Empowering medicine with artificial intelligence: Multidimensional applications, key challenges, and future directions_Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Authors：

ZHOU Xiaoqin ^1,2 , LIU Huizhen ¹ , WAGN Ting ¹ , LIU Xueting ³ , LIU Fang ¹ ,  KANG Deying ^1,2

1. Center of Biostatistics, Design, Measurement and Evaluation (CBDME), Department of Clinical Research Management, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
2. Chinese Evidence-based Medicine Center, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;
3. Discipline Construction Department, West China Hospital, Sichuan University, Chengdu, 610041, P. R. China;

Corresponding author：

KANG Deying, Email: deyingkang@126.com

Keywords：

Artificial Intelligence; healthcare; smart healthcare; precision medicine; medical ethics; inter-disciplinary integration

DOI：

10.7507/1007-4848.202407041

Video：

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Abstract Full text Figures/Tables Video References Cited by

This comprehensive review systematically explores the multifaceted applications, inherent challenges, and promising future directions of artificial intelligence (AI) within the medical domain. It meticulously examines AI's specific contributions to basic medical research, disease prevention, intelligent diagnosis, treatment, rehabilitation, nursing, and health management. Furthermore, the review delves into AI's innovative practices and pivotal roles in clinical trials, hospital administration, medical education, as well as the realms of medical ethics and policy formulation. Notably, the review identifies several key challenges confronting AI in healthcare, encompassing issues such as inadequate algorithm transparency, data privacy concerns, absent regulatory standards, and incomplete risk assessment frameworks. Looking ahead, the future trajectory of AI in healthcare encompasses enhancing algorithm interpretability, propelling generative AI applications, establishing robust data-sharing mechanisms, refining regulatory policies and standards, nurturing interdisciplinary talent, fostering collaboration among industry, academia, and medical institutions, and advancing inclusive, personalized precision medicine. Emphasizing the synergy between AI and emerging technologies like 5G, big data, and cloud computing, this review anticipates a new era of intelligent collaboration and inclusive sharing in healthcare. Through a multidimensional analysis, it presents a holistic overview of AI's medical applications and development prospects, catering to researchers, practitioners, and policymakers in the healthcare sector. Ultimately, this review aims to catalyze the deep integration and innovative deployment of AI technology in healthcare, thereby driving the sustainable advancement of smart healthcare.

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5.	He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med, 2019, 25(1): 30-36.
6.	Esteva A, Chou K, Yeung S, et al. Deep learning-enabled medical computer vision. NPJ Digit Med, 2021, 4(1): 5.
7.	Waldman CE, Hermel M, Hermel JA, et al. Artificial intelligence in healthcare: A primer for medical education in radiomics. Per Med, 2022, 19(5): 445-456.
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14.	Li ZA, Sant S, Cho SK, et al. Synovial joint-on-a-chip for modeling arthritis: progress, pitfalls, and potential. Trends Biotechnol, 2023, 41(4): 511-527.
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29.	Goh YS, Ow Yong JQY, Chee BQH, et al. Machine learning in health promotion and behavioral change: Scoping review. J Med Internet Res, 2022, 24(6): e35831.
30.	Olawade DB, Wada OJ, David-Olawade AC, et al. Using artificial intelligence to improve public health: A narrative review. Front Public Health, 2023, 11: 1196397.
31.	Hossain E, Rana R, Higgins N, et al. Natural language processing in electronic health records in relation to healthcare decision-making: A systematic review. Comput Biol Med, 2023, 155: 106649.
32.	Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: General overview. Korean J Radiol, 2017, 18(4): 570-584.
33.	Chugh A, Jain C. A Systematic Review on ECG and EMG biomedical signal using deep-learning approaches. In: Manju KS, Islam SMN. Artificial Intelligence-based Healthcare Systems, Cham: Springer Nature Switzerland. 2023: 145-161.
34.	McGenity C, Clarke EL, Jennings C, et al. Artificial intelligence in digital pathology: A systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med, 2024, 7(1): 114.
35.	Wekesa JS, Kimwele M. A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment. Front Genet, 2023, 14: 1199087.
36.	Ohta T, Hananoe A, Fukushima-Nomura A, et al. Best practices for multimodal clinical data management and integration: An atopic dermatitis research case. Allergol Int, 2024, 73(2): 255-263.
37.	Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med, 2023, 388(13): 1201-1208.
38.	Yalavarthi S. AI in medicine: From diagnosis to treatment decision support. Intern J Emerg Technol Innov Res, 2023, 10(10): e214-e217.
39.	Fernandes JG. Artificial intelligence in telemedicine. In: Lidströmer N, Ashrafian H. (eds) Artificial Intelligence in Medicine. Springer, Cham. 2022, 1219-1227.
40.	Rezaei T, Khouzani PJ, Khouzani SJ, et al. Integrating artificial intelligence into telemedicine: revolutionizing healthcare delivery. Kindle, 2023, 3(1): 1-161.
41.	Farrow L, Meek D, Leontidis G, et al. The Clinical practice integration of artificial intelligence (CPI-AI) framework. Bone Joint Res, 2024, 13(9): 507-512.
42.	Singh M, Nath G. Artificial intelligence and anesthesia: A narrative review. Saudi J Anaesth, 2022, 16(1): 86-93.
43.	Song B, Zhou M, Zhu J. Necessity and Importance of Developing AI in anesthesia from the perspective of clinical safety and information security. Med Sci Monit, 2023, 29: e938835.
44.	Mamo HB, Adamiak M, Kunwar A. 3D printed biomedical devices and their applications: A review on state-of-the-art technologies, existing challenges, and future perspectives. J Mech Behav Biomed Mater, 2023, 143: 105930.
45.	Lanotte F, O'Brien MK, Jayaraman A. AI in rehabilitation medicine: Opportunities and challenges. Ann Rehabil Med, 2023, 47(6): 444-458.
46.	Abedi A, Colella TJF, Pakosh M, et al. Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digit Med, 2024, 7(1): 25.
47.	Khokale R, S Mathew G, Ahmed S, et al. Virtual and augmented reality in post-stroke rehabilitation: A narrative review. Cureus, 2023, 15(4): e37559.
48.	Omarov B, Narynov S, Zhumanov Z. Artificial intelligence-enabled chatbots in mental health: A systematic review. Computers, Materials & Continua, 2023, 74(3): 5105-5122.
49.	Al Kuwaiti A, Nazer K, Al-Reedy A, et al. A review of the role of artificial intelligence in healthcare. J Pers Med, 2023, 13(6): 951.
50.	Jeddi Z, Bohr A, Chief editor. Remote patient monitoring using artificial intelligence. Academic Press, 2020, 203-234.
51.	Hunter DJ, Holmes C. Where medical statistics meets artificial intelligence. N Engl J Med, 2023, 389(13): 1211-1219.
52.	Hernandez AF, Lindsell CJ. The future of clinical trials: Artificial to augmented to applied intelligence. JAMA, 2023, 330(21): 2061-2063.
53.	Cunningham JW, Singh P, Reeder C, et al. Natural language processing for adjudication of heart failure in a multicenter clinical trial: A secondary analysis of a randomized clinical trial. JAMA Cardiol, 2024, 9(2): 174-181.
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55.	Cruz Rivera S, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nat Med, 2020, 26(9): 1351-1363.
56.	Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nat Med, 2020, 26(9): 1364-1374.
57.	Bhagat SV, Kanyal D. Navigating the future: The transformative impact of artificial intelligence on hospital management— A comprehensive review. Cureus, 2024, 16(2): e54518.
58.	Riyanti R. Legal status of artificial intelligence-based health insurance services: Challenges, opportunities for customer protection. Intern J Health Sci, 2023, 6(2): 1023-1034.
59.	Jebreen K, Radwan E, Kammoun-Rebai W, et al. Perceptions of undergraduate medical students on artificial intelligence in medicine: Mixed-methods survey study from Palestine. BMC Med Educ, 2024, 24(1): 507.
60.	Knopp MI, Warm EJ, Weber D, et al. AI-enabled medical education: Threads of change, promising futures, and risky realities across four potential future worlds. JMIR Med Educ, 2023, 9: e50373.
61.	Ong JCL, Chang SY, William W, et al. Ethical and regulatory challenges of large language models in medicine. Lancet Digit Health, 2024, 6(6): 428-432.
62.	Kumar P, Chauhan S, Awasthi LK. Artificial intelligence in healthcare: Review, ethics, trust challenges & future research directions. Eng Appl Artif Intell, 2023, 120: 105894.
63.	Wang Y, Liu K, He Y, et al. Enhancing air quality forecasting: A novel spatio-temporal model integrating graph convolution and multi-head attention mechanism. Atmosphere, 2024, 15(4): 418.
64.	Zhang Y, Sun B, Yu Y, et al. Multimodal fusion of liquid biopsy and CT enhances differential diagnosis of early-stage lung adenocarcinoma. NPJ Precis Oncol, 2024, 8(1): 50.
65.	Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artif Intell Med, 2024, 151: 102861.
66.	Singh AP, Saxena R, Saxena S, et al. Artificial intelligence revolution in healthcare: Transforming diagnosis, treatment, and patient care. Asian J Advances Res, 2024, 7(1): 241-263.
67.	Reddy S. Generative AI in healthcare: An implementation science informed translational path on application, integration and governance. Implement Sci, 2024, 19(1): 27.
68.	Stark L. Medicine's Lessons for AI Regulation. N Engl J Med, 2023, 389(24): 2213-2215.

1. Wang F, Preininger A. AI in health: State of the art, challenges, and future directions. Yearb Med Inform, 2019, 28(1): 16-26.
2. Ray A, Bhardwaj A, Malik YK, et al. Artificial intelligence and Psychiatry: An overview. Asian J Psychiatr, 2022, 70: 103021.
3. Ali O, Abdelbaki W, Shrestha A, et al. A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. J Innov Knowl, 2023, 8(1): 100333.
4. Melnyk O, Ismail A, Ghorashi NS, et al. Generative artificial intelligence terminology: A primer for clinicians and medical researchers. Cureus, 2023, 15(12): e49890.
5. He J, Baxter SL, Xu J, et al. The practical implementation of artificial intelligence technologies in medicine. Nat Med, 2019, 25(1): 30-36.
6. Esteva A, Chou K, Yeung S, et al. Deep learning-enabled medical computer vision. NPJ Digit Med, 2021, 4(1): 5.
7. Waldman CE, Hermel M, Hermel JA, et al. Artificial intelligence in healthcare: A primer for medical education in radiomics. Per Med, 2022, 19(5): 445-456.
8. Murali L, Gopakumar G, Viswanathan DM, et al. Towards electronic health record-based medical knowledge graph construction, completion, and applications: A literature study. J Biomed Inform, 2023, 143: 104403.
9. Hutson M. Hypotheses devised by AI could find 'blind spots' in research. Nature, 2023, Epub ahead of print.
10. Acosta JN, Falcone GJ, Rajpurkar P, et al. Multimodal biomedical AI. Nat Med, 2022, 28(9): 1773-1784.
11. Guo K, Wu M, Soo Z, et al. Artificial intelligence-driven biomedical genomics. Knowledge-Based Systems, 2023, 279: 110937.
12. Feuerriegel S, Frauen D, Melnychuk V, et al. Causal machine learning for predicting treatment outcomes. Nat Med, 2024, 30(4): 958-968.
13. Deng S, Li C, Cao J, et al. Organ-on-a-chip meets artificial intelligence in drug evaluation. Theranostics, 2023, 13(13): 4526-4558.
14. Li ZA, Sant S, Cho SK, et al. Synovial joint-on-a-chip for modeling arthritis: progress, pitfalls, and potential. Trends Biotechnol, 2023, 41(4): 511-527.
15. Dasgupta I, Rangineni DP, Abdelsaid H, et al. Tiny organs, big impact: how microfluidic organ-on-chip technology is revolutionizing mucosal tissues and vasculature. Bioengineering (Basel), 2024, 11(5): 476.
16. Thenuwara G, Javed B, Singh B, et al. Biosensor-enhanced organ-on-a-chip models for investigating glioblastoma tumor microenvironment dynamics. Sensors (Basel), 2024, 24(9): 2865.
17. Holland I, Davies JA. Automation in the life science research laboratory. Front Bioeng Biotechnol, 2020, 8: 571777.
18. Gholap AD, Uddin MJ, Faiyazuddin M, et al. Advances in artificial intelligence for drug delivery and development: A comprehensive review. Comput Biol Med, 2024 Aug: 178: 108702.
19. Khan MK, Raza M, Shahbaz M, et al. The recent advances in the approach of artificial intelligence (AI) towards drug discovery. Front Chem, 2024 May 31: 12: 1408740.
20. Yadav S, Singh A, Singhal R, et al. Revolutionizing drug discovery: The impact of artificial intelligence on advancements in pharmacology and the pharmaceutical industry. Intelligent Pharmacy, 2024, 2(3): 367-380.
21. Kline A, Wang H, Li Y, et al. Multimodal machine learning in precision health: A scoping review. NPJ Digit Med, 2022, 5(1): 171.
22. Zheng S, Zhu Z, Liu Z, et al. Multi-modal graph learning for disease prediction. IEEE Trans Med Imaging, 2022, 41(9): 2207-2216.
23. Carini C, Seyhan AA. Tribulations and future opportunities for artificial intelligence in precision medicine. J Transl Med, 2024, 22(1): 411.
24. Delpino FM, Costa ÂK, Farias SR, et al. Machine learning for predicting chronic diseases: A systematic review. Public Health, 2022, 205-214.
25. Alimadadi A, Aryal S, Manandhar I, et al. Artificial intelligence and machine learning to fight COVID-19. Physiol Genomics, 2020, 52(4): 200-202.
26. Chisom O, Biu P, Umoh A, et al. Reviewing the role of AI in environmental monitoring and conservation: A data-driven revolution for our plane. World J Adv Res Rev, 2024, 21(1): 161-171.
27. Mensah GA, Fuster V, Murray CJL, et al. Global Burden of cardiovascular diseases and risks, 1990-2022. J Am Coll Cardiol, 2023, 82(25): 2350-2473.
28. Hoang T, Ky NM, Thuong NTN, et al. Artificial intelligence in pollution control and management: Status and future prospects. In: Ong HL, Doong R, Naguib R, Lim CP, Nagar AK, Chief editor. Artificial Intelligence and Environmental Sustainability: Challenges and Solutions in the Era of Industry 4.0. Singapore: Springer Nature Singapore, 2022: 23-43 https://doi.org/10.1007/978-981-19-1434-8_2.
29. Goh YS, Ow Yong JQY, Chee BQH, et al. Machine learning in health promotion and behavioral change: Scoping review. J Med Internet Res, 2022, 24(6): e35831.
30. Olawade DB, Wada OJ, David-Olawade AC, et al. Using artificial intelligence to improve public health: A narrative review. Front Public Health, 2023, 11: 1196397.
31. Hossain E, Rana R, Higgins N, et al. Natural language processing in electronic health records in relation to healthcare decision-making: A systematic review. Comput Biol Med, 2023, 155: 106649.
32. Lee JG, Jun S, Cho YW, et al. Deep learning in medical imaging: General overview. Korean J Radiol, 2017, 18(4): 570-584.
33. Chugh A, Jain C. A Systematic Review on ECG and EMG biomedical signal using deep-learning approaches. In: Manju KS, Islam SMN. Artificial Intelligence-based Healthcare Systems, Cham: Springer Nature Switzerland. 2023: 145-161.
34. McGenity C, Clarke EL, Jennings C, et al. Artificial intelligence in digital pathology: A systematic review and meta-analysis of diagnostic test accuracy. NPJ Digit Med, 2024, 7(1): 114.
35. Wekesa JS, Kimwele M. A review of multi-omics data integration through deep learning approaches for disease diagnosis, prognosis, and treatment. Front Genet, 2023, 14: 1199087.
36. Ohta T, Hananoe A, Fukushima-Nomura A, et al. Best practices for multimodal clinical data management and integration: An atopic dermatitis research case. Allergol Int, 2024, 73(2): 255-263.
37. Haug CJ, Drazen JM. Artificial intelligence and machine learning in clinical medicine, 2023. N Engl J Med, 2023, 388(13): 1201-1208.
38. Yalavarthi S. AI in medicine: From diagnosis to treatment decision support. Intern J Emerg Technol Innov Res, 2023, 10(10): e214-e217.
39. Fernandes JG. Artificial intelligence in telemedicine. In: Lidströmer N, Ashrafian H. (eds) Artificial Intelligence in Medicine. Springer, Cham. 2022, 1219-1227.
40. Rezaei T, Khouzani PJ, Khouzani SJ, et al. Integrating artificial intelligence into telemedicine: revolutionizing healthcare delivery. Kindle, 2023, 3(1): 1-161.
41. Farrow L, Meek D, Leontidis G, et al. The Clinical practice integration of artificial intelligence (CPI-AI) framework. Bone Joint Res, 2024, 13(9): 507-512.
42. Singh M, Nath G. Artificial intelligence and anesthesia: A narrative review. Saudi J Anaesth, 2022, 16(1): 86-93.
43. Song B, Zhou M, Zhu J. Necessity and Importance of Developing AI in anesthesia from the perspective of clinical safety and information security. Med Sci Monit, 2023, 29: e938835.
44. Mamo HB, Adamiak M, Kunwar A. 3D printed biomedical devices and their applications: A review on state-of-the-art technologies, existing challenges, and future perspectives. J Mech Behav Biomed Mater, 2023, 143: 105930.
45. Lanotte F, O'Brien MK, Jayaraman A. AI in rehabilitation medicine: Opportunities and challenges. Ann Rehabil Med, 2023, 47(6): 444-458.
46. Abedi A, Colella TJF, Pakosh M, et al. Artificial intelligence-driven virtual rehabilitation for people living in the community: A scoping review. NPJ Digit Med, 2024, 7(1): 25.
47. Khokale R, S Mathew G, Ahmed S, et al. Virtual and augmented reality in post-stroke rehabilitation: A narrative review. Cureus, 2023, 15(4): e37559.
48. Omarov B, Narynov S, Zhumanov Z. Artificial intelligence-enabled chatbots in mental health: A systematic review. Computers, Materials & Continua, 2023, 74(3): 5105-5122.
49. Al Kuwaiti A, Nazer K, Al-Reedy A, et al. A review of the role of artificial intelligence in healthcare. J Pers Med, 2023, 13(6): 951.
50. Jeddi Z, Bohr A, Chief editor. Remote patient monitoring using artificial intelligence. Academic Press, 2020, 203-234.
51. Hunter DJ, Holmes C. Where medical statistics meets artificial intelligence. N Engl J Med, 2023, 389(13): 1211-1219.
52. Hernandez AF, Lindsell CJ. The future of clinical trials: Artificial to augmented to applied intelligence. JAMA, 2023, 330(21): 2061-2063.
53. Cunningham JW, Singh P, Reeder C, et al. Natural language processing for adjudication of heart failure in a multicenter clinical trial: A secondary analysis of a randomized clinical trial. JAMA Cardiol, 2024, 9(2): 174-181.
54. Mahaffey KW, Gibson CM, Lopes RD. Innovation in event adjudication-human vs machine. JAMA Cardiol, 2024, 9(2): 101-102.
55. Cruz Rivera S, Liu X, Chan AW, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: The SPIRIT-AI extension. Nat Med, 2020, 26(9): 1351-1363.
56. Liu X, Cruz Rivera S, Moher D, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: The CONSORT-AI extension. Nat Med, 2020, 26(9): 1364-1374.
57. Bhagat SV, Kanyal D. Navigating the future: The transformative impact of artificial intelligence on hospital management— A comprehensive review. Cureus, 2024, 16(2): e54518.
58. Riyanti R. Legal status of artificial intelligence-based health insurance services: Challenges, opportunities for customer protection. Intern J Health Sci, 2023, 6(2): 1023-1034.
59. Jebreen K, Radwan E, Kammoun-Rebai W, et al. Perceptions of undergraduate medical students on artificial intelligence in medicine: Mixed-methods survey study from Palestine. BMC Med Educ, 2024, 24(1): 507.
60. Knopp MI, Warm EJ, Weber D, et al. AI-enabled medical education: Threads of change, promising futures, and risky realities across four potential future worlds. JMIR Med Educ, 2023, 9: e50373.
61. Ong JCL, Chang SY, William W, et al. Ethical and regulatory challenges of large language models in medicine. Lancet Digit Health, 2024, 6(6): 428-432.
62. Kumar P, Chauhan S, Awasthi LK. Artificial intelligence in healthcare: Review, ethics, trust challenges & future research directions. Eng Appl Artif Intell, 2023, 120: 105894.
63. Wang Y, Liu K, He Y, et al. Enhancing air quality forecasting: A novel spatio-temporal model integrating graph convolution and multi-head attention mechanism. Atmosphere, 2024, 15(4): 418.
64. Zhang Y, Sun B, Yu Y, et al. Multimodal fusion of liquid biopsy and CT enhances differential diagnosis of early-stage lung adenocarcinoma. NPJ Precis Oncol, 2024, 8(1): 50.
65. Esmaeilzadeh P. Challenges and strategies for wide-scale artificial intelligence (AI) deployment in healthcare practices: A perspective for healthcare organizations. Artif Intell Med, 2024, 151: 102861.
66. Singh AP, Saxena R, Saxena S, et al. Artificial intelligence revolution in healthcare: Transforming diagnosis, treatment, and patient care. Asian J Advances Res, 2024, 7(1): 241-263.
67. Reddy S. Generative AI in healthcare: An implementation science informed translational path on application, integration and governance. Implement Sci, 2024, 19(1): 27.
68. Stark L. Medicine's Lessons for AI Regulation. N Engl J Med, 2023, 389(24): 2213-2215.

Chinese Journal of Clinical Thoracic and Cardiovascular Surgery

Latest ArticlesEmpowering medicine with artificial intelligence: Multidimensional applications, key challenges, and future directions

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