Computer vision to enhance healthcare domain: An overview of features, implementation, and opportunities

Abstract

The emergence of Artificial Intelligence (AI) has already brought several advantages to the healthcare sector. Computer Vision (CV) is one of the growing modern AI technologies. The distribution and administration of medications are about to change by using CV for medication management. This system scans pharmaceutical labels and keeps track of the process from delivery to administration using cameras, sensors, and computer algorithms. In order to assure accuracy in medicine delivery and dose, the system also makes it easier for doctors, nurses, and chemists to communicate. The computer vision-driven medication management system can significantly lower the number of medical mistakes that result from inaccurate or missing prescriptions, improper doses, or simply forgetting to take a particular drug. An exhaustive literature review has been done to identify work related to the research objectives. This paper is about CV and their need in healthcare. Various tasks associated with CV in the healthcare domain are discussed. Targeted healthcare goals through CV traits are briefed. Finally, the significant applications of CVs in healthcare were identified and discussed. Nowadays, CV has practical uses in healthcare. Its methods are widely used since they have shown excellent utility in several medical contexts, including medical imaging and surgical planning. The CV is used to study how to program computers to comprehend digital pictures. Numerous medical applications utilise this technology, such as automated abnormality identification, illness diagnosis, and surgical procedure guiding. CV is expanding quickly and has enormous promise to enhance healthcare. Some of the many CV applications in the healthcare sector include patient identification systems, medical picture analysis, surgical simulation and illness diagnosis.

Keywords

Computer vision (CV) / Artificial intelligence (AI) / Healthcare / Patient / Images

References

[1] Leo M, Carcagnì P, Mazzeo PL, Spagnolo P, Cazzato D, Distante C. Analysis of facial information for healthcare applications: a survey on computer vision-based approaches. Information. 2020;11(3):128.

[2] Colantonio S, Coppini G, Giorgi D, Morales MA, Pascali MA. Computer vision for ambient assisted living: monitoring systems for personalised healthcare and wellness that are robust in the real world and accepted by users, carers, and society. In: Computer Vision for Assistive Healthcare. Academic Press;2018:147–182.

[3] Esteva A, Chou K, Yeung S, et al. Deep learning-enabled medical computer vision. NPJ digital medicine. 2021;4(1):5.

[4] Khang A, Abdullayev V, Litvinova E, Chumachenko S, Alyar AV, Anh PTN. Application of computer vision (CV) in the healthcare Ecosystem. In: Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press;2024:1–16.

[5] Ahad MAR, Antar AD, Shahid O. Vision-based action understanding for assistive healthcare: a short review. In: CVPR Workshops. 2019, June:1–11.

[6] Eswaran U, Khang A. Artificial intelligence (AI)-Aided computer vision (CV) in healthcare system. In: Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press;2024:125–137.

[7] Sharma A, Prasad KDV, Chakrasali SV, et al. Computer vision based healthcare system for identification of diabetes & its types using AI. Measurement: Sensors. 2023;27:100751.

[8] Awwad S, Tarvade S, Piccardi M, Gattas DJ. The use of privacy-protected computer vision to measure the quality of healthcare worker hand hygiene. Int J Qual Health Care. 2019;31(1):36–42.

[9] Mosquera C, Lara MAR, Díaz FN, Binder F, Benitez SE. Introducing computer vision into healthcare workflows. In: Digital Health: From Assumptions to Implementations. Cham: Springer International Publishing;2023:43–62.

[10] Lindroth H, Nalaie K, Raghu R, et al. Applied artificial intelligence in healthcare: a review of computer vision technology application in hospital settings. Journal of Imaging. 2024;10(4):81.

[11] Chen J, Cremer JF, Zarei K, Segre AM, Polgreen PM. Using computer vision and depth sensing to measure healthcare worker-patient contacts and personal protective equipment adherence within hospital rooms. Open Forum Infect Dis. 2016, January;3(1):ofv200. Oxford University Press.

[12] Artime-Ríos E, Suárez-Sánchez A, Sánchez-Lasheras F, Seguí-Crespo M. Computer vision syndrome in healthcare workers using video display terminals: an exploration of the risk factors. J Adv Nurs. 2022;78(7):2095–2110.

