PreprintCited by 5

Artificial Intelligence Application for Thoracic Surgeon: “Phe-Nomenal Cosmic Powers into the Magic Lamp”

Cusumano, Giacomo, D'arrigo, Stefano, Terminella, Alberto, Lococo, Filippo·2024

Preprint — published as 10.3390/jcm13133750

Abstract

In the digital age, Artificial Intelligence (AI) is emerging as a transformative force in various sectors, in-cluding medicine. This article explores the potential of AI, akin to the magical genie of Aladdin's lamp, particularly within thoracic surgery and lung cancer management. It examines AI applications like ma-chine learning and deep learning in achieving more precise diagnoses, preoperative risk assessment, and improved surgical outcomes. The challenges and advancements in AI integration, especially in computer vision and multi-modal models, are discussed alongside their impact on robotic surgery and operating room management. Despite its transformative potential, implementing AI in medicine faces challenges regarding data scarcity, interpretability issues, and ethical concerns. Collaboration between AI and medi-cal communities is essential to address these challenges and unlock the full potential of AI in revolution-izing clinical practice. This article underscores the importance of further research and interdisciplinary collaboration to ensure the safe and effective deployment of AI in real-world clinical settings.

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  • Intraoperative AI applications such as augmented reality, real-time image-guided navigation, and robotic-assisted thoracic surgery have demonstrated increased surgical precision, reduced operative times, and enhanced safety.

    “Intraoperatively, AI applications such as augmented reality (AR), real-time image-guided navigation, and robotic-assisted thoracic surgery (RATS) have demonstrated increased surgical precision, reduced operative times, and enhanced safety [7,8,9].”

    From Artificial Intelligence in Oncologic Thoracic Surgery: Clinical Decision Support and Emerging Applications (2026) · cites this paper as [8]

    The Operating Room section says AI can enhance precision and safety in robotic-assisted surgery, but §6, “The Third Wish” describes these benefits as largely prospective. That section also says robotic-assisted surgery does not yet incorporate AI-based technology and is associated with prolonged—not reduced—procedural times. The cited passages do not establish the claimed benefits for augmented reality or real-time image-guided navigation. (LLM, conf=0.90)

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Research works printed but not linked: 49

