Site icon Harmony Thrive

Artificial intelligence in cancer: applications, challenges, and future perspectives | Molecular Cancer

Artificial intelligence in cancer: applications, challenges, and future perspectives | Molecular Cancer
  • Siegel RL, et al. Cancer statistics, 2025. CA Cancer J Clin. 2025;75(1):10–45.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Bray F, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229–63.

    PubMed 

    Google Scholar 

  • Chang T-G, et al. Hallmarks of artificial intelligence contributions to precision oncology. Nat Cancer. 2025;6(3):417–31.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Yates J, Van Allen EM. New horizons at the interface of artificial intelligence and translational cancer research. Cancer Cell. 2025;43(4):708–27.

    CAS 
    PubMed 

    Google Scholar 

  • Wang J, et al. Self-improving generative foundation model for synthetic medical image generation and clinical applications. Nat Med. 2025;31(2):609–17.

    CAS 
    PubMed 

    Google Scholar 

  • Jumper J, et al. Highly accurate protein structure prediction with AlphaFold. Nature. 2021;596(7873):583–9.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Topol EJ. Learning the language of life with AI. Science. 2025;387(6733):eadv4414.

    PubMed 

    Google Scholar 

  • Gong D, et al. Spatial oncology: translating contextual biology to the clinic. Cancer Cell. 2024;42(10):1653–75.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ligero M, et al. Artificial intelligence-based biomarkers for treatment decisions in oncology. Trends Cancer. 2025;11(3):232–44.

    CAS 
    PubMed 

    Google Scholar 

  • Kleppe A, et al. A clinical decision support system optimising adjuvant chemotherapy for colorectal cancers by integrating deep learning and pathological staging markers: a development and validation study. Lancet Oncol. 2022;23(9):1221–32.

    CAS 
    PubMed 

    Google Scholar 

  • Rosenthal JT, Beecy A, Sabuncu MR. Rethinking clinical trials for medical AI with dynamic deployments of adaptive systems. NPJ Digit Med. 2025;8(1):252.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Xu Y, et al. Artificial intelligence: a powerful paradigm for scientific research. Innovation (Camb). 2021;2(4):100179.

    PubMed 

    Google Scholar 

  • Dlamini Z, et al. Artificial intelligence (AI) and big data in cancer and precision oncology. Comput Struct Biotechnol J. 2020;18:2300–11.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sharma A, et al. Advances in AI and machine learning for predictive medicine. J Hum Genet. 2024;69(10):487–97.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Mostavi M, et al. Convolutional neural network models for cancer type prediction based on gene expression. BMC Med Genomics. 2020;13(5):44.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mienye ID, Swart TG, Obaido G. Recurrent neural networks: a comprehensive review of architectures, variants, and applications. Information. 2024;15(9):517.

    Google Scholar 

  • Zhou D, et al. Diagnostic evaluation of a deep learning model for optical diagnosis of colorectal cancer. Nat Commun. 2020;11(1):2961.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kominami Y, et al. Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. Gastrointest Endosc. 2016;83(3):643–9.

    PubMed 

    Google Scholar 

  • Djinbachian R, et al. Autonomous artificial intelligence vs artificial intelligence-assisted human optical diagnosis of colorectal polyps: a randomized controlled trial. Gastroenterology. 2024;167(2):392-399.e2.

    PubMed 

    Google Scholar 

  • McKinney SM, et al. International evaluation of an AI system for breast cancer screening. Nature. 2020;577(7788):89–94.

    CAS 
    PubMed 

    Google Scholar 

  • Lotter W, et al. Robust breast cancer detection in mammography and digital breast tomosynthesis using an annotation-efficient deep learning approach. Nat Med. 2021;27(2):244–9.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Shen Y, et al. Artificial intelligence system reduces false-positive findings in the interpretation of breast ultrasound exams. Nat Commun. 2021;12(1):5645.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Liao J, et al. Artificial intelligence-assisted ultrasound image analysis to discriminate early breast cancer in Chinese population: a retrospective, multicentre, cohort study. EClinMed. 2023;60:102001.

    Google Scholar 

  • Sun S, et al. Deep learning prediction of axillary lymph node status using ultrasound images. Comput Biol Med. 2022;143:105250.

    PubMed 

    Google Scholar 

  • Sandbank J, et al. Validation and real-world clinical application of an artificial intelligence algorithm for breast cancer detection in biopsies. NPJ Breast Cancer. 2022;8(1):129.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang Y, et al. Improved breast cancer histological grading using deep learning. Ann Oncol. 2022;33(1):89–98.

    CAS 
    PubMed 

    Google Scholar 

  • Steiner DF, et al. Impact of deep learning assistance on the histopathologic review of lymph nodes for metastatic breast cancer. Am J Surg Pathol. 2018;42(12):1636–46.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Challa B, et al. Artificial intelligence-aided diagnosis of breast cancer lymph node metastasis on histologic slides in a digital workflow. Mod Pathol. 2023;36(8):100216.

    CAS 
    PubMed 

    Google Scholar 

  • Hwang EJ, et al. Deep learning for detection of pulmonary metastasis on chest radiographs. Radiology. 2021;301(2):455–63.

    PubMed 

    Google Scholar 

  • Nam JG, et al. AI improves nodule detection on chest radiographs in a health screening population: a randomized controlled trial. Radiology. 2023;307(2):e221894.

    PubMed 

    Google Scholar 

  • Ardila D, et al. End-to-end lung cancer screening with three-dimensional deep learning on low-dose chest computed tomography. Nat Med. 2019;25(6):954–61.

    CAS 
    PubMed 

    Google Scholar 

  • Venkadesh KV, et al. Deep learning for malignancy risk estimation of pulmonary nodules detected at low-dose screening CT. Radiology. 2021;300(2):438–47.

    PubMed 

    Google Scholar 

  • Wang C, et al. Data-driven risk stratification and precision management of pulmonary nodules detected on chest computed tomography. Nat Med. 2024;30(11):3184–95.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mazzone PJ, et al. Clinical validation of a cell-free DNA fragmentome assay for augmentation of lung cancer early detection. Cancer Discov. 2024;14(11):2224–42.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Liang N, et al. Ultrasensitive detection of circulating tumour DNA via deep methylation sequencing aided by machine learning. Nat Biomed Eng. 2021;5(6):586–99.

    CAS 
    PubMed 

    Google Scholar 

  • Coudray N, et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat Med. 2018;24(10):1559–67.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Mehta P, et al. AutoProstate: towards automated reporting of prostate MRI for prostate cancer assessment using deep learning. Cancers. 2021;13. https://doi.org/10.3390/cancers13236138.

  • Hamm CA, et al. Interactive explainable deep learning model informs prostate cancer diagnosis at MRI. Radiology. 2023;307(4):e222276.