[13] Hua Z, Ke Y, Yang Z, Di Z, Pan G, Gao K. Computer vision-aided mmWave communications for indoor medical healthcare. Comput Biol Med. 2024;169:107869.

[14] Yeung S, Rinaldo F, Jopling J, et al. A computer vision system for deep learning-based detection of patient mobilisation activities in the ICU. NPJ digital medicine. 2019;2(1):11.

[15] Luo Z, Hsieh JT, Balachandar N, et al. Computer vision-based descriptive analytics of seniors’ daily activities for long-term health monitoring. Machine Learning for Healthcare (MLHC). 2018;2(1).

[16] Yeung S, Downing NL, Fei-Fei L, Milstein A. Bedside computer vision-moving artificial intelligence from driver assistance to patient safety. N Engl J Med. 2018;378(14):1271–1273.

[17] Lokhande M, Kalpanadevi D, Kate V, Tripathi AK, Bethapudi P. Study of computer vision applications in healthcare industry 4.0. In: Healthcare Industry 4.0. CRC Press;2024:151–166.

[18] Jain Y, Gandhi H, Burte A, Vora A. Mental and physical health management system using ML, computer vision and IoT sensor network. 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE;2020, November:786–791.

[19] Bao Y, Tang Z, Li H, Zhang Y. Computer vision and deep learning–based data anomaly detection method for structural health monitoring. Struct Health Monit. 2019;18(2):401–421.

[20] Kolikipogu R, Ramya D, Siddiqui M, Siddiqui A, Prakash CO. Introduction to computer vision aided data analytics in healthcare industry 4.0. In: Healthcare Industry 4.0. CRC Press;2024:1–18.

[21] Vij R, Arora S. Computer vision with deep learning techniques for neurodegenerative diseases analysis using neuroimaging: a survey. International Conference on Innovative Computing and Communications: Proceedings of ICICC 2021. 2022;2:179–189. Springer Singapore.

[22] Kitaguchi D, Takeshita N, Hasegawa H, Ito M. Artificial intelligence-based computer vision in surgery: recent advances and future perspectives. Annals of gastroenterological surgery. 2022;6(1):29–36.

[23] Thevenot J, López MB, Hadid A. A survey on computer vision for assistive medical diagnosis from faces. IEEE journal of biomedical and health informatics. 2017;22(5):1497–1511.

[24] Guan V, Zhou C, Wan H, et al. A novel mobile app for personalised dietary advice leveraging persuasive technology, computer vision, and cloud computing: development and usability study. JMIR Formative Research. 2023;7:e46839.

[25] Reifs D, Casanova-Lozano L, Reig-Bolaño R, Grau-Carrion S. Clinical validation of computer vision and artificial intelligence algorithms for wound measurement and tissue classification in wound care. Inform Med Unlocked. 2023;37:101185.

[26] Zestas ON. A proposed computer-vision based upper-limb rehabilitation & evaluation suite. In: Proceedings of the 2nd International Conference of the ACM Greek SIGCHI Chapter. 2023, September:1–5.

[27] Robertson FC, Raahil MS, Amich JM, et al. Frameless neuronavigation with computer vision and real-time tracking for bedside external ventricular drain placement: a cadaveric study. J Neurosurg. 2021;136(5):1475–1484.

[28] Sathesh A. Computer vision on IOT-based patient preference management system. Journal of Trends in Computer Science and Smart Technology. 2020;2(2):68–77.

[29] Mehta, P., Singh, M. K., & Singha, N. S. Computer vision (CV)-based machine learning (ML) models for the healthcare system. In Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem (pp. 37-47). CRC Press.

[30] Elyan E, Vuttipittayamongkol P, Johnston P, et al. Computer vision and machine learning for medical image analysis: recent advances, challenges, and way forward. Artificial Intelligence Surgery. 2022;2.

[31] Hsu FR, Kuo YH, Wei SY, Hsieh YH, Nguyen DC. A study of user interface with wearable devices based on computer vision. IEEE Consumer Electronics Magazine. 2019;9(1):43–48.

[32] Ward TM, Mascagni P, Ban Y, et al. Computer vision in surgery. Surgery. 2021;169(5):1253–1256.

