Artificial Intelligence Application for Thoracic Surgeon: “Phe-Nomenal Cosmic Powers into the Magic Lamp”
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.
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
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)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
No references could be resolved yet.
Research works printed but not linked: 49
- 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
- Floridi, L.; Chiriatti, M. Gpt-3: Its nature, scope, limits, and consequences. Minds Mach. 2020, 30, 681-694. [CrossRef] · DOI · Paper page
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- Najjar, R. Redefining radiology: A review of artificial intelligence integration in medical imaging. Diagnostics 2023, 13, 2760. [CrossRef] [PubMed] · DOI
- Noble, W.S. What is a support vector machine? Nat. Biotechnol. 2006, 24, 1565-1567. [CrossRef] · DOI
- 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
- 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
- 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
- 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
- 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
- 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
- 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.
- 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.
- 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
- 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).
- 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
- Abraham, J. Reduced lung cancer mortality with low-dose computed tomographic screening. Community Oncol. 2011, 8, 441-444. [CrossRef] · DOI
- 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
- 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
- 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
- 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
- Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. Commun. ACM 2017, 60, 84-90. [CrossRef] · DOI · Paper page
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
Cited by
Sorted by citations
Petrella, F., & Rizzo, S. (2026) · Cancers · doi:10.3390/cancers18020246 · Cited by 10 · see citing works
- Authors
- Cusumano, G., D'arrigo, S., Terminella, A., & Lococo, F.
- ORCID
- Cusumano, Giacomo
- Publisher
- MDPI AG
- Published
- 2024-05-13
- Cited by
- 5
- References
- 0
- Type
- Preprint
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review