    PubMed 

    Google Scholar 

  • Saha A, et al. Artificial intelligence and radiologists in prostate cancer detection on MRI (PI-CAI): an international, paired, non-inferiority, confirmatory study. Lancet Oncol. 2024;25(7):879–87.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bulten W, et al. Artificial intelligence for diagnosis and Gleason grading of prostate cancer: the PANDA challenge. Nat Med. 2022;28(1):154–63.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bulten W, et al. Automated deep-learning system for Gleason grading of prostate cancer using biopsies: a diagnostic study. Lancet Oncol. 2020;21(2):233–41.

    PubMed 

    Google Scholar 

  • Cho H-H, et al. Classification of the glioma grading using radiomics analysis. PeerJ. 2018;6:e5982.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Park YW, et al. Robust performance of deep learning for automatic detection and segmentation of brain metastases using three-dimensional black-blood and three-dimensional gradient echo imaging. Eur Radiol. 2021;31(9):6686–95.

    PubMed 

    Google Scholar 

  • Yan J, et al. Predicting 1p/19q co-deletion status from magnetic resonance imaging using deep learning in adult-type diffuse lower-grade gliomas: a discovery and validation study. Lab Invest. 2022;102(2):154–9.

    CAS 
    PubMed 

    Google Scholar 

  • Zhou X, et al. Tumor fractions deciphered from circulating cell-free DNA methylation for cancer early diagnosis. Nat Commun. 2022;13(1):7694.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Misawa M, Kudo S-E. Current status of artificial intelligence use in colonoscopy. Digestion. 2025;106(2):138–45.

    PubMed 

    Google Scholar 

  • Chitca DD, et al. Advancing colorectal cancer diagnostics from barium enema to AI-assisted colonoscopy. Diagnostics. 2025. https://doi.org/10.3390/diagnostics15080974.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hewett DG. Measurement of polyp size at colonoscopy: addressing human and technology bias. Dig Endosc. 2022;34(7):1478–80.

    PubMed 

    Google Scholar 

  • Babu B, et al. A narrative review on the role of Artificial Intelligence (AI) in colorectal cancer management. Cureus. 2025;17(2):e79570.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen J, et al. Ai support for colonoscopy quality control using CNN and transformer architectures. BMC Gastroenterol. 2024;24(1):257.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • FDA. Available from: Cited 2025 Aug 15.

  • FDA. Available from: Cited 2025 Aug 15.

  • Wang P, et al. Effect of a deep-learning computer-aided detection system on adenoma detection during colonoscopy (CADe-DB trial): a double-blind randomised study. Lancet Gastroenterol Hepatol. 2020;5(4):343–51.

    PubMed 

    Google Scholar 

  • Repici A, et al. Efficacy of real-time computer-aided detection of colorectal neoplasia in a randomized trial. Gastroenterology. 2020. https://doi.org/10.1053/j.gastro.2020.04.062.

    Article 
    PubMed 

    Google Scholar 

  • Karsenti D, et al. Effect of real-time computer-aided detection of colorectal adenoma in routine colonoscopy (COLO-GENIUS): a single-centre randomised controlled trial. Lancet Gastroenterol Hepatol. 2023;8(8):726–34.

    CAS 
    PubMed 

    Google Scholar 

  • Shaukat A, et al. Computer-aided detection improves adenomas per colonoscopy for screening and surveillance colonoscopy: a randomized trial. Gastroenterology. 2022;163(3):732–41.

    PubMed 

    Google Scholar 

  • Mangas-Sanjuan C, et al. Role of artificial intelligence in colonoscopy detection of advanced neoplasias: a randomized trial. Ann Intern Med. 2023;176(9):1145–52.

    PubMed 

    Google Scholar 

  • Hassan C, et al. Real-time computer-aided detection of colorectal neoplasia during colonoscopy : a systematic review and meta-analysis. Ann Intern Med. 2023;176(9):1209–20.

    PubMed 

    Google Scholar 

  • Reynolds S. Available from: Cited 2025 Aug 15.

  • Yeasmin MN, et al. Advances of AI in image-based computer-aided diagnosis: a review. Array. 2024;23:100357.

    Google Scholar 

  • Hassan C, et al. Artificial intelligence allows leaving-in-situ colorectal polyps. Clin Gastroenterol Hepatol. 2022. https://doi.org/10.1016/j.cgh.2022.04.045.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Rondonotti E, et al. Artificial intelligence-assisted optical diagnosis for the resect-and-discard strategy in clinical practice: the artificial intelligence BLI characterization (ABC) study. Endoscopy. 2023;55(1):14–22.

    PubMed 

    Google Scholar 

  • Barua I, et al. Real-time artificial intelligence-based optical diagnosis of neoplastic polyps during colonoscopy. NEJM Evid. 2022;1(6):EVIDoa2200003.

    PubMed 

    Google Scholar 

  • Li JW, et al. Real-world validation of a computer-aided diagnosis system for prediction of polyp histology in colonoscopy: a prospective multicenter study. Am J Gastroenterol. 2023;118(8):1353–64.

    PubMed 

    Google Scholar 

  • Rex DK, et al. The American Society for Gastrointestinal Endoscopy PIVI (preservation and incorporation of valuable endoscopic innovations) on real-time endoscopic assessment of the histology of diminutive colorectal polyps. Gastrointest Endosc. 2011;73(3):419–22.

    PubMed 

    Google Scholar 

  • Hassan C, et al. Computer-aided diagnosis for the resect-and-discard strategy for colorectal polyps: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2024;9(11):1010–9.

    PubMed 

    Google Scholar 

  • Graham S, et al. MILD-net: Minimal information loss dilated network for gland instance segmentation in colon histology images. Med Image Anal. 2019;52:199–211.

    PubMed 

    Google Scholar 

  • Zhao K, et al. Artificial intelligence quantified tumour-stroma ratio is an independent predictor for overall survival in resectable colorectal cancer. EBioMedicine. 2020;61:103054.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Giger ML, Chan HP, Boone J. Anniversary paper: history and status of CAD and quantitative image analysis: the role of medical physics and AAPM. Med Phys. 2008;35(12):5799–820.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Gao Y, et al. New frontiers: an update on computer-aided diagnosis for breast imaging in the age of artificial intelligence. AJR Am J Roentgenol. 2019;212(2):300–7.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen Y, et al. AI in breast cancer imaging: an update and future trends. Semin Nucl Med. 2025;55(3):358–70.

    PubMed 

    Google Scholar 

  • FDA. Available from: Cited 2025 Aug 15.

  • FDA. Available from: Cited 2025 Aug 15.

  • FDA. Available from: Cited 2025 Aug 15.

  • Lång K, et al. Artificial intelligence-supported screen reading versus standard double reading in the Mammography Screening with Artificial Intelligence trial (MASAI): a clinical safety analysis of a randomised, controlled, non-inferiority, single-blinded, screening accuracy study. Lancet Oncol. 2023;24(8):936–44.

    PubMed 

    Google Scholar 

  • Yala A, et al. Multi-institutional validation of a mammography-based breast cancer risk model. J Clin Oncol. 2022;40(16):1732–40.

