DingH, WuJ, ZhaoW, MatinlinnaJP, BurrowMF, TsoiJKH. Artificial intelligence in dentistry—A review. Front Dent Med, 2023; 4:1085251; doi: 10.3389/fdmed.2023.1085251
2.
PoplinR, VaradarajanAV, BlumerK, et al.Prediction of cardiovascular risk factors from retinal fundus photographs via deep learning. Nat Biomed Eng, 2018; 2(3):158–164; doi: 10.1038/s41551-018-0195-0
3.
SalehGA, BatoutyNM, GamalA, et al.Impact of imaging biomarkers and AI on breast cancer management: A brief review. Cancers (Basel), 2023; 15(21):5216; doi: 10.3390/cancers15215216
4.
RajeshAE, DavidsonOQ, LeeCS, LeeAY. Artificial intelligence and diabetic retinopathy: AI framework, prospective studies, head-to-head validation, and cost-effectiveness. Diabetes Care, 2023; 46(10):1728–1739; doi: 10.2337/dci23-0032
5.
ThakurS, DinhL, Le LavanyaR, et al.Use of artificial intelligence in forecasting glaucoma progression. Taiwan J Ophthalmol, 2023; 13(2):168–183; doi: 10.4103/tjo.TJO-D-23-00022
6.
ShiJ, GuoZ, ChenH, et al.Artificial intelligence-assisted terahertz imaging for rapid and label-free identification of efficient light formula in laser therapy. Biosensors (Basel), 2022; 12(10); doi: 10.3390/bios12100826
7.
PoalelungiDG, NeaguAI, FulgaA, et al.Revolutionizing pathology with artificial intelligence: Innovations in immunohistochemistry. J Pers Med, 2024; 14(7):693; doi: 10.3390/jpm14070693
8.
NkuneNW, AbrahamseH. Possible integration of artificial intelligence with photodynamic therapy and diagnosis: A review. J Drug Deliv Sci Technol, 2024; 101:106210; doi: 10.1016/j.jddst.2024.106210
9.
SzymaszekP, Tyszka-CzocharaM, OrtylJ. Application of photoactive compounds in cancer theranostics: Review on recent trends from photoactive chemistry to artificial intelligence. Molecules, 2024; 29(13); doi: 10.3390/molecules29133164
10.
YassineA-A, LilgeL, BetzV. Machine learning for real-time optical property recovery in interstitial photodynamic therapy: A stimulation-based study. Biomed Opt Express, 2021; 12(9):5401–5422; doi: 10.1364/BOE.431310
11.
LimL. Traumatic brain injury recovery with photobiomodulation: Cellular mechanisms, clinical evidence, and future potential. Cells, 2024; 13(5); doi: 10.3390/cells13050385
12.
LeeK, ChunM, JungB, et al.Machine-learning-based prediction of photobiomodulation effects on older adults with cognitive decline using functional near-infrared spectroscopy. IEEE Trans Neural Syst Rehabil Eng, 2024; 32:3710–3718; doi: 10.1109/TNSRE.2024.3469284
13.
Smak GregoorAM, SangersTE, BakkerLJ, et al.An artificial intelligence based app for skin cancer detection evaluated in a population based setting. Npj Digit Med, 2023; 6(1):90; doi: 10.1038/s41746-023-00831-w
14.
WilleminkMJ, KoszekWA, HardellC, et al.Preparing medical imaging data for machine learning. Radiology, 2020; 295(1):4–15; doi: 10.1148/radiol.2020192224
15.
SermesantM, DelingetteH, CochetH, JaïsP, AyacheN. Applications of artificial intelligence in cardiovascular imaging. Nat Rev Cardiol, 2021; 18(8):600–609; doi: 10.1038/s41569-021-00527-2
SavadjievP, ChongJ, DohanA, et al.Demystification of AI-driven medical image interpretation: Past, present and future. Eur Radiol, 2019; 29(3):1616–1624; doi: 10.1007/s00330-018-5674-x