  1. Rajpurkar, P.; Chen, E.; Banerjee, O.; Topol, E.J. Ai in health and medicine. Nat. Med. 2022, 28, 31-38. [CrossRef] [PubMed] · DOI · Paper page
  2. Floridi, L.; Chiriatti, M. Gpt-3: Its nature, scope, limits, and consequences. Minds Mach. 2020, 30, 681-694. [CrossRef] · DOI · Paper page
  3. Touvron, H.; Lavril, T.; Izacard, G.; Martinet, X.; Lachaux, M.; Lacroix, T.; Rozière, B.; Goyal, N.; Hambro, E.; Azhar, F.; et al. Llama: Open and efficient foundation language models. arXiv 2023, arXiv:2302.13971. · arXiv
  4. Thirunavukarasu, A.J.; Shu Jeng Ting, D.; Elangovan, K.; Gutierrez, L.; Fang Tan, T.; Shu Wei Ting, D. Large language models in medicine. Nat. Med. 2023, 29, 1930-1940. [CrossRef] [PubMed] · DOI · Paper page
  5. Sallam, M. Chatgpt utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare 2023, 11, 887. [CrossRef] [PubMed] · DOI · Paper page
  6. Harrer, S. Attention is not all you need: The complicated case of ethically using large language models in healthcare and medicine. EBioMedicine 2023, 90, 104512. [CrossRef] [PubMed] · DOI · Paper page
  7. Clusmann, J.; Kolbinger, F.R.; Muti, H.S.; Carrero, Z.I.; Eckardt, J.N.; Laleh, N.G.; Löffler, C.M.L.; Schwarzkopf, S.C.; Unger, M.; Veldhuizen, G.P.; et al. The future landscape of large language models in medicine. Commun. Med. 2023, 3, 141. [CrossRef] [PubMed] · DOI · Paper page
  8. Jin, Q.; Dhingra, B.; Liu, Z.; Cohen, W.W.; Lu, X. Pubmedqa: A dataset for biomedical research question answering. arXiv 2019, arXiv:1909.06146. · arXiv
  9. Pal, A.; Kumar Umapathi, L.; Sankarasubbu, M. Medmcqa: A large-scale multi-subject multi-choice dataset for medical domain question answering. In Proceedings of the Conference on Health, Inference, and Learning, PMLR, Virtual, 7-8 April 2022; Volume 174, pp. 248-260.
  10. Jin, D.; Pan, E.; Oufattole, N.; Weng, W.-H.; Fang, H.; Szolovits, P. What Disease Does This Patient Have? A Large-Scale Open Domain Question Answering Dataset from Medical Exams. Appl. Sci. 2021, 11, 6421. [CrossRef] · DOI · Paper page
  11. Singhal, K.; Azizi, S.; Tu, T.; Mahdavi, S.S.; Wei, J.; Chung, H.W.; Scales, N.; Tanwani, A.; Cole-Lewis, H.; Pfohl, S.; et al. Large language models encode clinical knowledge. Nature 2023, 620, 172-180. [CrossRef] · DOI · Paper page
  12. Rajpurkar, P.; Lungren, M.P. The current and future state of ai interpretation of medical images. N. Engl. J. Med. 2023, 388, 1981-1990. [CrossRef] · DOI · Paper page
  13. Najjar, R. Redefining radiology: A review of artificial intelligence integration in medical imaging. Diagnostics 2023, 13, 2760. [CrossRef] [PubMed] · DOI
  14. Noble, W.S. What is a support vector machine? Nat. Biotechnol. 2006, 24, 1565-1567. [CrossRef] · DOI
  15. Elyan, E.; Gaber, M.M. A genetic algorithm approach to optimising random forests applied to class engineered data. Inf. Sci. 2017, 384, 220-234. [CrossRef] · DOI
  16. Acosta, J.N.; Falcone, G.J.; Rajpurkar, P.; Topol, E.J. Multimodal biomedical AI. Nat. Med. 2022, 28, 1773-1784. [CrossRef] [PubMed] · DOI · Paper page
  17. Gu, J.; Wang, Z.; Kuen, J.; Ma, L.; Shahroudy, A.; Shuai, B.; Liu, T.; Wang, X.; Wang, G.; Cai, J.; et al. Recent advances in convolutional neural networks. Pattern Recognit. 2018, 77, 354-377. [CrossRef] · DOI · Paper page
  18. Liu, Z.; Wang, S.; Dong, D.; Wei, J.; Fang, C.; Zhou, X.; Sun, K.; Li, L.; Li, B.; Wang, M.; et al. The Applications of Radiomics in Precision Diagnosis and Treatment of Oncology: Opportunities and Challenges. Theranostics 2019, 9, 1303-1322. [CrossRef] · DOI
  19. Lambin, P.; Leijenaar, R.T.H.; Deist, T.M.; Peerlings, J.; de Jong, E.E.C.; van Timmeren, J.; Sanduleanu, S.; Larue, R.T.H.M.; Even, A.J.G.; Jochems, A.; et al. Radiomics: The bridge between medical imaging and personalized medicine. Nat. Rev. Clin. Oncol. 2017, 14, 749-762. [CrossRef] · DOI · Paper page
