Kononenko, I.: Machine learning for medical diagnosis: history, state of the art and perspective. Artif. Intell. Med. 23(1), 89–109 (2001)

CAS  PubMed  Google Scholar 

Thevenot, J., López, M.B., Hadid, A.: A survey on computer vision for assistive medical diagnosis from faces. IEEE J. Biomed. Health Inform. 22(5), 1497–1511 (2017)

PubMed  Google Scholar 

Chen, X., Xie, H., Wang, F.L., Liu, Z., Xu, J., Hao, T.: A bibliometric analysis of natural language processing in medical research. BMC Med. Inform. Decis. Mak. 18(Suppl 1), 14 (2018)

PubMed  PubMed Central  Google Scholar 

Dario, P., Guglielmelli, E., Allotta, B., Carrozza, M.C.: Robotics for medical applications. IEEE Robot. Autom. Mag. 3(3), 44–56 (2002)

Google Scholar 

Kaur, S., Singla, J., Nkenyereye, L., Jha, S., Prashar, D., Joshi, G.P., et al.: Medical diagnostic systems using artificial intelligence (AI) algorithms: principles and perspectives. IEEE Access 8, 228049–228069 (2020)

Google Scholar 

Vaishya, R., Javaid, M., Khan, I.H., Haleem, A.: Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab. Syndr. 14(4), 337–339 (2020)

PubMed  PubMed Central  Google Scholar 

Fernandes, J.G.: Artificial intelligence in telemedicine. In: Artificial intelligence in medicine, pp. 1219–1227. Springer International Publishing, Cham (2022)

Google Scholar 

Sujith, A.V.L.N., Sajja, G.S., Mahalakshmi, V., Nuhmani, S., Prasanalakshmi, B.: Systematic review of smart health monitoring using deep learning and artificial intelligence. Neurosci. Inform. 2(3), 100028 (2022)

Google Scholar 

Reverberi, C., Rigon, T., Solari, A., Hassan, C., Cherubini, P., Cherubini, A.: Experimental evidence of effective human–AI collaboration in medical decision-making. Sci. Rep. 12(1), 14952 (2022)

ADS  CAS  PubMed  PubMed Central  Google Scholar 

Johnson, K.B., Wei, W.Q., Weeraratne, D., Frisse, M.E., Misulis, K., Rhee, K.,et al.: Precision medicine, AI, and the future of personalized health care. Clin. Transl. Sci. 14(1), 86–93 (2021)

Google Scholar 

Taimoor, N., Rehman, S.: Reliable and resilient AI and IoT-based personalised healthcare services: a survey. IEEE Access 10, 535–563 (2021)

Google Scholar 

Vishwakarma, L.P., Singh, R.K., Mishra, R., Kumari, A.: Application of artificial intelligence for resilient and sustainable healthcare system: systematic literature review and future research directions. Int. J. Prod. Res. 63(2), 822–844 (2025)

Google Scholar 

Kohli, M.D., Summers, R.M., Geis, J.R.: Medical image data and datasets in the era of machine learning—whitepaper from the 2016 C-MIMI meeting dataset session. J. Digit. Imaging 30(4), 392–399 (2017)

PubMed  PubMed Central  Google Scholar 

Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Annu. Rev. Biomed. Eng. 19(1), 221–248 (2017)

CAS  PubMed  PubMed Central  Google Scholar 

Khanbhai, M., Anyadi, P., Symons, J., Flott, K., Darzi, A., Mayer, E.: Applying natural language processing and machine learning techniques to patient experience feedback: a systematic review. BMJ Health Care Inform. 28(1), e100262 (2021)

PubMed  PubMed Central  Google Scholar 

Subramanian, M., Wojtusciszyn, A., Favre, L., Boughorbel, S., Shan, J., Letaief, K.B., et al.: Precision medicine in the era of artificial intelligence: implications in chronic disease management. J. Transl. Med. 18(1), 472 (2020)

Google Scholar 

Pesapane, F., Codari, M., Sardanelli, F.: Artificial intelligence in medical imaging: threat or opportunity? Radiologists again at the forefront of innovation in medicine. Eur. Radiol. Exp. 2(1), 35 (2018)

PubMed  PubMed Central  Google Scholar 

Dvijotham, K., Winkens, J., Barsbey, M., Ghaisas, S., Stanforth, R., Pawlowski, N., et al.: Enhancing the reliability and accuracy of AI-enabled diagnosis via complementarity-driven deferral to clinicians. Nat. Med. 29(7), 1814–1820 (2023)

Google Scholar 

Liao, J., Li, X., Gan, Y., Han, S., Rong, P., Wang, W., et al.: Artificial intelligence assists precision medicine in cancer treatment. Front. Oncol. 12, 998222 (2023)

Google Scholar 

Bi, W.L., Hosny, A., Schabath, M.B., Giger, M.L., Birkbak, N.J., Mehrtash, A., et al.: Artificial intelligence in cancer imaging: clinical challenges and applications. CA Cancer J. Clin. 69(2), 127–157 (2019)

Google Scholar 

Suzuki, K.: Overview of deep learning in medical imaging. Radiol. Phys. Technol. 10(3), 257–273 (2017)

PubMed  Google Scholar 

Salehi, A.W., Khan, S., Gupta, G., Alabduallah, B.I., Almjally, A., Alsolai, H., et al.: A study of CNN and transfer learning in medical imaging: advantages, challenges, future scope. Sustainability 15(7), 5930 (2023)

Google Scholar 

Levine, A.B., Schlosser, C., Grewal, J., Coope, R., Jones, S.J., Yip, S.: Rise of the machines: advances in deep learning for cancer diagnosis. Trends Cancer 5(3), 157–169 (2019)

PubMed  Google Scholar 

Bakator, M., Radosav, D.: Deep learning and medical diagnosis: a review of literature. Multimodal Technol. Interact. 2(3), 47 (2018)

Google Scholar 

Pei, Y., Yang, J.: Biomedical applications of big data and artificial intelligence. Bioengineering 12(2), 207 (2025)

PubMed  PubMed Central  Google Scholar 

Hrinivich, W.T., Lee, J.: Artificial intelligence-based radiotherapy machine parameter optimization using reinforcement learning. Med. Phys. 47(12), 6140–6150 (2020)

PubMed  Google Scholar 

Ferrara, M., Bertozzi, G., Di Fazio, N., Aquila, I., Di Fazio, A., Maiese, A., et al.: Risk management and patient safety in the artificial intelligence era: a systematic review. Healthcare 12(5), 549 (2024)

Google Scholar 

Sun, X., Yin, Y., Yang, Q., Huo, T.: Artificial intelligence in cardiovascular diseases: diagnostic and therapeutic perspectives. Eur. J. Med. Res. 28(1), 242 (2023)

PubMed  PubMed Central  Google Scholar 

Shaik, T., Tao, X., Higgins, N., Li, L., Gururajan, R., Zhou, X., Acharya, U.R.: Remote patient monitoring using artificial intelligence: current state, applications, and challenges. Wiley Interdisc. Rev. Data Min. Knowl. Disc. 13(2), e1485 (2023)

Google Scholar 

Zhou, B., Yang, G., Shi, Z., Ma, S.: Natural language processing for smart healthcare. IEEE Rev. Biomed. Eng. 17, 4–18 (2022)

Google Scholar 

Juhn, Y., Liu, H.: Artificial intelligence approaches using natural language processing to advance EHR-based clinical research. J. Allergy Clin. Immunol. 145(2), 463–469 (2020)

PubMed  Google Scholar 

Liopyris, K., Gregoriou, S., Dias, J., Stratigos, A.J.: Artificial intelligence in dermatology: challenges and perspectives. Dermatol. Ther. 12(12), 2637–2651 (2022)

Google Scholar 

Son, H.M., Jeon, W., Kim, J., Heo, C.Y., Yoon, H.J., Park, J.U., Chung, T.M.: AI-based localization and classification of skin disease with erythema. Sci. Rep. 11(1), 5350 (2021)