[33] Shahin M, Chen FF, Hosseinzadeh A, Koodiani HK, Shahin A, Nafi OA. A smartphone-based application for an early skin disease prognosis: towards a lean healthcare system via computer-based vision. Adv Eng Inf. 2023;57:102036.

[34] Costa T, Coelho L, Silva MF. Integrating computer vision, robotics, and artificial intelligence for healthcare: an application case for diabetic foot management. In: Exploring the Convergence of Computer and Medical Science through Cloud Healthcare. IGI Global;2023:134–162.

[35] Al-Faris M, Chiverton J, Ndzi D, Ahmed AI. A review of computer vision-based methods for human action recognition. Journal of Imaging. 2020;6(6):46.

[36] Mascagni P, Alapatt D, Sestini L, et al. Computer vision in surgery: from potential to clinical value. npj Digital Medicine. 2022;5(1):163.

[37] Olveres J, González G, Torres F, et al. What is new in computer vision and artificial intelligence in medical image analysis applications? Quant Imag Med Surg. 2021;11(8):3830.

[38] Noor MBT, Zenia NZ, Kaiser MS. Challenges ahead in healthcare applications for vision and sensors. In: Vision, Sensing and Analytics: Integrative Approaches. Cham: Springer International Publishing;2021:397–413.

[39] Li R, St George RJ, Wang X, et al. Moving towards intelligent telemedicine: computer vision measurement of human movement. Comput Biol Med. 2022;147:105776.

[40] Muhammad K, Khan S, Kumar N, Del Ser J, Mirjalili S. Vision-based personalised wireless capsule endoscopy for smart healthcare: taxonomy, literature review, opportunities and challenges. Future Generat Comput Syst. 2020;113:266–280.

[41] Vamsikrishna KM, Dogra DP, Desarkar MS. Computer-vision-assisted palm rehabilitation with supervised learning. IEEE (Inst Electr Electron Eng) Trans Biomed Eng. 2015;63(5):991–1001.

[42] Angelica C, Purnama H, Purnomo F. Impact of computer vision with deep learning approach in medical imaging diagnosis. 2021 1st International Conference on Computer Science and Artificial Intelligence (ICCSAI). 2021, October;1:37–41. IEEE.

[43] Khang A, Abdullayev V, Hrybiuk O, Shukla AK, eds. Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press;2024.

[44] Sager C, Janiesch C, Zschech P. A survey of image labelling for computer vision applications. Journal of Business Analytics. 2021;4(2):91–110.

[45] Shafik W, Hidayatullah AF, Kalinaki K, Aslam MM. Artificial intelligence (AI)-Assisted computer vision (CV) in healthcare systems. In: Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press;2024:17–36.

[46] Do QT, Chaudri J. Creating computer vision models for respiratory status detection. In: 2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). IEEE;2022, July:1350–1353.

[47] Singh C, Bhavani GL, Maginmani UH, Singh R, Kumar AS, Rebelli S. A WSN-based, intelligent medical IoT systems integrating computer vision platform. In: 2023 3rd International Conference on Advance Computing and Innovative Technologies in Engineering (ICACITE). IEEE;2023, May:1234–1239.

[48] Pitale R, Kale H, Kshirsagar S, Rajput H. A schematic review on applications of deep learning and computer vision. 2021 Asian Conference on Innovation in Technology (ASIANCON). IEEE;2021, August:1–6.

[49] Ravankar A, Rawankar A, Ravankar AA. Real-time monitoring of elderly people through computer vision. Artif Life Robot. 2023;28(3):496–501.

[50] Carpenter M, Carpenter D, Jangid VK, Sharma L. Computer vision in surgical operating theatre and medical imaging. In: Computer Vision and Internet of Things. Chapman and Hall/CRC;2022:75–96.

[51] Gumbs AA, Grasso V, Bourdel N, et al. The advances in computer vision that are enabling more autonomous actions in surgery: a systematic review of the literature. Sensors. 2022;22(13):4918.

[52] Vora P, Shrestha S. Detecting diabetic retinopathy using embedded computer vision. Appl Sci. 2020;10(20):7274.

[53] Parvaiz A, Khalid MA, Zafar R, Ameer H, Ali M, Fraz MM. Vision transformers in medical computer vision—a contemplative retrospection. Eng Appl Artif Intell. 2023;122:106126.

[54] Zainuddin AA, Superamaniam S, Andrew AC, et al. Patient monitoring system using computer vision for emotional recognition and vital signs detection. 2020 IEEE Student Conference on Research and Development (SCOReD). IEEE;2020, September:22–27.

[55] Bharti RK, Khan MA, Narayanamurthy R, Biswas P, Sharma H. Future of computer vision application in digital healthcare. In: Healthcare Industry 4.0. CRC Press;2023:139–150.

[56] Shukla S, Lakhmani A, Agarwal AK. Approaches of artificial intelligence in biomedical image processing: a leading tool between computer vision & biological vision. 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA)(Spring). IEEE;2016, April:1–6.

[57] Livingstone D, Chau J. Otoscopic diagnosis using computer vision: an automated machine learning approach. Laryngoscope. 2020;130(6):1408–1413.

[58] Dong CZ, Catbas FN. A review of computer vision-based structural health monitoring at local and global levels. Struct Health Monit. 2021;20(2):692–743.

[59] Giaquinto N, Scarpetta M, Ragolia MA, Pappalardi P. Real-time drip infusion monitoring through a computer vision system. In: 2020 IEEE International Symposium on Medical Measurements and Applications (MeMeA). IEEE;2020, June:1–5.

[60] Dias RD, Kennedy-Metz LR, Yule SJ, Gombolay M, Zenati MA. Assessing Team Situational Awareness in the Operating Room via Computer Vision. IEEE: IEEE Conference on Cognitive and Computational Aspects of Situation Management (CogSIMA);2022, June:94–96, 2022.

[61] Huang KT, Silva MA, See AP, et al. A computer vision approach to identifying the manufacturer and model of anterior cervical spinal hardware. J Neurosurg Spine. 2019;31(6):844–850.

[62] Mayrose H, Niranjana S, Bairy GM, Edwankar H, Belurkar S, Saravu K. Computer vision approach for the detection of thrombocytopenia from microscopic blood smear images. In: 2021 IEEE International Conference on Electronics, Computing and Communication Technologies (CONECCT). IEEE;2021, July:1–5.

[63] Arslan M, Haider A, Khurshid M, et al. From pixels to pathology: employing computer vision to decode chest diseases in medical images. Cureus. 2023;15(9).

[64] Khanal SR, Paulino D, Sampaio J, Barroso J, Reis A, Filipe V. A review on computer vision technology for physical exercise monitoring. Algorithms. 2022;15(12):444.

[65] Bruno A, Caudai C, Leone GR, Martinelli M, Moroni D, Crotti F. Medical Waste Sorting: a computer vision approach for assisted primary sorting. In: 2023 IEEE International Conference on Acoustics, Speech, and Signal Processing Workshops (ICASSPW). IEEE;2023, June:1–5.

[66] Tokayev KJ, Iqbal K, Younis FA. Clinical decision support systems using NLP and computer vision and their integration in healthcare. International Journal of Applied Health Care Analytics. 2023;8(6):1–17.

[67] Khan YF, Kaushik B. Computer vision technique for neuro-image analysis in neurodegenerative diseases: a survey. In: 2020 International Conference on Emerging Smart Computing and Informatics (ESCI). IEEE;2020, March:346–350.

[68] Rieke N, Tombari F, Navab N. Computer vision and machine learning for surgical instrument tracking: focus: random forest-based microsurgical tool tracking. In: Computer Vision for Assistive Healthcare. Academic Press;2018:105–126.

[69] Palenychka R, Lakhssassi A, Palenychka M. Verification of medication dispensing using the attentive computer vision approach. In: 2018 IEEE International Symposium on Circuits and Systems (ISCAS). IEEE;2018, May:1–4.

[70] Giaquinto N, Scarpetta M, Spadavecchia M, Andria G. Deep learning-based computer vision for real-time intravenous drip infusion monitoring. IEEE Sensor J. 2020;21(13):14148–14154.

[71] Osztrogonacz P, Chinnadurai P, Lumsden AB. Emerging applications for computer vision and artificial intelligence in management of the cardiovascular patient. Methodist DeBakey Cardiovascular Journal. 2023;19(4):17.

[72] Mustafa Z, Nsour H. Using computer vision techniques to automatically detect abnormalities in chest X-rays. Diagnostics. 2023;13(18):2979.

[73] Saxena R, Sharma V, Gupta M. Ethical and Social Consequences of Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. 2023 International Conference On Advances In Computation, Communication And Information Technology (ICAICCIT). IEEE;2023, November:1018–1023.

[74] Mehta O, Liao Z, Jenkinson M, Carneiro G, Verjans J. Machine learning in medical imaging–clinical applications and challenges in computer vision. Artif Intell Med: Applications, Limitations and Future Directions. 2022:79–99.

[75] Froese L, Dian J, Batson C, et al. Computer vision for continuous bedside pharmacological data extraction: a novel application of artificial intelligence for clinical data recording and biomedical research. Frontiers in Big Data. 2021;4:689358.

[76] Glancova A, Do QT, Sanghavi DK, et al. Are we ready for video recognition and computer vision in the intensive care unit? A survey. Appl Clin Inf. 2021;12(1):120–132.

[77] Fialho R, Moreira R, Santos TC, et al. Can computer vision be used for anthropometry? A feasibility study of a smart mobile application. 2021 IEEE 34th International Symposium on Computer-Based Medical Systems (CBMS). IEEE;2021, June:119–124.

[78] Jain PK, Jain SK. Computer vision-based remote care of microbiological data analysis. In: Microbial Data Intelligence and Computational Techniques for Sustainable Computing. Singapore: Springer Nature Singapore;2024:49–57.

[79] Vuppalapati JS, Kedari S, Kedari S, Vuppalapati R, Vuppalapati C, Ilapakurti A. The role of selfies in creating the next generation computer vision infused outpatient data driven electronic health records (EHR). 2018 IEEE International Conference on Big Data (Big Data). IEEE;2018, December:2458–2466.

[80] Sikka K, Ahmed AA, Diaz D, et al. Automated assessment of children’s postoperative pain using computer vision. Pediatrics. 2015;136(1):e124–e131.

[81] Hassannejad H, Matrella G, Ciampolini P, De Munari I, Mordonini M, Cagnoni S. Automatic diet monitoring: a review of computer vision and wearable sensor-based methods. Int J Food Sci Nutr. 2017;68(6):656–670.

[82] Levin I, Rapoport FJ, Bar O, Cohen A, Asselmann D, Wolf T. Calculating the burden of time outside the body in endoscopic surgery using a novel computer-vision-based surgical intelligence platform. J Minim Invasive Gynecol. 2023;30(11):S2.

[83] Debnath B, O’Brien M, Yamaguchi M, Behera A. A review of computer vision-based approaches for physical rehabilitation and assessment. Multimed Syst. 2022;28(1):209–239.

[84] Al-Naji A, Mahmood MF, Fakhri AB, Chahl J. Computer vision for non-contact blood pressure (BP): preliminary results. AIP Conf Proc. 2023, September;2804(1). AIP Publishing.

[85] Zestas ON, Soumis DN, Kyriakou KD, Seklou K, Tselikas ND. A computer-vision based hand rehabilitation assessment suite. AEU-International Journal of Electronics and Communications. 2023;169:154762.

[86] Shrotriya N, Thakore P. Computer vision (CV)-Aided medical diagnosis for cardiovascular disease detection. Computer Vision and AI-Integrated IoT Technologies in the Medical Ecosystem. CRC Press;2024:48–67.

[87] Shukla AK, ed. Computer vision and AI-integrated IoT technologies in the medical. Neural Network. 2023;1:1, 1.

[88] Secco EL, McHugh DD, Buckley N. A CNN-Based Computer Vision Interface for Prosthetics’ Control. International Conference On Wireless Mobile Communication And Healthcare. Cham: Springer International Publishing;2021, November:41–59.

[89] Srilakshmi V, Kiran GU, Mounika M, et al. Evolving convolutional neural networks with meta-heuristics for transfer learning in computer vision. Procedia Comput Sci. 2023;230:658–668.

[90] Praneetha D, Pandala ML, Sai GN. Computer vision-based deep learning for comprehensive skin cancer detection: precise diagnosis and early intervention in dermatology. In:2023 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES). IEEE;2023, December:1–8.

[91] Lu CP, Wu TK, Liang CK, Hsu YH, Yang T. Development of brain MRI image segmentation methods based on computer vision and deep learning. In: 2023 12th International Conference on Awareness Science and Technology (iCAST). IEEE;2023, November:59–65.

[92] Luo Z. The application and development of image recognition technology in the field of computer vision. Highlights in Science, Engineering and Technology. 2024;85:909–914.

[93] Sharma S, Ratha SK. Computer vision syndrome and its risk factors among medical students of a tertiary care centre in Odisha: a cross-sectional study. International Journal of Health Systems and Implementation Research. 2021;5(1):42–49.

[94] Xueyi ZHANG, Bo SUN, Guilin CHEN, Ge HU, Qing LI, Guixing QIU. Computer vision in medical imaging and its impact on the rare musculoskeletal diseases. Journal of Rare Diseases. 2023;2(4):589–595.

[95] Francisco JA, Rodrigues PS. Computer vision based on a modular neural network for automatic assessment of physical therapy rehabilitation activities. IEEE Trans Neural Syst Rehabil Eng. 2022.

[96] Khanam FTZ, Al-Naji A, Chahl J. Remote monitoring of vital signs in diverse non-clinical and clinical scenarios using computer vision systems: a review. Appl Sci. 2019;9(20):4474.

[97] Ouhami M, Hafiane A, Es-Saady Y, El Hajji M, Canals R. Computer vision, IoT and data fusion for crop disease detection using machine learning: a survey and ongoing research. Rem Sens. 2021;13(13):2486.

[98] Hwang Y, Shin D, Eun J, Suh B, Lee J. Design guidelines of a computer-based intervention for computer vision syndrome: focus group study and real-world deployment. J Med Internet Res. 2021;23(3):e22099.

[99] Li X, Fan F, Chen X, et al. Computer vision for brain disorders based primarily on ocular responses. Front Neurol. 2021;12:584270.

[100] Xu H, Han T, Wang H, et al. Detection of blood stains using computer vision-based algorithms and their association with postoperative outcomes in thoracoscopic lobectomies. Eur J Cardio Thorac Surg. 2022;62(5):ezaC154.

[101] Erno J, Gomes T, Baltimore C, Lineberger JP, Smith DH, Baker GH. Automated identification of patent ductus arteriosus using a computer vision model. J Ultrasound Med. 2023;42(12):2707–2713.

[102] Al Tawil L, Aldokhayel S, Zeitouni L, Qadoumi T, Hussein S, Ahamed SS. Prevalence of self-reported computer vision syndrome symptoms and its associated factors among university students. Eur J Ophthalmol. 2020;30(1):189–195.

[103] Blasco-García JD, García-López G, Jiménez-Muñoz M, et al. A computer vision-based system to help health professionals to apply tests for fall risk assessment. Sensors. 2024;24(6):2015.

[104] Talaa S, El Fezazi M, Jilbab A, El yousfi Alaoui MH. Computer vision-based approach for automated monitoring and assessment of gait rehabilitation at home. International Journal of Online & Biomedical Engineering. 2023;19(18).

[105] Long L, Sun J, Jiang L, et al. MRI-based traditional radiomics and computer-vision nomogram for predicting lymphovascular space invasion in endometrial carcinoma. Diagnostic and interventional imaging. 2021;102(7–8):455–462.

[106] Qiang Y, Xu M, Pochron MP, Jupelli M, Dao M. A framework of computer vision-enhanced microfluidic approach for automated assessment of the transient sickling kinetics in sickle red blood cells. Frontiers in Physics. 2024;12:1331047.

[107] Barrett T, Chen Q, Zhang A. Skin deep: investigating subjectivity in skin tone annotations for computer vision benchmark datasets. In: Proceedings of the 2023 ACM Conference on Fairness, Accountability, and Transparency. 2023, June:1757–1771.

[108] Constable MD, Shum HP, Clark S. Enhancing surgical performance in cardiothoracic surgery with innovations from computer vision and artificial intelligence: a narrative review. J Cardiothorac Surg. 2024;19(1):94.

RIGHTS & PERMISSIONS

2024 2024 The Authors. Publishing services by Elsevier B.V. on behalf of KeAi Communications Co. Ltd.