  20. Qian, Z.; Li, Y.; Wang, Y.; Li, L.; Li, R.; Wang, K.; Li, S.; Tang, K.; Zhang, C.; Fan, X.; et al. Differentiation of glioblastoma from solitary brain metastases using radiomic machine-learning classifiers. Cancer Lett. 2019, 451, 128-135. [CrossRef] [PubMed] · DOI
  21. Vaswani, A.; Shazeer, N.; Parmar, N.; Uszkoreit, J.; Jones, L.; Gomez, A.N.; Kaiser, L.; Polosukhin, I. Attention is all you need. In Proceedings of the Advances in Neural Information Processing Systems, Long Beach, CA, USA, 4-9 December 2017; Volume 30.
  22. Dosovitskiy, A.; Beyer, L.; Kolesnikov, A.; Weissenborn, D.; Zhai, X.; Unterthiner, T.; Dehghani, M.; Minderer, M.; Heigold, G.; Gelly, S.; et al. An image is worth 16x16 words: Transformers for image recognition at scale. In Proceedings of the International Conference on Learning Representations, Virtual, 3-7 May 2021.
  23. Li, C.; Li, L.; Geng, Y.; Jiang, H.; Cheng, M.; Zhang, B.; Ke, Z.; Xu, X.; Chu, X. Yolov6 v3.0: A full-scale reloading. arXiv 2023, arXiv:2301.05586. · arXiv
  24. Cheng, B.; Misra, I.; Schwing, A.G.; Kirillov, A.; Girdhar, R. Masked-attention mask transformer for universal image segmentation. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, New Orleans, LA, USA, 18-24 June 2022; Available online: https://ieeexplore.ieee.org/xpl/conhome/9878378/proceeding (accessed on 15 June 2022).
  25. Tu, T.; Azizi, S.; Driess, D.; Schaekermann, M.; Amin, M.; Chang, P.; Carroll, A.; Lau, C.; Tanno, R.; Ktena, I. Towards generalist biomedical ai. NEJM AI 2024, 1, AIoa2300138. [CrossRef] · DOI · Paper page
  26. Abraham, J. Reduced lung cancer mortality with low-dose computed tomographic screening. Community Oncol. 2011, 8, 441-444. [CrossRef] · DOI
  27. Kakeda, S.; Moriya, J.; Sato, H.; Aoki, T.; Watanabe, H.; Nakata, H.; Oda, N.; Katsuragawa, S.; Yamamoto, K.; Doi, K. Improved detection of lung nodules on chest radiographs using a commercial computer-aided diagnosis system. AJR Am. J. Roentgenol. 2004, 182, 505-510. [CrossRef] [PubMed] · DOI
  28. Naqi, S.M.; Sharif, M.; Yasmin, M. Multistage segmentation model and SVM-ensemble for precise lung nodule detection. Int. J. Comput. Assist. Radiol. Surg. 2018, 13, 1083-1095. [CrossRef] [PubMed] · DOI
  29. Choi, W.; Oh, J.H.; Riyahi, S.; Liu, C.J.; Jiang, F.; Chen, W.; White, C.; Rimner, A.; Mechalakos, J.G.; Deasy, J.O.; et al. Radiomics analysis of pulmonary nodules in low-dose CT for early detection of lung cancer. Med. Phys. 2018, 45, 1537-1549. [CrossRef] [PubMed] · DOI
  30. Roos, J.E.; Paik, D.; Olsen, D.; Liu, E.G.; Chow, L.C.; Leung, A.N.; Mindelzun, R.; Choudhury, K.R.; Naidich, D.P.; Napel, S.; et al. Computer-aided detection (CAD) of lung nodules in CT scans: Radiologist performance and reading time with incremental CAD assistance. Eur. Radiol. 2010, 20, 549-557. [CrossRef] [PubMed] · DOI
  31. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. Commun. ACM 2017, 60, 84-90. [CrossRef] · DOI · Paper page
  32. Tran, G.S.; Nghiem, T.P.; Nguyen, V.T.; Luong, C.M.; Burie, J.C. Improving Accuracy of Lung Nodule Classification Using Deep Learning with Focal Loss. J. Health Eng. 2019, 2019, 5156416. [CrossRef] [PubMed] · DOI
  33. Nibali, A.; He, Z.; Wollersheim, D. Pulmonary nodule classification with deep residual networks. Int. J. Comput. Assist. Radiol. Surg. 2017, 12, 1799-1808. [CrossRef] · DOI
  34. Murphy, A.; Skalski, M.; Gaillard, F. The utilisation of convolutional neural networks in detecting pulmonary nodules: A review. Br. J. Radiol. 2018, 91, 20180028. [CrossRef] · DOI
  35. Bellini, V.; Valente, M.; Del Rio, P.; Bignami, E. Artificial intelligence in thoracic surgery: A narrative review. J. Thorac. Dis. 2021, 13, 6963-6975. [CrossRef] [PubMed] · DOI · Paper page
