapes perceptions of AI explanations. Preprint at https://arxiv.org/abs/2107.13509 (2021).

  102. Reyes, M. et al. On the interpretability of artificial intelligence in radiology: Challenges and opportunities. Radio. Artif. Intell. 2, e190043 (2020).

    Article  Google Scholar 

  103. Liu, C. et al. On the replicability and reproducibility of deep learning in software engineering. Preprint at https://arxiv.org/abs/2006.14244 (2020).

  104. Beam, A. L., Manrai, A. K. & Ghassemi, M. Challenges to the reproducibility of machine learning models in health care. J. Am. Med. Assoc. 323, 305–306 (2020).

  105. Gerke, S., Babic, B., Evgeniou, T. & Cohen, I. G. The need for a system view to regulate artificial intelligence/machine learning-based software as medical device. NPJ Digit. Med. 3, 53 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  106. Lee, C. S. & Lee, A. Y. Clinical applications of continual learning machine learning. Lancet Digit. Health 2, e279–e281 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  107. Food and Drug Administration. Proposed Regulatory Framework for Modifications to Artificial Intelligence/Machine Learning (AI/ML)-Based Software as a Medical Device (SaMD): Discussion Paper and Request for Feedback (FDA, 2019).

  108. Morley, J. et al. The debate on the ethics of AI in health care: a reconstruction and critical review. SSRN http://dx.doi.org/10.2139/ssrn.3486518 (2019.

  109. Price, W. N., Gerke, S. & Cohen, I. G. Potential liability for physicians using artificial intelligence. J. Am. Med. Assoc. 322, 1765–1766 (2019).

    Article  Google Scholar 

  110. Larson, D. B., Magnus, D. C., Lungren, M. P., Shah, N. H. & Langlotz, C. P. Ethics of using and sharing clinical imaging data for artificial intelligence: a proposed framework. Radiology 295, 675–682 (2020).

    Article  PubMed  Google Scholar 

  111. Kaissis, G. A., Makowski, M. R., Rückert, D. & Braren, R. F. Secure, privacy-preserving and federated machine learning in medical imaging. Nat. Mach. Intell. 2, 305–311 (2020).

    Article  Google Scholar 

  112. Larrazabal, A. J., Nieto, N., Peterson, V., Milone, D. H. & Ferrante, E. Gender imbalance in medical imaging datasets produces biased classifiers for computer-aided diagnosis. Proc. Natl Acad. Sci. USA 117, 12592–12594 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  113. Vyas, D. A., Eisenstein, L. G. & Jones, D. S. Hidden in plain sight: reconsidering the use of race correction in clinical algorithms. N. Engl. J. Med. 383, 874–882 (2020).

    Article  PubMed  Google Scholar 

  114. Obermeyer, Z., Powers, B., Vogeli, C. & Mullainathan, S. Dissecting racial bias in an algorithm used to manage the health of populations. Science 366, 447–453 (2019).

    Article  CAS  PubMed  Google Scholar 

  115. Cirillo, D. et al. Sex and gender differences and biases in artificial intelligence for biomedicine and healthcare. NPJ Digit. Med. 3, 81 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank A. Tamkin and N. Phillips for their feedback. E.J.T. receives funding support from US National Institutes of Health grant UL1TR002550.

Author information

Authors and Affiliations

Authors

Contributions

P.R. and E.J.T. conceptualized this Review. E.C., O.B. and P.R. were responsible for the design and synthesis of this Review. All authors contributed to writing and editing the manuscript.

Corresponding author

Correspondence to Eric J. Topol.

Ethics declarations

Competing interests

The authors declare no competing interests.

Peer review

Peer review information

Nature Medicine thanks Despina Kontos and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Karen O’Leary was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rajpurkar, P., Chen, E., Banerjee, O. et al. AI in health and medicine. Nat Med 28, 31–38 (2022). https://doi.org/10.1038/s41591-021-01614-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41591-021-01614-0

This article is cited by

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing