Opportunities & Challenges of Artificial Intelligent-Powered Technology in Healthcare

Main Article Content

M. Sawkat Anwer, PhD, DMVH

Abstract

Artificial Intelligent (AI)-powered technology is expected to significantly alter the way healthcare is delivered. Artificial Intelligence tools, such as machine learning and deep learning, have shown promise in supporting diagnostic assessments, recommending treatments, guiding surgical care, monitoring patients, supporting population health management, and enhancing drug development research. These tools at varying stages of maturity can also reduce provider burden and increase efficiency by recording digital notes, optimizing operational processes, and automating laborious tasks. Challenges surrounding AI tools include high-quality data access, potentially biased data, inadequate transparency, and uncertainty over liability. Fundamental changes in governmental oversight of health care, industry-hospital communication, the patient-provider relationship, and human-AI cooperation will be necessary to take advantage of the opportunities and overcome the challenges. We need to be critical and at the same time receptive as we embrace AI tools to deliver healthcare. It would be important to maintain human oversight and control to avoid unintended consequences of runaway machines making life and death decisions.

Article Details

How to Cite
ANWER, M. Sawkat. Opportunities & Challenges of Artificial Intelligent-Powered Technology in Healthcare. Medical Research Archives, [S.l.], v. 12, n. 3, mar. 2024. ISSN 2375-1924. Available at: <https://esmed.org/MRA/mra/article/view/5141>. Date accessed: 13 apr. 2024. doi: https://doi.org/10.18103/mra.v12i3.5141.
Section
Research Articles