    PubMed 

    Google Scholar 

  • Vachon CM, et al. Impact of artificial intelligence system and volumetric density on risk prediction of interval, screen-detected, and advanced breast cancer. J Clin Oncol. 2023;41(17):3172–83.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lotter W, et al. Artificial intelligence in oncology: current landscape, challenges, and future directions. Cancer Discov. 2024;14(5):711–26.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Liu Y, et al. Applications of artificial intelligence in breast pathology. Arch Pathol Lab Med. 2023;147(9):1003–13.

    CAS 
    PubMed 

    Google Scholar 

  • Zhang-Yin J, Mauel E, Talpe S. Update on sentinel lymph node methods and pathology in breast cancer. Diagnostics (Basel, Switzerland). 2024. https://doi.org/10.3390/diagnostics14030252.

    Article 
    PubMed 

    Google Scholar 

  • Paige. Available from: Cited 2025 Aug 15.

  • Brown JR, et al. Quantitative assessment Ki-67 score for prediction of response to neoadjuvant chemotherapy in breast cancer. Lab Invest. 2014;94(1):98–106.

    CAS 
    PubMed 

    Google Scholar 

  • Casterá C, Bernet L. HER2 immunohistochemistry inter-observer reproducibility in 205 cases of invasive breast carcinoma additionally tested by ISH. Ann Diagn Pathol. 2020;45:151451.

    PubMed 

    Google Scholar 

  • Cai L, et al. Improving Ki67 assessment concordance by the use of an artificial intelligence-empowered microscope: a multi-institutional ring study. Histopathology. 2021;79(4):544–55.

    PubMed 

    Google Scholar 

  • Bodén ACS, et al. The human-in-the-loop: an evaluation of pathologists’ interaction with artificial intelligence in clinical practice. Histopathology. 2021;79(2):210–8.

    PubMed 

    Google Scholar 

  • Dy A, et al. AI improves accuracy, agreement and efficiency of pathologists for Ki67 assessments in breast cancer. Sci Rep. 2024;14(1):1283.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Albuquerque DAN, et al. Systematic review and meta-analysis of artificial intelligence in classifying HER2 status in breast cancer immunohistochemistry. NPJ Digit Med. 2025;8(1):144.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Hwang EJ, Goo JM, Park CM. AI applications for thoracic imaging: considerations for best practice. Radiology. 2025;314(2):e240650.

    PubMed 

    Google Scholar 

  • Nam JG, et al. Development and validation of deep learning-based automatic detection algorithm for malignant pulmonary nodules on chest radiographs. Radiology. 2019;290(1):218–28.

    PubMed 

    Google Scholar 

  • Hwang EJ, et al. Development and validation of a deep learning-based automated detection algorithm for major thoracic diseases on chest radiographs. JAMA Netw Open. 2019;2(3):e191095.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Aberle DR, et al. Reduced lung-cancer mortality with low-dose computed tomographic screening. N Engl J Med. 2011;365(5):395–409.

    PubMed 

    Google Scholar 

  • RadNet. Available from: Cited 2025 Aug 16.

  • DeepHealth. Available from: Cited 2025 Aug 16.

  • Qure.ai. Available from: Cited 2025 Aug 16.

  • Yu K-H, et al. Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features. Nat Commun. 2016;7(1):12474.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Khosravi P, et al. Deep convolutional neural networks enable discrimination of heterogeneous digital pathology images. EBioMedicine. 2018;27:317–28.

    PubMed 

    Google Scholar 

  • Lu MY, et al. Data-efficient and weakly supervised computational pathology on whole-slide images. Nat Biomed Eng. 2021;5(6):555–70.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang S, et al. Convpath: a software tool for lung adenocarcinoma digital pathological image analysis aided by a convolutional neural network. EBioMedicine. 2019;50:103–10.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Hofman P, et al. Artificial intelligence for diagnosis and predictive biomarkers in non-small cell lung cancer patients: new promises but also new hurdles for the pathologist. Lung Cancer. 2025;200:108110.

    PubMed 

    Google Scholar 

  • Kasivisvanathan V, et al. MRI-targeted or standard biopsy for prostate-cancer diagnosis. N Engl J Med. 2018;378(19):1767–77.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Turkbey B, Haider MA. Deep learning-based artificial intelligence applications in prostate MRI: brief summary. Br J Radiol. 2022;95(1131):20210563.

    PubMed 

    Google Scholar 

  • Winkel DJ, et al. A novel deep learning based computer-aided diagnosis system improves the accuracy and efficiency of radiologists in reading biparametric magnetic resonance images of the prostate: results of a multireader, multicase study. Invest Radiol. 2021;56(10):605–13.

    CAS 
    PubMed 

    Google Scholar 

  • Siemens. Available from: Cited 2025 Aug 16.

  • Quantib. Available from: Cited 2025 Aug 16.

  • Cortechs.ai. Available from: Cited 2025 Aug 16.

  • GeHealthCare. Available from: Cited 2025 Aug 16.

  • Quibim. Available from: Cited 2025 Aug 16.

  • Campanella G, et al. Clinical-grade computational pathology using weakly supervised deep learning on whole slide images. Nat Med. 2019;25(8):1301–9.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Pinckaers H, et al. Detection of prostate cancer in whole-slide images through end-to-end training with image-level labels. IEEE Trans Med Imaging. 2021;40(7):1817–26.

    PubMed 

    Google Scholar 

  • Chen C-M, et al. A computer-aided diagnosis system for differentiation and delineation of malignant regions on whole-slide prostate histopathology image using spatial statistics and multidimensional densenet. Med Phys. 2020;47(3):1021–33.

    PubMed 

    Google Scholar 

  • Raciti P, et al. Clinical validation of artificial intelligence-augmented pathology diagnosis demonstrates significant gains in diagnostic accuracy in prostate cancer detection. Arch Pathol Lab Med. 2023;147(10):1178–85.

    PubMed 

    Google Scholar 

  • da Silva LM, et al. Independent real-world application of a clinical-grade automated prostate cancer detection system. J Pathol. 2021;254(2):147–58.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Perincheri S, et al. An independent assessment of an artificial intelligence system for prostate cancer detection shows strong diagnostic accuracy. Mod Pathol. 2021;34(8):1588–95.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Nagpal K, et al. Development and validation of a deep learning algorithm for improving Gleason scoring of prostate cancer. NPJ Digit Med. 2019;2(1):48.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Khalighi S, et al. Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis Oncol. 2024;8(1):80.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Musthafa N, Memon QA, Masud MM. Advancing brain tumor analysis: current trends, key challenges, and perspectives in deep learning-based brain MRI tumor diagnosis. Eng. 2025;6(5):82.

    Google Scholar 

  • Ertosun MG, Rubin DL. Automated Grading of Gliomas using Deep Learning in Digital Pathology Images: A modular approach with ensemble of convolutional neural networks. AMIA Annu Symp Proc. 2015;2015:1899–1908.

  • Li Z, et al. Vision transformer-based weakly supervised histopathological image analysis of primary brain tumors. iScience. 2023;26(1):105872.

    CAS 
    PubMed 

    Google Scholar 

  • Louis DN, et al. The 2021 WHO classification of tumors of the central nervous system: a summary. Neuro-oncol. 2021;23(8):1231–51.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Bender K, et al. High-grade astrocytoma with piloid features (HGAP): the Charité experience with a new central nervous system tumor entity. J Neuro-Oncol. 2021;153(1):109–20.

    Google Scholar 

  • Vermeulen C, et al. Ultra-fast deep-learned CNS tumour classification during surgery. Nature. 2023;622(7984):842–9.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hoang D-T, et al. Prediction of DNA methylation-based tumor types from histopathology in central nervous system tumors with deep learning. Nat Med. 2024;30(7):1952–61.

    CAS 
    PubMed 

    Google Scholar 

  • Kim HS, et al. Single-incision robotic colorectal surgery with the da Vinci SP® surgical system: initial results of 50 cases. Tech Coloproctol. 2023;27(7):589–99.

    CAS 
    PubMed 

    Google Scholar 

  • Picciariello A, et al. Evaluation of the da Vinci single-port system in colorectal cancer surgery: a scoping review. Update Surg. 2024;76(7):2515–20.

    Google Scholar 

  • Di Costanzo G, et al. Artificial intelligence and radiomics in magnetic resonance imaging of rectal cancer: a review. Explor Target Antitumor Ther. 2023;4(3):406–21.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Kather JN, et al. Predicting survival from colorectal cancer histology slides using deep learning: a retrospective multicenter study. PLoS Med. 2019;16(1):e1002730.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Reichling C, et al. Artificial intelligence-guided tissue analysis combined with immune infiltrate assessment predicts stage III colon cancer outcomes in PETACC08 study. Gut. 2020;69(4):681–90.

    CAS 
    PubMed 

    Google Scholar 

  • Wu Z, et al. Graph deep learning for the characterization of tumour microenvironments from spatial protein profiles in tissue specimens. Nat Biomed Eng. 2022;6(12):1435–48.

    CAS 
    PubMed 

    Google Scholar 

  • Foersch S, et al. Multistain deep learning for prediction of prognosis and therapy response in colorectal cancer. Nat Med. 2023;29(2):430–9.

    CAS 
    PubMed 

    Google Scholar 

  • Jiang X, et al. An MRI deep learning model predicts outcome in rectal cancer. Radiology. 2023;307(5):e222223.

    PubMed 

    Google Scholar 

  • Arole V, et al. Clinical validation of Histotype Px colorectal in patients in a U.S. colon cancer cohort. J Clin Oncol. 2024;42(16_suppl):3622–3622.

    Google Scholar 

  • DoMore. Available from: Cited 2025 Aug 16.

  • L’Imperio V, et al. Pathologist validation of a machine learning-derived feature for colon cancer risk stratification. JAMA Netw Open. 2023;6(3):e2254891.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Skrede O-J, et al. Deep learning for prediction of colorectal cancer outcome: a discovery and validation study. Lancet. 2020;395(10221):350–60.

    CAS 
    PubMed 

    Google Scholar 

  • Echle A, et al. Clinical-grade detection of microsatellite instability in colorectal tumors by deep learning. Gastroenterology. 2020. https://doi.org/10.1053/j.gastro.2020.06.021.

    Article 
    PubMed 

    Google Scholar 

  • Kather JN, et al. Deep learning can predict microsatellite instability directly from histology in gastrointestinal cancer. Nat Med. 2019;25(7):1054–6.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Owkin. Available from: Cited 2025 Aug 16.

  • Pfob A, et al. Towards patient-centered decision-making in breast cancer surgery: machine learning to predict individual patient-reported outcomes at 1-year follow-up. Ann Surg. 2023;277(1):e144–52.

    PubMed 

    Google Scholar 

  • Kothari R, et al. Raman spectroscopy and artificial intelligence to predict the Bayesian probability of breast cancer. Sci Rep. 2021;11(1):6482.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Park S, et al. A deep learning model of tumor cell architecture elucidates response and resistance to CDK4/6 inhibitors. Nat Cancer. 2024;5(7):996–1009.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sammut S-J, et al. Multi-omic machine learning predictor of breast cancer therapy response. Nature. 2022;601(7894):623–9.

    CAS 
    PubMed 

    Google Scholar 

  • du Ogier Terrail J, et al. Federated learning for predicting histological response to neoadjuvant chemotherapy in triple-negative breast cancer. Nat Med. 2023;29(1):135–46.

    Google Scholar 

  • Amgad M, et al. A population-level digital histologic biomarker for enhanced prognosis of invasive breast cancer. Nat Med. 2024;30(1):85–97.

    CAS 
    PubMed 

    Google Scholar 

  • Saltz J, et al., Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images. Cell Reports. 2018;23(1):181-193.

  • Binder A, et al. Morphological and molecular breast cancer profiling through explainable machine learning. Nat Mach Intell. 2021;3(4):355–66.

    Google Scholar 

  • Shamai G, et al. Deep learning-based image analysis predicts PD-L1 status from H&E-stained histopathology images in breast cancer. Nat Commun. 2022;13(1):6753.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Shamai G, et al. Artificial intelligence algorithms to assess hormonal status from tissue microarrays in patients with breast cancer. JAMA Netw Open. 2019;2(7):e197700–e197700.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Assaf ZJF, et al. A longitudinal circulating tumor DNA-based model associated with survival in metastatic non-small-cell lung cancer. Nat Med. 2023;29(4):859–68.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Widman AJ, et al. Ultrasensitive plasma-based monitoring of tumor burden using machine-learning-guided signal enrichment. Nat Med. 2024;30(6):1655–66.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Heeke S, et al. Tumor- and circulating-free DNA methylation identifies clinically relevant small cell lung cancer subtypes. Cancer Cell. 2024;42(2):225-237.e5.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Park S, et al. Artificial intelligence-powered spatial analysis of tumor-infiltrating lymphocytes as complementary biomarker for immune checkpoint inhibition in non-small-cell lung cancer. J Clin Oncol. 2022;40(17):1916–28.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Lunit. Available from: Cited 2025 Aug 17.

  • Vanguri RS, et al. Multimodal integration of radiology, pathology and genomics for prediction of response to PD-(L)1 blockade in patients with non-small cell lung cancer. Nat Cancer. 2022;3(10):1151–64.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang S, et al. Mining whole-lung information by artificial intelligence for predicting EGFR genotype and targeted therapy response in lung cancer: a multicohort study. Lancet Digit Health. 2022;4(5):e309–19.

    CAS 
    PubMed 

    Google Scholar 

  • Rakaee M, et al. Machine learning-based immune phenotypes correlate with STK11/KEAP1 co-mutations and prognosis in resectable NSCLC: a sub-study of the TNM-I trial. Ann Oncol. 2023;34(7):578–88.

    CAS 
    PubMed 

    Google Scholar 

  • Ricciuti B, et al. Genomic and immunophenotypic landscape of acquired resistance to PD-(L)1 blockade in non–small-cell lung cancer. J Clin Oncol. 2024;42(11):1311–21.

    CAS 
    PubMed 

    Google Scholar 

  • Khanna R, et al. Artificial intelligence in the management of prostate cancer. Nat Rev Urol. 2025;22(3):125–6.

    PubMed 

    Google Scholar 

  • Hung AJ, et al. Utilizing machine learning and automated performance metrics to evaluate robot-assisted radical prostatectomy performance and predict outcomes. J Endourol. 2018;32(5):438–44.

    PubMed 

    Google Scholar 

  • Checcucci E, et al. Three-dimensional automatic artificial intelligence driven augmented-reality selective biopsy during nerve-sparing robot-assisted radical prostatectomy: a feasibility and accuracy study. Asian J Urol. 2023;10(4):407–15.

    PubMed 
    PubMed Central 

    Google Scholar 

  • McIntosh C, et al. Clinical integration of machine learning for curative-intent radiation treatment of patients with prostate cancer. Nat Med. 2021;27(6):999–1005.

    CAS 
    PubMed 

    Google Scholar 

  • Nouranian S, et al. Learning-based multi-label segmentation of transrectal ultrasound images for prostate brachytherapy. IEEE Trans Med Imaging. 2016;35(3):921–32.

    PubMed 

    Google Scholar 

  • Daskivich TJ, et al. Limitations of the National Comprehensive Cancer Network® (NCCN®) guidelines for prediction of limited life expectancy in men with prostate cancer. J Urol. 2017;197(2):356–62.

    PubMed 

    Google Scholar 

  • Esteva A, et al. Prostate cancer therapy personalization via multi-modal deep learning on randomized phase III clinical trials. NPJ Digit Med. 2022;5(1):71.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Parker CTA, et al. External validation of a digital pathology-based multimodal artificial intelligence-derived prognostic model in patients with advanced prostate cancer starting long-term androgen deprivation therapy: a post-hoc ancillary biomarker study of four phase 3 randomised controlled trials of the STAMPEDE platform protocol. Lancet Digit Health. 2025. https://doi.org/10.1016/j.landig.2025.100885.

    Article 
    PubMed 

    Google Scholar 

  • Elmarakeby HA, et al. Biologically informed deep neural network for prostate cancer discovery. Nature. 2021;598(7880):348–52.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kartasalo K, et al. Detection of perineural invasion in prostate needle biopsies with deep neural networks. Virchows Arch. 2022;481(1):73–82.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Spratt DE, et al. Artificial intelligence predictive model for hormone therapy use in prostate cancer. NEJM Evid. 2023;2(8):EVIDoa2300023.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Le NQK, et al. XGBoost improves classification of MGMT promoter methylation status in IDH1 wildtype glioblastoma. J Pers Med. 2020;10(3):128.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Do DT, et al. Improving MGMT methylation status prediction of glioblastoma through optimizing radiomics features using genetic algorithm-based machine learning approach. Sci Rep. 2022;12(1):13412.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kawahara D, et al. Predicting the local response of metastatic brain tumor to Gamma Knife radiosurgery by radiomics with a machine learning method. Front Oncol. 2020;10:569461.

    PubMed 

    Google Scholar 

  • Peng L, et al. Distinguishing true progression from radionecrosis after stereotactic radiation therapy for brain metastases with machine learning and radiomics. Int J Radiat Oncol Biol Phys. 2018;102(4):1236–43.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhang B, et al. Machine-learning based MRI radiomics models for early detection of radiation-induced brain injury in nasopharyngeal carcinoma. BMC Cancer. 2020;20(1):502.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Kocher M, et al. Applications of radiomics and machine learning for radiotherapy of malignant brain tumors. Strahlenther Onkol. 2020;196(10):856–67.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Macyszyn L, et al. Imaging patterns predict patient survival and molecular subtype in glioblastoma via machine learning techniques. Neuro-oncol. 2016;18(3):417–25.

    PubMed 

    Google Scholar 

  • Zheng Y, et al. Spatial cellular architecture predicts prognosis in glioblastoma. Nat Commun. 2023;14(1):4122.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hsu E, et al. Machine learning and deep learning tools for the automated capture of cancer surveillance data. J Natl Cancer Inst Monogr. 2024;2024(65):145–51.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Alawad M, et al. Automatic extraction of cancer registry reportable information from free-text pathology reports using multitask convolutional neural networks. J Am Med Inform Assoc. 2020;27(1):89–98.

    PubMed 

    Google Scholar 

  • Chandrashekar M, et al. Path-BigBird: an AI-driven transformer approach to classification of cancer pathology reports. JCO Clin Cancer Inform. 2024;8:e2300148.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Placido D, et al. A deep learning algorithm to predict risk of pancreatic cancer from disease trajectories. Nat Med. 2023;29(5):1113–22.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Guevara M, et al. Large language models to identify social determinants of health in electronic health records. NPJ Digit Med. 2024;7(1):6.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Pan J, et al. Integrating large language models with human expertise for disease detection in electronic health records. Comput Biol Med. 2025;191:110161.

    PubMed 

    Google Scholar 

  • Abramson J, et al. Accurate structure prediction of biomolecular interactions with AlphaFold 3. Nature. 2024;630(8016):493–500.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang T, et al. Ab initio characterization of protein molecular dynamics with AI2BMD. Nature. 2024;635(8040):1019–27.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Ren F, et al. Alphafold accelerates artificial intelligence powered drug discovery: efficient discovery of a novel CDK20 small molecule inhibitor. Chem Sci. 2023;14(6):1443–52.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhavoronkov A, et al. Deep learning enables rapid identification of potent DDR1 kinase inhibitors. Nat Biotechnol. 2019;37(9):1038–40.

    CAS 
    PubMed 

    Google Scholar 

  • Vijayan RSK, et al. Enhancing preclinical drug discovery with artificial intelligence. Drug Discov Today. 2022;27(4):967–84.

    CAS 
    PubMed 

    Google Scholar 

  • Tran NL, et al. Artificial intelligence-driven new drug discovery targeting serine/threonine kinase 33 for cancer treatment. Cancer Cell Int. 2023;23(1):321.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Abdel-Rehim A, et al. Scientific hypothesis generation by large language models: laboratory validation in breast cancer treatment. J R Soc Interface. 2025;22(227):20240674.

    PubMed 
    PubMed Central 

    Google Scholar 

  • De Vries M, et al. Geometric deep learning and multiple-instance learning for 3d cell-shape profiling. Cell Syst. 2025;16(3):101229.

    PubMed 

    Google Scholar 

  • Zhao X, et al. Cancer mutations converge on a collection of protein assemblies to predict resistance to replication stress. Cancer Discov. 2024;14(3):508–23.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chaix, B., et al., When Chatbots Meet Patients: One-Year Prospective Study of Conversations Between Patients With Breast Cancer and a Chatbot. JMIR Cancer. 2019;5(1):e12856.

  • Tawfik, E., E. Ghallab, and A. Moustafa, A nurse versus a chatbot ‒ the effect of an empowerment program on chemotherapy-related side effects and the self-care behaviors of women living with breast Cancer: a randomized controlled trial. BMC Nurs. 2023;22(1):102.

  • Park S. AI chatbots and linguistic injustice. J Univ Lang. 2024;25(1):99–119.

    Google Scholar 

  • Kataoka Y, et al. Development and early feasibility of chatbots for educating patients with lung cancer and their caregivers in Japan: mixed methods study. JMIR Cancer. 2021;7(1):e26911.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Wu X, et al. reguloGPT: Harnessing GPT for Knowledge Graph Construction of Molecular Regulatory Pathways. BioRxiv : the Preprint Server For Biology. bioRxiv [Preprint]. 2024.

  • Ingólfsson HI, et al. Machine learning-driven multiscale modeling: bridging the scales with a next-generation simulation infrastructure. J Chem Theory Comput. 2023;19(9):2658–75.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Lee M, Ahmad SF, Xu J. Regulation and function of transposable elements in cancer genomes. Cell Mol Life Sci. 2024;81(1):157.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Riehl K, et al. Transposonultimate: software for transposon classification, annotation and detection. Nucleic Acids Res. 2022;50(11):e64.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang R, et al. DeepBIO: an automated and interpretable deep-learning platform for high-throughput biological sequence prediction, functional annotation and visualization analysis. Nucleic Acids Res. 2023;51(7):3017–29.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Williamson SM, Prybutok V. Balancing privacy and progress: a review of privacy challenges, systemic oversight, and patient perceptions in AI-driven healthcare. Appl Sci. 2024;14(2):675.

    CAS 

    Google Scholar 

  • Wang C, et al. Privacy protection in using artificial intelligence for healthcare: Chinese regulation in comparative perspective. Healthcare. 2022. https://doi.org/10.3390/healthcare10101878.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Khalid N, et al. Privacy-preserving artificial intelligence in healthcare: techniques and applications. Comput Biol Med. 2023;158:106848.

    PubMed 

    Google Scholar 

  • Pool J, et al. A systematic analysis of failures in protecting personal health data: a scoping review. Int J Inf Manage. 2024;74:102719.

    Google Scholar 

  • Murdoch B. Privacy and artificial intelligence: challenges for protecting health information in a new era. BMC Med Ethics. 2021;22(1):122.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Panagopoulos A, et al. Incentivizing the sharing of healthcare data in the AI era. Comput Law Secur Rev. 2022;45:105670.

    Google Scholar 

  • Li M, et al. From challenges and pitfalls to recommendations and opportunities: implementing federated learning in healthcare. Med Image Anal. 2025;101:103497.

    PubMed 

    Google Scholar 

  • Rieke N, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3(1):119.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Elbachir YM, et al. Federated Learning for Multi-institutional on 3D Brain Tumor Segmentation. in 2024 6th International Conference on Pattern Analysis and Intelligent Systems (PAIS). pages 1-8, IEEE, 2024.

  • Mohri M, Sivek G, Suresh AT. Agnostic federated learning. in International conference on machine learning. PMLR; 2019, pp. 4615–25.

  • Li T, et al. Fair resource allocation in federated learning. arXiv. 2019, arXiv:1905.10497.

  • Xu J, et al. Federated learning for healthcare informatics. J Healthc Inform Res. 2021;19. https://doi.org/10.1007/s41666-020-00082-4.

  • Almanifi ORA, et al. Communication and computation efficiency in federated learning: a survey. Internet of Things. 2023;22:100742.

    Google Scholar 

  • Singh JP, et al. Privacy-aware hierarchical federated learning in healthcare: integrating differential privacy and secure multi-party computation. Future Internet. 2025;17(8):345.

    Google Scholar 

  • Shukla S, et al. Federated learning with differential privacy for breast cancer diagnosis enabling secure data sharing and model integrity. Sci Rep. 2025;15(1):13061.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Nisevic M, Milojevic D, Spajic D. Synthetic data in medicine: legal and ethical considerations for patient profiling. Comput Struct Biotechnol J. 2025;28:190–8.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Zhou Z, et al. Privacy enhancing and generalizable deep learning with synthetic data for mediastinal neoplasm diagnosis. NPJ Digit Med. 2024;7(1):293.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Walonoski J, et al. Synthea: an approach, method, and software mechanism for generating synthetic patients and the synthetic electronic health care record. J Am Med Inform Assoc. 2018;25(3):230–8.

    PubMed 

    Google Scholar 

  • Li J, et al. A comprehensive survey on source-free domain adaptation. IEEE Trans Pattern Anal Mach Intell. 2024;46(8):5743–62.

    PubMed 

    Google Scholar 

  • Peng D, et al. Unsupervised domain adaptation via domain-adaptive diffusion. IEEE Trans Image Process. 2024. https://doi.org/10.1109/TIP.2024.3424985.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang H, et al. Dual-reference source-free active domain adaptation for nasopharyngeal carcinoma tumor segmentation across multiple hospitals. IEEE Trans Med Imaging. 2024;43(12):4078–90.

    PubMed 

    Google Scholar 

  • Guichemerre A, et al. Source-free domain adaptation of weakly-supervised object localization models for histology. in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. arXiv. 2024, arXiv:2404.19113.

  • Agbo CC, Mahmoud QH, Eklund JM. Blockchain technology in healthcare: a systematic review. Healthcare (Basel). 2019. https://doi.org/10.3390/healthcare7020056.

    Article 
    PubMed 

    Google Scholar 

  • Pokharel BP, et al. BlockHealthSecure: integrating blockchain and cybersecurity in post-pandemic healthcare systems. Information. 2025;16(2):133.

    Google Scholar 

  • Munjal, K. and R. Bhatia, A systematic review of homomorphic encryption and its contributions in healthcare industry. Complex Intell Systems. 2022;9(4):1–28.

  • Lawlor RT. The impact of GDPR on data sharing for European cancer research. Lancet Oncol. 2023;24(1):6–8.

    PubMed 

    Google Scholar 

  • Harvey HB, Gowda V. How the FDA regulates AI. Acad Radiol. 2020;27(1):58–61.

    PubMed 

    Google Scholar 

  • FDA. Available from: Cited 2025 Aug 12.

  • Venkatesh KP, Kadakia KT, Gilbert S. Learnings from the first AI-enabled skin cancer device for primary care authorized by FDA. NPJ Digit Med. 2024;7(1):156.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Fehrmann RSN, van Kruchten M, de Vries EGE. How to critically appraise and direct the trajectory of AI development and application in oncology. ESMO Real World Data and Digital Oncology. 2024;5:100066.