ADS  CAS  PubMed  PubMed Central  Google Scholar 

Young, A.T., Xiong, M., Pfau, J., Keiser, M.J., Wei, M.L.: Artificial intelligence in dermatology: a primer. J. Investig. Dermatol. 140(8), 1504–1512 (2020)

CAS  PubMed  Google Scholar 

Dick, V., Sinz, C., Mittlböck, M., Kittler, H., Tschandl, P.: Accuracy of computer-aided diagnosis of melanoma: a meta-analysis. JAMA Dermatol. 155(11), 1291–1299 (2019)

PubMed  PubMed Central  Google Scholar 

Esteva, A., Kuprel, B., Novoa, R. A., Ko, J., Swetter, S.M., Blau, H.M., Thrun, S.: Dermatologist-level classification of skin cancer with deep neural networks. Nature 542(7639), 115–118 (2017)

Google Scholar 

Mathur, P., Srivastava, S., Xu, X., Mehta, J.L.: Artificial intelligence, machine learning, and cardiovascular disease. Clin. Med. Insights Cardiol. 14, 1179546820927404 (2020)

PubMed  PubMed Central  Google Scholar 

Ng, B., Nayyar, S., Chauhan, V.S.: The role of artificial intelligence and machine learning in clinical cardiac electrophysiology. Can. J. Cardiol. 38(2), 246–258 (2022)

PubMed  Google Scholar 

Abubaker, M.B., Babayiğit, B.: Detection of cardiovascular diseases in ECG images using machine learning and deep learning methods. IEEE Trans. Artif. Intell. 4(2), 373–382 (2022)

Google Scholar 

Attia, Z.I., Kapa, S., Lopez-Jimenez, F., McKie, P. M., Ladewig, D.J., Satam, G., et al.: Screening for cardiac contractile dysfunction using an artificial intelligence–enabled electrocardiogram. Nat. Med. 25(1), 70–74 (2019)

Google Scholar 

Alsharqi, M., Woodward, W.J., Mumith, J.A., Markham, D.C., Upton, R., Leeson, P.: Artificial intelligence and echocardiography. Echo Res. Pract. 5(4), R115–R125 (2018)

CAS  PubMed  PubMed Central  Google Scholar 

Raghavendra, U., Acharya, U.R., Adeli, H.: Artificial intelligence techniques for automated diagnosis of neurological disorders. Eur. Neurol. 82(1–3), 41–64 (2020)

Google Scholar 

Pedersen, M., Verspoor, K., Jenkinson, M., Law, M., Abbott, D.F., Jackson, G.D.: Artificial intelligence for clinical decision support in neurology. Brain Commun. 2(2), fcaa096 (2020)

Google Scholar 

Khalighi, S., Reddy, K., Midya, A., Pandav, K.B., Madabhushi, A., Abedalthagafi, M.: Artificial intelligence in neuro-oncology: advances and challenges in brain tumor diagnosis, prognosis, and precision treatment. NPJ Precis. Oncol. 8(1), 80 (2024)

PubMed  PubMed Central  Google Scholar 

Ranjbarzadeh, R., Caputo, A., Tirkolaee, E.B., Ghoushchi, S.J., Bendechache, M.: Brain tumor segmentation of MRI images: a comprehensive review on the application of artificial intelligence tools. Comput. Biol. Med. 152, 106405 (2023)

PubMed  Google Scholar 

Ibrahim, R., Ghnemat, R., Abu Al-Haija, Q.: Improving Alzheimer’s disease and brain tumor detection using deep learning with particle swarm optimization. AI 4(3), 551–573 (2023)

Google Scholar 

Berbís, M.A., Aneiros-Fernández, J., Olivares, F.J.M., Nava, E., Luna, A.: Role of artificial intelligence in multidisciplinary imaging diagnosis of gastrointestinal diseases. World J. Gastroenterol. 27(27), 4395 (2021)

PubMed