  36. Bonde, A.; Varadarajan, K.M.; Bonde, N.; Troelsen, A.; Muratoglu, O.K.; Malchau, H.; Yang, A.D.; Alam, H.; Sillesen, M. Assessing the utility of deep neural networks in predicting postoperative surgical complications: A retrospective study. Lancet Digit. Health 2021, 3, e471-e485. [CrossRef] [PubMed] · DOI
  37. Bolourani, S.; Wang, P.; Patel, V.M.; Manetta, F.; Lee, P.C. Predicting respiratory failure after pulmonary lobectomy using machine learning techniques. Surgery 2020, 168, 743-752. [CrossRef] [PubMed] · DOI
  38. Esteva, H.; Marchevsky, A.; Núñez, T.; Luna, C.; Esteva, M. Neural networks as a prognostic tool of surgical risk in lung resections. Ann. Thorac. Surg. 2002, 73, 1576-1581. [CrossRef] · DOI · Paper page
  39. Santos-Garcıa, G.; Varela, G.; Novoa, N.; Jiménez, M.F. Prediction of postoperative morbidity after lung resection using an artificial neural network ensemble. Artif. Intell. Med. 2004, 30, 61-69. [CrossRef] · DOI · Paper page
  40. Salati, M.; Migliorelli, L.; Moccia, S.; Andolfi, M.; Roncon, A.; Guiducci, G.M.; Xiumè, F.; Tiberi, M.; Frontoni, E.; Refai, M. A Machine Learning Approach for Postoperative Outcome Prediction: Surgical Data Science Application in a Thoracic Surgery Setting. World J. Surg. 2021, 45, 1585-1594. [CrossRef] [PubMed] · DOI
  41. Altorki, N.; Wang, X.; Kozono, D.; Watt, C.; Landrenau, R.; Wigle, D.; Port, J.; Jones, D.R.; Conti, M.; Ashrafi, A.S.; et al. Lobar or Sublobar Resection for Peripheral Stage IA Non-Small-Cell Lung Cancer. N. Engl. J. Med. 2023, 388, 489-498. [CrossRef] · DOI
  42. Sadeghi, A.H.; Maat, A.P.W.M.; Taverne, Y.J.H.J.; Cornelissen, R.; Dingemans, A.C.; Bogers, A.J.J.C.; Mahtab, E.A.F. Virtual reality and artificial intelligence for 3-dimensional planning of lung segmentectomies. JTCVS Tech. 2021, 7, 309-321. [CrossRef] · DOI · Paper page
  43. Kassahun, Y.; Yu, B.; Tibebu, A.T.; Stoyanov, D.; Giannarou, S.; Metzen, J.H.; Vander Poorten, E. Surgical robotics beyond enhanced dexterity instrumentation: A survey of machine learning techniques and their role in intelligent and autonomous surgical actions. Int. J. Comput. Assist. Radiol. Surg. 2016, 11, 553-568. [CrossRef] · DOI · Paper page
  44. Gumbs, A.A.; Frigerio, I.; Spolverato, G.; Croner, R.; Illanes, A.; Chouillard, E.; Elyan, E. Artificial Intelligence Surgery: How Do We Get to Autonomous Actions in Surgery? Sensors 2021, 21, 5526. [CrossRef] · DOI · Paper page
  45. Marcus, H.J.; Payne, C.J.; Hughes-Hallett, A.; Gras, G.; Leibrandt, K.; Nandi, D.; Yang, G.Z. Making the Leap: The Translation of Innovative Surgical Devices from the Laboratory to the Operating Room. Ann. Surg. 2016, 263, 1077-1078. [CrossRef] · DOI
  46. Domingues, I.; Pereira, G.; Martins, P.; Duarte, H.; Santos, J.; Abreu, P.H. Using deep learning techniques in medical imaging: A systematic review of applications on CT and PET. Artif. Intell. Rev. 2020, 53, 4093-4160. [CrossRef] · DOI
  47. Halder, A.; Chatterjee, S.; Dey, D. Adaptive morphology aided 2-pathway convolutional neural network for lung nodule classification. Biomed. Signal Process. Control 2022, 72, 103347. [CrossRef] · DOI
  48. Mellia, J.A.; Basta, M.N.; Toyoda, Y.; Othman, S.; Elfanagely, O.; Morris, M.P.; Torre-Healy, L.; Ungar, L.H.; Fischer, J.P. Natural Language Processing in Surgery: A Systematic Review and Meta-analysis. Ann. Surg. 2021, 273, 900-908. [CrossRef] · DOI
  49. Stahl, C.C.; Jung, S.A.; Rosser, A.A.; Kraut, A.S.; Schnapp, B.H.; Westergaard, M.; Hamedani, A.G.; Minter, R.M.; Greenberg, J.A. Natural language processing and entrustable professional activity text feedback in surgery: A machine learning model of resident autonomy. Am. J. Surg. 2021, 221, 369-375. [CrossRef] · DOI

Authors
Cusumano, G., D'arrigo, S., Terminella, A., & Lococo, F.
Publisher
MDPI AG
Published
2024-05-13
Cited by
5
References
0
Type
Preprint

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