References

1. Israel, J. AI for Dummies: A Beginner's Guide to Artificial Intelligence. 2023 Kindle Edition.
2. A beginner's guide to artificial intelligence and machine learning - IBM Developer Fundamental AI | NIST
3. Yang S, Zhu F, Ling X, Liu Q and Zhao P (2021) Intelligent Health Care: Applications of Deep Learning in Computational Medicine. Front. Genet. 12:607471.doi: 10.3389/fgene.2021.607471
4. Tai MC. The impact of artificial intelligence on human society and bioethics. Tzu Chi Med J. 2020 Aug 14;32(4):339-343. doi: 10.4103/tcmj.tcmj_71_20. PMID: 33163378; PMCID: PMC7605294.
5. Artificial Intelligence in Health Care: Benefits and Challenges of Technologies to Augment Patient Care | U.S. GAO-21-7SP, November 2020 Artificial Intelligence in Health Care: Benefits and Challenges of Technologies to Augment Patient Care | U.S. GAO
6. Coiera E, Kocaballi B, Halamka J, et al. The digital scribe. npj Digital Med 1, 58 (2018). https://doi.org/10.1038/s41746-018-0066-9.
7. van Buchem MM, Boosman H, Bauer MP, et al. The digital scribe in clinical practice: a scoping review and research agenda. npj Digit. Med. 4, 57 (2021). https://doi.org/10.1038/s41746-021-00432-5).
8. Matulis JC, Kok SN, Dankbar EC, Majka AJ. A survey of outpatient Internal Medicine clinician perceptions of diagnostic error. Diagn. Berl. Ger. 2020;7:107–114. doi: 10.1515/dx-2019-0070.
9. Singh H, Meyer AND, Thomas EJ. The frequency of diagnostic errors in outpatient care: Estimations from three large observational studies involving US adult populations. BMJ Qual. Saf. 2014;23:727–731. doi: 10.1136/bmjqs-2013-002627.
10. Watari T, Tokuda Y, Mitsuhashi S, et al. Factors and impact of physicians’ diagnostic errors in malpractice claims in Japan. PLoS ONE. 2020;15:e0237145. doi: 10.1371/journal.pone.0237145.
11. Blease C, Kharko A, Locher C, DesRoches CM, Mandl KD. US primary care in 2029: A Delphi survey on the impact of machine learning. PLoS ONE. 2020;15 doi: 10.1371/journal.pone.0239947.
12. Coughlan JJ, Mullins CF, Kiernan TJ. Diagnosing, fast and slow. Postgrad Med. J. 2020 doi: 10.1136/postgradmedj-2019-137412.
13. Semigran HL, Levine DM, Nundy S, Mehrotra A. Comparison of Physician and Computer Diagnostic Accuracy. JAMA Intern. Med. 2016;176:1860–1861. doi: 10.1001/jamainternmed.2016.6001.
14. Gilbert S, Mehl A, Baluch A, et al. How accurate are digital symptom assessment apps for suggesting conditions and urgency advice? A clinical vignettes comparison to GPs. BMJ Open. 2020;10:e040269. doi: 10.1136/bmjopen-2020-040269.
15. Berry AC, Berry NA, Wang B, et al. Symptom checkers versus doctors: A prospective, head-to-head comparison for cough. Clin Respir J. 2020 Apr;14(4):413-415. doi: 10.1111/crj.13135. Epub 2020 Jan 4. PMID: 31860762.
16. Harada Y, Katsukura S, Kawamura R, Shimizu T. Efficacy of Artificial-Intelligence-Driven Differential-Diagnosis List on the Diagnostic Accuracy of Physicians: An Open-Label Randomized Controlled Study. Int J Environ Res Public Health. 2021 Feb 21;18(4):2086. doi: 10.3390/ijerph18042086. PMID: 33669930; PMCID: PMC7924871.
17. Jha S, Topol EJ. Adapting to artificial intelligence: radiologists and pathologists as information specialists. JAMA 316(22), 2353–2354 (2016).
18. Hosny A, Parmar C, Quackenbush J, Schwartz LH, Aerts HJWL. Artificial intelligence in radiology. Nat. Rev. Cancer 18(8), 500–510 (2018).
19. Bi WL, Hosny A, Schabath MB et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J. Clin. 69(2), 127–157 (2019).
20. Wu N, Phang J, Park J, et al. Deep Neural Networks Improve Radiologists' Performance in Breast Cancer Screening. IEEE Trans Med Imaging. 2020 Apr;39(4):1184-1194. doi: 10.1109/TMI.2019.2945514. Epub 2019 Oct 7. PMID: 31603772; PMCID: PMC7427471.
21. McKinney SM, Sieniek M, Godbole V, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020 Jan 2;577(7788):89-94.
22. Ghorbani A, Ouyang D, Abid A, et al. Deep learning interpretation of echocardiograms. npj Digit. Med. 3, 10 (2020). https://doi.org/10.1038/s41746-019-0216-8
23. Ouyang D, He B, Ghorbani A, et al. Video-based AI for beat-to-beat assessment of cardiac function. Nature. 2020 Apr;580(7802):252-256. doi: 10.1038/s41586-020-2145-8. Epub 2020 Mar 25. PMID: 32269341; PMCID: PMC8979576.
24. Ardila D, Kiraly AP, Bharadwaj S, 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). https://doi.org/10.1038/s41591-019-0447-x
25. Leming M, Das S, Im H. Adversarial confound regression and uncertainty measurements to classify heterogeneous clinical MRI in Mass General Brigham. PLoS ONE 18(3): e0277572 (2023). https://doi.org/10.1371/journal.pone.0277572
