|
1
|
Hamamoto R, Suvarna K, Yamada M, Kobayashi
K, Shinkai N, Miyake M, Takahashi M, Jinnai S, Shimoyama R, Sakai
A, et al: Application of artificial intelligence technology in
oncology: Towards the establishment of precision medicine. Cancers
(Basel). 12(3532)2020.PubMed/NCBI View Article : Google Scholar
|
|
2
|
Lin PC, Tsai YS, Yeh YM and Shen MR:
Cutting-edge AI technologies meet precision medicine to improve
cancer care. Biomolecules. 12(1133)2022.PubMed/NCBI View Article : Google Scholar
|
|
3
|
Naik K, Goyal RK, Foschini L, Choi WC,
Thielscher C, Zhu H, Lu J, Lehár J, Pacanoswki MA, Terranova N, et
al: Current status and future directions: The application of
artificial intelligence/machine learning for precision medicine.
Clin Pharmacol Ther. 115:673–686. 2024.PubMed/NCBI View
Article : Google Scholar
|
|
4
|
Tsopra R, Fernandez X, Luchinat C,
Alberghina L, Lehrach H, Vanoni M, Dreher F, Sezerman OU, Cuggia M,
de Tayrac M, et al: A framework for validating AI in precision
medicine: Considerations from the European ITFoC consortium. BMC
Med Inform Decis Mak. 21(274)2021.PubMed/NCBI View Article : Google Scholar
|
|
5
|
Singh AV, Chandrasekar V, Paudel N, Laux
P, Luch A, Gemmati D, Tisato V, Prabhu KS, Uddin S and Dakua SP:
Integrative toxicogenomics: Advancing precision medicine and
toxicology through artificial intelligence and OMICs technology.
Biomed Pharmacother. 163(114784)2023.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Wu J and Zhao Y: Machine learning
technology in the application of genome analysis: A systematic
review. Gene. 705:149–156. 2019.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Bose S, Banerjee S, Kumar S, Saha A, Nandy
D and Hazra S: Review of applications of artificial intelligence
(AI) methods in crop research. J Appl Genet. 65:225–240.
2024.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Sun X, Xue A, Qi T, Chen D, Shi D, Wu Y,
Zheng Z, Zeng J and Yang J: Tumor mutational burden is polygenic
and genetically associated with complex traits and diseases. Cancer
Res. 81:1230–1239. 2021.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Zhang Y, Pang D, Wang Z, Ma L, Chen Y,
Yang L, Xiao W, Yuan H, Chang F and Ouyang H: An integrative
analysis of genotype-phenotype correlation in Charcot Marie Tooth
type 2A disease with MFN2 variants: A case and systematic review.
Gene. 883(147684)2023.PubMed/NCBI View Article : Google Scholar
|
|
10
|
Bahado-Singh RO, Ibrahim A, Al-Wahab Z,
Aydas B, Radhakrishna U, Yilmaz A and Vishweswaraiah S: Precision
gynecologic oncology: Circulating cell free DNA epigenomic
analysis, artificial intelligence and the accurate detection of
ovarian cancer. Sci Rep. 12(18625)2022.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Choi S and Kim S: Artificial intelligence
in the pathology of gastric cancer. J Gastric Cancer. 23:410–427.
2023.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Huang S, Hu P and Lakowski TM: Predicting
breast cancer drug response using a multiple-layer cell line drug
response network model. BMC Cancer. 21(648)2021.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Trac QT, Pawitan Y, Mou T, Erkers T,
Östling P, Bohlin A, Österroos A, Vesterlund M, Jafari R, Siavelis
I, et al: Prediction model for drug response of acute myeloid
leukemia patients. NPJ Precis Oncol. 7(32)2023.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Avci CB, Bagca BG, Shademan B, Takanlou
LS, Takanlou MS and Nourazarian A: Machine learning in oncological
pharmacogenomics: Advancing personalized chemotherapy. Funct Integr
Genomics. 24(182)2024.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Chiffelle J and Harari A: Personalized
cancer T-cell therapy takes the stage, mirroring vaccine success. J
Exp Med. 221(e20240854)2024.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Horgan R, Hage Diab Y, Fishel Bartal M,
Sibai BM and Saade G: Continuous glucose monitoring in pregnancy.
Obstet Gynecol. 143:195–203. 2024.PubMed/NCBI View Article : Google Scholar
|
|
17
|
Shi Y, Wang Y, Niu K, Zhang W, Lv Q and
Zhang Y: How CLSPN could demystify its prognostic value and
potential molecular mechanism for hepatocellular carcinoma: A
crosstalk study. Comput Biol Med. 172(108260)2024.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Zhu J, Shi Y, Lan S, Wang J, Jiang F, Tang
C, Cai Y, Pan Z, Jian H, Fang H, et al: Dissection of
pyroptosis-related prognostic signature and CASP6-mediated
regulation in pancreatic adenocarcinoma: New sights to clinical
decision-making. Apoptosis. 28:769–782. 2023.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Biso L, Aringhieri S, Carli M, Scarselli M
and Longoni B: Therapeutic drug monitoring in psychiatry: Enhancing
treatment precision and patient outcomes. Pharmaceuticals (Basel).
17(642)2024.PubMed/NCBI View Article : Google Scholar
|
|
20
|
Cheung CY, Tang F, Ting DSW, Tan GSW and
Wong TY: Artificial intelligence in diabetic eye disease screening.
Asia Pac J Ophthalmol (Phila). 8:158–164. 2019.PubMed/NCBI View Article : Google Scholar
|
|
21
|
Li XH, Liu F and Rao HY: New advances of
artificial intelligence in the diagnosis of non-alcoholic fatty
liver disease. Zhonghua Gan Zang Bing Za Zhi. 30:443–446.
2022.PubMed/NCBI View Article : Google Scholar : (In Chinese).
|
|
22
|
Wang HY, Lin WY, Zhou C, Yang ZA, Kalpana
S and Lebowitz MS: Integrating artificial intelligence for
advancing multiple-cancer early detection via serum biomarkers: A
narrative review. Cancers (Basel). 16(862)2024.PubMed/NCBI View Article : Google Scholar
|
|
23
|
Reitsam NG, Enke JS, Vu Trung K, Märkl B
and Kather JN: Artificial intelligence in colorectal cancer: From
patient screening over tailoring treatment decisions to
identification of novel biomarkers. Digestion. 105:331–344.
2024.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Huang JD, Wang J, Ramsey E, Leavey G,
Chico TJA and Condell J: Applying artificial intelligence to
wearable sensor data to diagnose and predict cardiovascular
disease: A review. Sensors (Basel). 22(8002)2022.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Tan YY, Kang HG, Lee CJ, Kim SS, Park S,
Thakur S, Da Soh Z, Cho Y, Peng Q, Lee K, et al: Prognostic
potentials of AI in ophthalmology: Systemic disease forecasting via
retinal imaging. Eye Vis (Lond). 11(17)2024.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Guo L, Gao W, Cao Y and Lai X: Research on
medical data security sharing scheme based on homomorphic
encryption. Math Biosci Eng. 20:2261–2279. 2023.PubMed/NCBI View Article : Google Scholar
|
|
27
|
Gupta R, Kanungo P, Dagdee N, Madhu G,
Sahoo KS, Jhanjhi NZ, Masud M, Almalki NS and AlZain MA: Secured
and privacy-preserving multi-authority access control system for
cloud-based healthcare data sharing. Sensors (Basel).
23(2617)2023.PubMed/NCBI View Article : Google Scholar
|
|
28
|
Bertolaccini L, Falcoz PE, Brunelli A,
Batirel H, Furak J, Passani S and Szanto Z: The significance of
general data protection regulation in the compliant data
contribution to the European society of thoracic surgeons database.
Eur J Cardiothorac Surg. 64(ezad289)2023.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Miller DC and Smith CC: Spine Intervention
Society's Patient Safety Committee. Informed consent. Pain Med:
pnaa112, 2020 (Epub ahead of print).
|
|
30
|
Zhang J and Zhang ZM: Ethics and
governance of trustworthy medical artificial intelligence. BMC Med
Inform Decis Mak. 23(7)2023.PubMed/NCBI View Article : Google Scholar
|
|
31
|
Khatiwada P, Yang B, Lin JC and Blobel B:
Patient-generated health data (PGHD): Understanding, requirements,
challenges, and existing techniques for data security and privacy.