    Google Scholar 

  • Hovda T, et al. Retrospective evaluation of a CE-marked AI system, including 1,017,208 mammography screening examinations. Eur Radiol. 2025. https://doi.org/10.1007/s00330-025-11521-4.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Aiforia. Available from: Cited 2025 Aug 12.

  • Aiosyn. Available from: Cited 2025 Aug 12.

  • Lvovs D, et al. Balancing ethical data sharing and open science for reproducible research in biomedical data science. Cell Rep Med. 2025;6(4):102080.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Cheng C, et al. A general primer for data harmonization. Sci Data. 2024;11(1):152.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Bhinder B, et al. Artificial intelligence in cancer research and precision medicine. Cancer Discov. 2021;11(4):900–15.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Zavala VA, et al. Cancer health disparities in racial/ethnic minorities in the United States. Br J Cancer. 2021;124(2):315–32.

    PubMed 

    Google Scholar 

  • Bouguettaya A, Stuart EM, Aboujaoude E. Racial bias in AI-mediated psychiatric diagnosis and treatment: a qualitative comparison of four large language models. npj Digit Med. 2025;8(1):332.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Yuan J, et al. Integrated analysis of genetic ancestry and genomic alterations across cancers. Cancer Cell. 2018. https://doi.org/10.1016/j.ccell.2018.08.019.

    Article 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Spratt DE, et al. Racial/ethnic disparities in genomic sequencing. JAMA Oncol. 2016;2(8):1070–4.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Ju D, et al. Importance of including non-European populations in large human genetic studies to enhance precision medicine. Annu Rev Biomed Data Sci. 2022;5:321–39.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Sollis E, et al. The NHGRI-EBI GWAS catalog: knowledgebase and deposition resource. Nucleic Acids Res. 2023;51(D1):D977–85.

    CAS 
    PubMed 

    Google Scholar 

  • Martin AR, et al. Clinical use of current polygenic risk scores may exacerbate health disparities. Nat Genet. 2019;51(4):584–91.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Dutil J, et al. An interactive resource to probe genetic diversity and estimated ancestry in cancer cell lines. Cancer Res. 2019;79(7):1263–73.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Wonkam A, Adeyemo A. Leveraging our common African origins to understand human evolution and health. Cell Genom. 2023;3(3):100278.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Duda P, Jan Z. Human population history revealed by a supertree approach. Sci Rep. 2016;6(1):29890.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Smith LA, et al. Equitable machine learning counteracts ancestral bias in precision medicine. Nat Commun. 2025;16(1):2144.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Koçak B, et al. Bias in artificial intelligence for medical imaging: fundamentals, detection, avoidance, mitigation, challenges, ethics, and prospects. Diagn Intervent Radiol (Ankara, Turkey). 2025;31(2):75–88.

    Google Scholar 

  • Norori N, et al. Addressing bias in big data and AI for health care: a call for open science. Patterns. 2021;2(10):100347.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Pinaya WHL, et al. Brain Imaging Generation with Latent Diffusion Models. In: Deep Generative Models: Second MICCAI Workshop, DGM4MICCAI 2022, Held in Conjunction with MICCAI 2022, Singapore, September 22, 2022, Proceedings. Singapore, Singapore: Springer-Verlag; 2022. p. 117–26.

    Google Scholar 

  • McCradden MD, et al. Ethical limitations of algorithmic fairness solutions in health care machine learning. The Lancet Digital Health. 2020;2(5):e221–3.

    PubMed 

    Google Scholar 

  • Seyyed-Kalantari L, et al. Underdiagnosis bias of artificial intelligence algorithms applied to chest radiographs in under-served patient populations. Nat Med. 2021;27(12):2176–82.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Chen RJ, et al. Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng. 2023;7(6):719–42.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Buolamwini J, Gebru T. Gender shades: Intersectional accuracy disparities in commercial gender classification. InProceedings of the 1st Conference on Fairness, Accountability and Transparency. A.F. Sorelle and W. Christo, Editors. PMLR: Proceedings of Machine Learning Research; 2018. pp. 77–91.

  • Diao JA, et al. Clinical implications of removing race from estimates of kidney function. JAMA. 2021;325(2):184–6.

    PubMed 

    Google Scholar 

  • Kamiran F, Calders T. Data preprocessing techniques for classification without discrimination. Knowl Inf Syst. 2012;33(1):1–33.

    Google Scholar 

  • Krasanakis E, et al. Adaptive Sensitive Reweighting to Mitigate Bias in Fairness-aware Classification. In Proceedings of the 2018 World Wide Web Conference. Lyon, France: International World Wide Web Conferences Steering Committee; 2018. pp. 853–862.

  • Jiang H, Nachum O. Identifying and Correcting Label Bias in Machine Learning. Inproceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. C. Silvia and C. Roberto, Editors. PMLR: Proceedings of Machine Learning Research; 2020. pp. 702-712.

  • Kamishima T, et al. Fairness-aware classifier with prejudice remover regularizer. Berlin, Heidelberg: Springer Berlin Heidelberg; 2012.

  • Zafar MB, et al. Fairness Constraints: Mechanisms for Fair Classification. InProceedings of the 20th International Conference on Artificial Intelligence and Statistics. S. Aarti and Z. Jerry, Editors. PMLR: Proceedings of Machine Learning Research; 2017. pp. 962-970.

  • Goel N, Yaghini M, Faltings B. Non-Discriminatory Machine Learning through Convex Fairness Criteria. InProceedings of the 2018 AAAI/ACM Conference on AI, Ethics, and Society. New Orleans, LA, USA: Association for Computing Machinery; 2018. p. 116.

  • Corbett-Davies S, et al. Algorithmic decision making and the cost of fairness. in Proceedings of the 23rd acm sigkdd international conference on knowledge discovery and data mining. arXiv. 2017, arXiv:1701.08230.

  • Hardt M, Price E, Srebro N, .J.A.i.n.i.p.s. Equality of opportunity in supervised learning. arXiv.2016, arXiv:1610.02413.

  • Corbett-Davies S, et al. The measure and mismeasure of fairness. 2023. 24(312):1-117.

  • Kleinberg J, Mullainathan S, Raghavan M, J.a.p.a. Inherent trade-offs in the fair determination of risk scores. arXiv. 2016, arXiv:1609.05807.

  • Pleiss G, et al. On fairness and calibration. arXiv. 2017, arXiv:1709.02012.

  • Chouldechova A. Fair prediction with disparate impact: a study of bias in recidivism prediction instruments. Big Data. 2017;5(2):153–63.

    PubMed 

    Google Scholar 

  • Pfohl SR, Foryciarz A, Shah NH. An empirical characterization of fair machine learning for clinical risk prediction. J Biomed Inform. 2021;113:103621.

    PubMed 

    Google Scholar 

  • Zhao H, Gordon GJ, J.J.o.M.L.R. Inherent tradeoffs in learning fair representations. arXiv.2022, arXiv:1906.08386.

  • Giguere S, et al. Fairness guarantees under demographic shift. InProceedings of the 10th International Conference on Learning Representations (ICLR). 2022 Poster.