26. Försch S, Klauschen F, Hufnagl P, Roth W. Artificial intelligence in pathology. Dtsch Arztebl Int 2021; 118: 199–204. DOI: 10.3238/arztebl.m2021.0011;
27. Fuetsch, S, Glasner, C, Woerl, AC, et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat Med 29, 430–439 (2023). https://doi.org/10.1038/s41591-022-02134-1
28. Steiner DF, MacDonald R, Liu Y, et al. Impact of Deep Learning Assistance on the Histopathologic Review of Lymph Nodes for Metastatic Breast Cancer. Am J Surg Pathol. 2018 Dec;42(12):1636-1646. doi: 10.1097/PAS.0000000000001151. PMID: 30312179; PMCID: PMC6257102.
29. Farina E, Nabhen JJ, Dacoregio MI, Batalini F, Moraes FY. An overview of artificial intelligence in oncology. Future Sci OA. 2022 Feb 10;8(4):FSO787. doi: 10.2144/fsoa-2021-0074. PMID: 35369274; PMCID: PMC8965797.
30. Huynh E, Hosny A, Guthier C, et al. Artificial intelligence in radiation oncology. Nat Rev Clin Oncol. 2020 Dec;17(12):771-781. doi: 10.1038/s41571-020-0417-8. Epub 2020 Aug 25. PMID: 32843739.
31. Huang P, Lin CT, Li Y, 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. 2019 Nov;1(7):e353-e362. doi: 10.1016/S2589-7500(19)30159-1. Epub 2019 Oct 17. PMID: 32864596; PMCID: PMC7450858.
32. Kather JN, Pearson AT, Halama N, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019 Jul;25(7):1054-1056. doi: 10.1038/s41591-019-0462-y. Epub 2019 Jun 3. PMID: 31160815; PMCID: PMC7423299.
33. Miller KJ, Müller K-R, Hermes D. Basis profile curve identification to understand electrical stimulation effects in human brain networks. PLoS Comput Biol 17(9): e1008710 (2021). https://doi.org/10.1371/journal.pcbi.1008710)
34. Ouyang D, Theurer J, Stein NR, et al. Electrocardiographic deep learning for predicting postprocedural mortality: a model development and validation study. The Lancet Digital Health. www.thelancet.com/digital-health Vol 6 January 2024 E70-E78, DOI:https://doi.org/10.1016/S2589-7500(23)00220-0
35. Jurmeister P, Bockmayr M, Seegerer P, et al. Machine learning analysis of DNA methylation profiles distinguishes primary lung squamous cell carcinomas from head and neck metastases. Sci. Transl. Med.11,eaaw8513(2019).DOI:10.1126/scitranslmed.aaw8513
36. Hoadley KA, Yau C, Hinoue T, et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell. 2018 Apr 5;173(2):291-304.e6. doi: 10.1016/j.cell.2018.03.022. PMID: 29625048; PMCID: PMC5957518.
37. Hoberger M, von Laffert M, Heim D, Klauschen F: Histomorphological and molecular profiling: friends not foes! Morpho-molecular analysis reveals agreement between histological and molecular profiling. Histopathology. 2019 Nov;75(5):694-703. doi: 10.1111/his.13930. Epub 2019 Sep 5. PMID: 31152602.
38. Zhou D, Tian F, Tian X, et al. Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer. Nat Commun. 2020 Jun 11;11(1):2961. doi: 10.1038/s41467-020-16777-6. PMID: 32528084; PMCID: PMC7289893.
39. Zhao S, Wang S, Pan P, et al. Magnitude, Risk Factors, and Factors Associated With Adenoma Miss Rate of Tandem Colonoscopy: A Systematic Review and Meta-analysis. Gastroenterology. 2019 May;156(6):1661-1674.e11. doi: 10.1053/j.gastro.2019.01.260. Epub 2019 Feb 6. PMID: 30738046.
40. Gong D, Wu L, Zhang J, et al. Detection of colorectal adenomas with a real-time computer-aided system (ENDOANGEL): a randomised controlled study. Lancet Gastroenterol Hepatol. 2020 Apr;5(4):352-361. doi: 10.1016/S2468-1253(19)30413-3. Epub 2020 Jan 22. Erratum in: Lancet Gastroenterol Hepatol. 2020 Apr;5(4):e3. PMID: 31981518.
41. Wang P, Liu X, Berzin TM, 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), DOI: https://doi.org/10.1016/S2468-1253(20)30051-0.
42. Freedman D, Blau Y, Katzir L, et al. Detecting deficient coverage in colonoscopies. IEEE Trans. Med. Imaging 39, 3451–3462 (2020),
Doi: 10.1109/TMI.2020.2994221.
43. Loftus TJ, Altieri MS, Balch JA, et al. Artificial Intelligence–enabled Decision Support in Surgery: State-of-the-art and Future Directions. Annals of Surgery 278(1):p 51-58, July 2023. | DOI: 10.1097/SLA.0000000000005853
44. Zhou XY, Guo Y, Shen M, Yang GZ. Application of artificial intelligence in surgery. Front Med. 2020 Aug;14(4):417-430.
Doi: 10.1007/s11684-020-0770-0. Epub 2020 Jul 23. PMID: 32705406.
45. Hashimoto DA, Rosman G, Rus D, Meireles OR. “Artificial Intelligence in Surgery: Promises and Perils,” Annals of Surgery, 268(1):p 70-76, July 2018. |
Doi: 10.1097/SLA.0000000000002693
46. Saeidi H, Opfermann JD, Kam M, et al. Autonomous robotic laparoscopic surgery for intestinal anastomosis. SCIENCE ROBOTICS 26 Jan 2022 Vol 7, Issue 62,
Doi: 10.1126/scirobotics.abj2908
47. Reddy S, Fox J, Purohit MP. Artificial intelligence-enabled healthcare delivery. Journal of the Royal Society of Medicine, vol. 112, no. 1 (2019). DOI: 10.1177/0141076818815510.