J Pers Med. 14(282)2024.PubMed/NCBI View Article : Google Scholar
|
|
32
|
Antwi WK, Akudjedu TN and Botwe BO:
Artificial intelligence in medical imaging practice in Africa: A
qualitative content analysis study of radiographers' perspectives.
Insights Imaging. 12(80)2021.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Sheth S, Baker HP, Prescher H and Strelzow
JA: Ethical considerations of artificial intelligence in health
care: Examining the role of generative pretrained transformer-4. J
Am Acad Orthop Surg. 32:205–210. 2024.PubMed/NCBI View Article : Google Scholar
|
|
34
|
Walker DM, Tarver WL, Jonnalagadda P,
Ranbom L, Ford EW and Rahurkar S: Perspectives on challenges and
opportunities for interoperability: Findings from key informant
interviews with stakeholders in ohio. JMIR Med Inform.
11(e43848)2023.PubMed/NCBI View
Article : Google Scholar
|
|
35
|
Pournik O, Ahmad B, Lim Choi Keung SN,
Peake A, Rafid S, Tong C, Laleci Erturkmen GB, Gencturk M, Akpinar
AE and Arvanitis TN: Interoperable E-health system using structural
and semantic interoperability approaches in CAREPATH. Stud Health
Technol Inform. 305:608–611. 2023.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Gavrilov G, Vlahu-Gjorgievska E and
Trajkovik V: Healthcare data warehouse system supporting
cross-border interoperability. Health Informatics J. 26:1321–1332.
2020.PubMed/NCBI View Article : Google Scholar
|
|
37
|
Ishimoto K, Arafune T, Washio T, Haishima
Y, Matsumoto K, Uematsu M, Nomura Y, Yokoi H, Sato H, Murakami M,
et al: Japanese regulatory considerations for interoperability of
medical devices. Ther Innov Regul Sci. 57:104–108. 2023.PubMed/NCBI View Article : Google Scholar
|
|
38
|
Nitiéma P: Artificial intelligence in
medicine: Text mining of health care workers' opinions. J Med
Internet Res. 25(e41138)2023.PubMed/NCBI View
Article : Google Scholar
|
|
39
|
Baric-Parker J and Anderson EE: Patient
data-sharing for AI: Ethical challenges, catholic solutions.
Linacre Q. 87:471–481. 2020.PubMed/NCBI View Article : Google Scholar
|
|
40
|
Müller L, Kloeckner R, Mildenberger P and
Pinto Dos Santos D: Validation and implementation of artificial
intelligence in radiology: Quo vadis in 2022? Radiologie (Heidelb).
63:381–386. 2023.PubMed/NCBI View Article : Google Scholar : (In German).
|
|
41
|
Shen TT, Liu CF and Wu MP: Implementation
of a machine learning model in acute coronary syndrome and stroke
risk assessment for patients with lower urinary tract symptoms.
Taiwan J Obstet Gynecol. 63:518–526. 2024.PubMed/NCBI View Article : Google Scholar
|
|
42
|
Barchielli C, Marullo C, Bonciani M and
Vainieri M: Nurses and the acceptance of innovations in
technology-intensive contexts: The need for tailored management
strategies. BMC Health Serv Res. 21(639)2021.PubMed/NCBI View Article : Google Scholar
|
|
43
|
Le Duff M, Michinov E, Bracq MS, Mukae N,
Eto M, Descamps J, Hashizume M and Jannin P: Virtual reality
environments to train soft skills in medical and nursing education:
A technical feasibility study between France and Japan. Int J
Comput Assist Radiol Surg. 18:1355–1362. 2023.PubMed/NCBI View Article : Google Scholar
|
|
44
|
Abril-Jiménez P, Merino-Barbancho B,
Lombroni I, Villanueva-Mascato S, Mallo I, Vera-Muñoz C, Arredondo
MT and Fico G: Design of a training model for remote management of
patients hospitalized at home. J Med Biol Eng. 40:610–617.
2020.PubMed/NCBI View Article : Google Scholar
|
|
45
|
Lafferty L, Smith K, Causer L, Andrewartha
K, Whiley D, Badman SG, Donovan B, Anderson L, Tangey A, Mak D, et
al: Scaling up sexually transmissible infections point-of-care
testing in remote aboriginal and torres strait islander
communities: Healthcare workers' perceptions of the barriers and
facilitators. Implement Sci Commun. 2(127)2021.PubMed/NCBI View Article : Google Scholar
|