  • Subbaswamy A, Saria S. From development to deployment: dataset shift, causality, and shift-stable models in health AI. Biostatistics (Oxford, England). 2020;21(2):345–52.

    PubMed 

    Google Scholar 

  • Guo LL, et al. Evaluation of domain generalization and adaptation on improving model robustness to temporal dataset shift in clinical medicine. Sci Rep. 2022;12(1):2726.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Castro DC, Walker I, Glocker B. Causality matters in medical imaging. Nat Commun. 2020;11(1):3673.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Hashimoto T, et al. Fairness without demographics in repeated loss minimization. InInternational Conference on Machine Learning. PMLR 80:1929-1938, 2018.

  • Wang S, et al. Robust optimization for fairness with noisy protected groups. arXiv. 2020, arXiv:2002.09343.

  • Duchi JC, Namkoong HJ. Learning models with uniform performance via distributionally robust optimization. Ann Stat. 2021;49(3):1378–406.

    Google Scholar 

  • Heslin KC, et al. Trends in opioid-related inpatient stays shifted after the US transitioned to ICD-10-CM diagnosis coding in 2015. Med Care. 2017;55(11):918–23.

    PubMed 

    Google Scholar 

  • Guo LL, et al. Systematic review of approaches to preserve machine learning performance in the presence of temporal dataset shift in clinical medicine. Appl Clin Inform. 2021;12(4):808–15.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Bifet A, Gavalda R. Learning from time-changing data with adaptive windowing. In: Proc. of the 7th SIAM Int. Conf. on Data Mining, SDM (2007).

  • Hao M, et al. Efficient and privacy-enhanced federated learning for industrial artificial intelligence. IEEE Trans Ind Inform. 2020;16(10):6532–42.

    Google Scholar 

  • Yang Q, et al. Federated machine learning: concept and applications. 2019. 10(2 %J ACM Trans. Intell. Syst. Technol.): p. Article 12.

  • Bonawitz K, et al. Practical Secure Aggregation for Privacy-Preserving Machine Learning. InProceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. Dallas, Texas, USA: Association for Computing Machinery; 2017. pp. 1175–1191.

  • Rieke N, et al. The future of digital health with federated learning. NPJ Digit Med. 2020;3:119.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Wang Y, et al. Intelligent fault diagnosis with deep adversarial domain adaptation. IEEE Transactions on Instrumentation and Measurement. 2020;70:p. 1-9.

  • Bercea CI, et al. Feddis: Disentangled federated learning for unsupervised brain pathology segmentation. arXiv. 2021, arXiv:2103.03705.

  • Wexler, J., et al., Probing ML models for fairness with the what-if tool and SHAP: hands-on tutorial, in Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency. Barcelona, Spain: Association for Computing Machinery;2020. ACM, 705.

  • Meng C, et al. Interpretability and fairness evaluation of deep learning models on MIMIC-IV dataset. Sci Rep. 2022;12(1):7166.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Jacovi A, et al. Formalizing trust in artificial intelligence: Prerequisites, causes and goals of human trust in AI. InProceedings of the 2021 ACM conference on fairness, accountability, and transparency. 2021. p. 624 – 635.

  • Floridi L. Establishing the rules for building trustworthy AI. Nat Mach Intell. 2019;1(6):261–2.

    Google Scholar 

  • Liu X, et al. Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension. Nat Med. 2020;26(9):1364–74.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Sounderajah V, et al. Developing a reporting guideline for artificial intelligence-centred diagnostic test accuracy studies: the STARD-AI protocol. BMJ Open. 2021;11(6):e047709.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Murikah W, Nthenge JK, Musyoka FM. Bias and ethics of AI systems applied in auditing – a systematic review. Sci Afr. 2024;25:e02281.

    Google Scholar 

  • Durán JM, Pozzi G. Trust and trustworthiness in AI. Philos Technol. 2025;38(1):16.

    Google Scholar 

  • Buiten MC. Product liability for defective AI. Eur J Law Econ. 2024;57(1):239–73.

    Google Scholar 

  • Zhang C, et al. Novel research and future prospects of artificial intelligence in cancer diagnosis and treatment. J Hematol Oncol. 2023;16(1):114.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Smith AA, Li R, Tse ZTH. Reshaping healthcare with wearable biosensors. Sci Rep. 2023;13(1):4998.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Vo D-K, Trinh KTL. Advances in wearable biosensors for healthcare: current trends, applications, and future perspectives. Biosensors. 2024;14(11):560.

    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 

  • Kashaninejad N, et al. Chapter Nine – Wearable biosensors for cancer detection and monitoring. InProgress in Molecular Biology and Translational Science. K. Mahato and A. Pandya, Editors. Academic Press; 2025. pp. 311–354.

  • Song B, Liang R. Integrating artificial intelligence with smartphone-based imaging for cancer detection in vivo. Biosens Bioelectron. 2025;271:116982.

    CAS 
    PubMed 

    Google Scholar 

  • Hunt B, Ruiz AJ, Pogue BW. Smartphone-based imaging systems for medical applications: a critical review. J Biomed Opt. 2021;26(4):040902.

  • Markus AF, Kors JA, Rijnbeek PR. The role of explainability in creating trustworthy artificial intelligence for health care: a comprehensive survey of the terminology, design choices, and evaluation strategies. J Biomed Inform. 2021;113:103655.

    PubMed 

    Google Scholar 

  • Hassija V, et al. Interpreting black-box models: a review on explainable artificial intelligence. Cogn Comput. 2024;16(1):45–74.

    Google Scholar 

  • Altman S. Available from: Cited 2025 Aug 17.

  • 36kr. Available from: Cited 2025 Aug 17.

  • McCarthy J, et al. A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence, August 31, 1955. AI Mag. 2006;27(4):12.

    Google Scholar 

  • Fradkov AL. Early history of machine learning. IFAC-PapersOnLine. 2020;53(2):1385–90.

    Google Scholar 

  • Cortes C, Vapnik V. Support-vector networks. Mach Learn. 1995;20(3):273–97.

    Google Scholar 

  • Quinlan JR. Induction of decision trees. Mach Learn. 1986;1(1):81–106.

    Google Scholar 

  • Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. Commun ACM. 2017;60(6):84–90.

    Google Scholar 

  • Sarker IH. Deep learning: a comprehensive overview on techniques, taxonomy, applications and research directions. SN Comput Sci. 2021;2(6):420.

    PubMed 
    PubMed Central 

    Google Scholar 

  • Vaswani A, et al. Attention is all you need. In Proceedings of the 31st International Conference on Neural Information Processing Systems. Long Beach, California, USA: Curran Associates Inc.; 2017. pp. 6000–6010.

  • Sapkota R, Raza S, Karkee M. Comprehensive analysis of transparency and accessibility of chatgpt, deepseek, and other sota large language models. in Preprints. arXiv. 2025, arXiv:2502.18505.

  • link

    Exit mobile version