48. Dubey A, Tiwari A. Artificial intelligence and remote patient monitoring in US healthcare market: a literature review. J Mark Access Health Policy. 2023 May 3;11(1):2205618. doi: 10.1080/20016689.2023.2205618. PMID: 37151736; PMCID: PMC10158563.
49. Sarkar C, Das B, Rawat VS et al. Artificial Intelligence and Machine Learning Technology Driven Modern Drug Discovery and Development. Int J Mol Sci. 2023 Jan 19;24(3):2026. doi: 10.3390/ijms24032026. PMID: 36768346; PMCID: PMC9916967
50. Jiménez-Luna J, Grisoni F, Weskamp N, Schneider G. Artificial intelligence in drug discovery: recent advances and future perspectives. Expert Opin Drug Discov. 2021 Sep;16(9):949-959. doi: 10.1080/17460441.2021.1909567. Epub 2021 Apr 2. PMID: 33779453.
51. Paul D, Sanap G, Shenoy S, Kalyane D, Kalia K, Tekade RK. Artificial intelligence in drug discovery and development. Drug Discov Today. 2021 Jan;26(1):80-93. doi: 10.1016/j.drudis.2020.10.010. Epub 2020 Oct 21. PMID: 33099022; PMCID: PMC7577280.
52. DiMasi JA, Grabowski HG, Hansen RW. Innovation in the pharmaceutical industry: New estimates of R&D costs. J Health Econ. 2016 May;47:20-33.
Doi: 10.1016/j.jhealeco.2016.01.012. Epub 2016 Feb 12. PMID: 26928437.
53. Turner, J.R. New Drug Development; Springer: New York, NY, USA, 2010.
54. Blanco-González A, Cabezón A, Seco-González A, et al. The Role of AI in Drug Discovery: Challenges, Opportunities, and Strategies. Pharmaceuticals (Basel). 2023 Jun 18;16(6):891. doi: 10.3390/ph16060891. PMID: 37375838; PMCID: PMC10302890.
55. Neil Savage. Tapping into the drug discovery potential of AI (nature.com)
Doi: https://doi.org/10.1038/d43747-021-00045-7
56. Obermeyer Z, Emanuel EJ. Predicting the Future - Big Data, Machine Learning, and Clinical Medicine. N Engl J Med. 2016 Sep 29;375(13):1216-9.
Doi: 10.1056/NEJMp1606181. PMID: 27682033; PMCID: PMC5070532.
57. Sendak M, D’Arcy J, Kashyap S, et al. A Path for Translation of Machine Learning Products into Healthcare Delivery. EMJ Innov. 2020; https://doi.org/10.33590/emjinnov/19-00172.
58. Price II WN, “Big Data, Patents, and the Future of Medicine”, Cardozo Law Review, vol. 37, no. 4 (2016). "Big Data, Patents, and the Future of Medicine" by W. Nicholson Price II (umich.edu)
59. Hall MA, “Property, Privacy, and the Pursuit of Interconnected Electronic Medical Records”, Iowa Law Review, vol. 95, no. 2 (2010). ULGM_03_273154.tex (uapd.com)
60. Price II WN. Medical AI and Contextual Bias (March 8, 2019). 33 Harv. J.L. & Tech. 66 (2019), U of Michigan Public Law Research Paper No. 632, Available at SSRN: https://ssrn.com/abstract=3347890
61. Washington V, DeSalvo K, Mostashari F, Blumenthal D. The HITECH Era and the Path Forward. N Engl J Med. 2017 Sep 7;377(10):904-906. doi: 10.1056/NEJMp1703370. PMID: 28877013.
62. Halamka JD, Mandl KD, Tang PC. Early experiences with personal health records. J Am Med Inform Assoc. 2008 Jan-Feb;15(1):1-7. doi: 10.1197/jamia.M2562. Epub 2007 Oct 18. PMID: 17947615; PMCID: PMC2274878.
Popejoy A, Fullerton S. Genomics is failing on diversity. Nature 538, 161–164 (2016). https://doi.org/10.1038/538161a
64. Watson W, Marsh C. Artificial Intelligence Bias in Healthcare (boozallen.com).
65. Bernal J, Mazo C. Transparency of Artificial Intelligence in Healthcare: Insights from Professionals in Computing and Healthcare Worldwide. Appl. Sci. 2022, 12, 10228. https://doi.org/ 10.3390/app122010228
66. Tyson A, Pasquini G, Spencer A, Funk C. Pew Research Center. February 22, 2023. Available at: https://www.pewresearch.org/science/2023/02/22/60-of-americans-would-be-uncomfortable-with-provider-relying-on-ai-in-their-own-health-care/.
67. Kiseleva A, Kotzinos D, De Hert P. Transparency of AI in Healthcare as a Multilayered System of Accountabilities: Between Legal Requirements and Technical Limitations. Front. Artif. Intell. 5:879603 (2020).
Doi: 10.3389/frai.2022.879603.
68. Linardatos P, Papastefanopoulos V, Kotsiantis S. Explainable AI: A Review of Machine Learning Interpretability Methods. Entropy (Basel). 2020 Dec 25;23(1):18. doi: 10.3390/e23010018. PMID: 33375658; PMCID: PMC7824368.
69. Artificial intelligence act (europa.eu)
70. Tanenbaum WA, Song K, Malek LA. 2022. Theories of AI liability: It's still about the human element | Reuters
71. EU Expert Group report on Liability and New Technologies (2019). https://www.europarl.europa.eu/meetdocs/2014_2019/plmrep/COMMITTEES/JURI/DV/2020/01-09/AI-report_EN.pdf
72. Savcisens G, Eliassi-Rad T, Hansen LK, et al. Using sequences of life-events to predict human lives. Nat Comput Sci 4, 43–56 (2024). https://doi.org/10.1038/s43588-023-00573-5