Feijóo C, Kwon Y, Bauer JM, et al. Harnessing artificial intelligence (AI) to increase wellbeing for all: the case for a new technology diplomacy. Telecommunications Policy. 2020;44(6):101988.

Article  PubMed  PubMed Central  Google Scholar 

Diaz-Flores E, Meyer T, Giorkallos A. Evolution of artificial intelligence-powered technologies in biomedical research and healthcare. Adv Biochem Eng Biotechnol. 2022;182:23–60.

CAS  PubMed  Google Scholar 

Asai A, Konno M, Taniguchi M, Vecchione A, Ishii H. Computational healthcare: present and future perspectives (Review). Exp Ther Med. 2021;22(6):1351.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Rajesh A, Asaad M. Artificial intelligence in surgery: a revolution in progress. Am Surg. 2022;31348221117024.

Hamet P, Tremblay J. Artificial intelligence in medicine. Metabolism. 2017;69:S36-s40.

Article  CAS  Google Scholar 

Mintz Y, Brodie R. Introduction to artificial intelligence in medicine. Minim Invasive Ther Allied Technol. 2019;28(2):73–81.

Article  PubMed  Google Scholar 

Hashimoto DA, Rosman G, Rus D, Meireles OR. Artificial intelligence in surgery: promises and perils. Ann Surg. 2018;268(1):70–6.

Article  PubMed  Google Scholar 

Rimmer L, Howard C, Picca L, Bashir M. The automaton as a surgeon: the future of artificial intelligence in emergency and general surgery. Eur J Trauma Emerg Surg. 2021;47(3):757–62.

Article  PubMed  Google Scholar 

Loftus TJ. Introduction to the artificial intelligence in surgery series. Surgery. 2021;169(4):744–5.

Article  PubMed  Google Scholar 

Moglia A, Georgiou K, Georgiou E, Satava RM, Cuschieri A. A systematic review on artificial intelligence in robot-assisted surgery. Int J Surg. 2021;95:106151.

Article  PubMed  Google Scholar 

Beyaz S. A brief history of artificial intelligence and robotic surgery in orthopedics & traumatology and future expectations. Jt Dis Relat Surg. 2020;31(3):653–5.

PubMed  PubMed Central  Google Scholar 

Ahmad A. Breast cancer statistics: recent trends. Adv Exp Med Biol. 2019;1152:1–7.

Article  CAS  PubMed  Google Scholar 

Sun L, Ang E, Ang WHD, Lopez V. Losing the breast: a meta-synthesis of the impact in women breast cancer survivors. Psychooncology. 2018;27(2):376–85.

Article  PubMed  Google Scholar 

Ośmiałowska E, Misiąg W, Chabowski M, Jankowska-Polańska B. Coping strategies, pain, and quality of life in patients with breast cancer. J Clin Med. 2021;10(19):4469.

Article  PubMed  PubMed Central  Google Scholar 

Yin J, Ngiam KY, Teo HH. Role of artificial intelligence applications in real-life clinical practice: systematic review. J Med Internet Res. 2021;23(4):e25759.

Article  PubMed  PubMed Central  Google Scholar 

Buda M, Saha A, Walsh R, et al. A data set and deep learning algorithm for the detection of masses and architectural distortions in digital breast tomosynthesis images. JAMA Netw Open. 2021;4(8):e2119100.

Article  PubMed  PubMed Central  Google Scholar 

Becker AS, Marcon M, Ghafoor S, Wurnig MC, Frauenfelder T, Boss A. Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest Radiol. 2017;52(7):434–40.

Article  PubMed  Google Scholar 

Soh CL, Shah V, Arjomandi Rad A, et al. Present and future of machine learning in breast surgery: systematic review. Br J Surg. 2022. https://doi.org/10.1093/bjs/znac224.

Article  PubMed  PubMed Central  Google Scholar 

Fu MR, Wang Y, Li C, et al. Machine learning for detection of lymphedema among breast cancer survivors. Mhealth. 2018;4:17.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Myung Y, Jeon S, Heo C, et al. Validating machine learning approaches for prediction of donor related complication in microsurgical breast reconstruction: a retrospective cohort study. Sci Rep. 2021;11(1):5615.

Article  CAS  PubMed  PubMed Central  Google Scholar 

Page MJ, McKenzie JE, Bossuyt PM, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71.

Article  PubMed  PubMed Central  Google Scholar 

Sterne JA, Hernán MA, Reeves BC, et al. ROBINS-I: a tool for assessing risk of bias in non-randomised studies of interventions. BMJ. 2016;355:i4919.

Article  PubMed  PubMed Central  Google Scholar 

Lötsch J, Sipilä R, Dimova V, Kalso E. Machine-learned selection of psychological questionnaire items relevant to the development of persistent pain after breast cancer surgery. Br J Anaesth. 2018;121(5):1123–32.

Article  PubMed  Google Scholar 

Juwara L, Arora N, Gornitsky M, Saha-Chaudhuri P, Velly AM. Identifying predictive factors for neuropathic pain after breast cancer surgery using machine learning. Int J Med Inform. 2020;141: 104170.

Article  PubMed  Google Scholar 

O’Neill AC, Yang D, Roy M, Sebastiampillai S, Hofer SOP, Xu W. Development and evaluation of a machine learning prediction model for flap failure in microvascular breast reconstruction. Ann Surg Oncol. 2020;27(9):3466–75.

Article  PubMed  Google Scholar 

van Egdom LSE, Pusic A, Verhoef C, Hazelzet JA, Koppert LB. Machine learning with PROs in breast cancer surgery; caution: collecting PROs at baseline is crucial. Breast J. 2020;26(6):1213–5.

Article  PubMed  Google Scholar 

Naoum GE, Ho AY, Shui A, et al. Risk of developing breast reconstruction complications: a machine-learning nomogram for individualized risk estimation with and without postmastectomy radiation therapy. Plast Reconstr Surg. 2022;149(1):1e–12e.

Article  CAS  PubMed  Google Scholar 

Pfob A, Mehrara BJ, Nelson JA, Wilkins EG, Pusic AL, Sidey-Gibbons C. Machine learning to predict individual patient-reported outcomes at 2-year follow-up for women undergoing cancer-related mastectomy and breast reconstruction (INSPiRED-001). Breast. 2021;60:111–22.

Article  PubMed  PubMed Central  Google Scholar 

Pfob A, Mehrara BJ, Nelson JA, Wilkins EG, Pusic AL, Sidey-Gibbons C. Towards patient-centered decision-making in breast cancer surgery: machine learning to predict individual patient-reported outcomes at 1-year follow-up. Ann Surg. 2021. https://doi.org/10.1097/SLA.0000000000004862.

Article  PubMed  Google Scholar 

Sidey-Gibbons C, Pfob A, Asaad M, et al. Development of machine learning algorithms for the prediction of financial toxicity in localized breast cancer following surgical treatment. JCO Clin Cancer Inform. 2021;5:338–47.

Article  PubMed  Google Scholar 

Shi Y-C, Li J, Li S-J, et al. Flap failure prediction in microvascular tissue reconstruction using machine learning algorithms. World J Clin Cases. 2022;10(12):3729.

Article  PubMed  PubMed Central  Google Scholar 

Mavioso C, Araujo RJ, Oliveira HP, et al. Automatic detection of perforators for microsurgical reconstruction. Breast. 2020;50:19–24.

Article  PubMed  PubMed Central  Google Scholar 

Eldaly AS, Avila FR, Torres-Guzman RA, et al. Simulation and artificial intelligence in rhinoplasty: a systematic review. Aesthet Plast Surg. 2022. https://doi.org/10.1007/s00266-022-02883-x.

Article  Google Scholar 

Chandawarkar A, Chartier C, Kanevsky J, Cress PE. A practical approach to artificial intelligence in plastic surgery. Aesthet Surg J Open Forum. 2020;2:ojaa001.

Article