|
1
|
Topol EJ: High-performance medicine: The
convergence of human and artificial intelligence. Nat Med.
25:44–56. 2019.PubMed/NCBI View Article : Google Scholar
|
|
2
|
Esteva A, Chou K, Yeung S, Naik N, Madani
A, Mottaghi A, Liu Y, Topol E, Dean J and Socher R: Deep
learning-enabled medical computer vision. NPJ Digit Med.
4(5)2021.PubMed/NCBI View Article : Google Scholar
|
|
3
|
Biswas M, Kuppili V, Saba L, Edla DR, Suri
HS, Cuadrado-Godia E, Laird JR, Marinhoe RT, Sanches JM, Nicolaides
A and Suri JS: State-of-the-art review on deep learning in medical
imaging. Front Biosci (Landmark Ed). 24:392–426. 2019.PubMed/NCBI View
Article : Google Scholar
|
|
4
|
Xu Y, Li Y, Wang F, Zhang Y and Huang D:
Addressing the current challenges in the clinical application of
AI-based radiomics for cancer imaging. Front Med (Lausanne).
12(1674397)2025.PubMed/NCBI View Article : Google Scholar
|
|
5
|
Abbas Q, Jeong W and Lee SW: Explainable
AI in clinical decision support systems: A meta-analysis of
methods, applications, and usability challenges. Healthcare
(Basel). 13(2154)2025.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Chua IS, Gaziel-Yablowitz M, Korach ZT,
Kehl KL, Levitan NA, Arriaga YE, Jackson GP, Bates DW and Hassett
M: Artificial intelligence in oncology: Path to implementation.
Cancer Med. 10:4138–4149. 2021.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Sultan AS, Elgharib MA, Tavares T, Jessri
M and Basile JR: The use of artificial intelligence, machine
learning and deep learning in oncologic histopathology. J Oral
Pathol Med. 49:849–856. 2020.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Hong GS, Jang M, Kyung S, Cho K, Jeong J,
Lee GY, Shin K, Kim KD, Ryu SM, Seo JB, et al: Overcoming the
challenges in the development and implementation of artificial
intelligence in radiology: A comprehensive review of solutions
beyond supervised learning. Korean J Radiol. 24:1061–1080.
2023.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Ueda D, Kakinuma T, Fujita S, Kamagata K,
Fushimi Y, Ito R, Matsui Y, Nozaki T, Nakaura T, Fujima N, et al:
Fairness of artificial intelligence in healthcare: Review and
recommendations. Jpn J Radiol. 42:3–15. 2024.PubMed/NCBI View Article : Google Scholar
|
|
10
|
Melazzini L, Bortolotto C, Brizzi L,
Achilli M, Basla N, D'Onorio De Meo A, Gerbasi A, Bottinelli OM,
Bellazzi R and Preda L: AI for image quality and patient safety in
CT and MRI. Eur Radiol Exp. 9(28)2025.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Nie Y, Sommella P, Carratù M, O'Nils M and
Lundgren J: A deep CNN transformer hybrid model for skin lesion
classification of dermoscopic images using focal loss. Diagnostics
(Basel). 13(72)2022.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Tschandl P, Rinner C, Apalla Z, Argenziano
G, Codella N, Halpern A, Janda M, Lallas A, Longo C, Malvehy J, et
al: Human-computer collaboration for skin cancer recognition. Nat
Med. 26:1229–1234. 2020.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Goyal H, Mann R, Gandhi Z, Perisetti A,
Ali A, Aman Ali K, Sharma N, Saligram S, Tharian B and Inamdar S:
Scope of artificial intelligence in screening and diagnosis of
colorectal cancer. J Clin Med. 9(3313)2020.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Ho TY, Chao CH, Chin SC, Ng SH, Kang CJ
and Tsang NM: Classifying neck lymph nodes of head and neck
squamous cell carcinoma in MRI images with radiomic features. J
Digit Imaging. 33:613–618. 2020.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Yala A, Lehman C, Schuster T, Portnoi T
and Barzilay R: A deep learning mammography-based model for
improved breast cancer risk prediction. Radiology. 292:60–66.
2019.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Sivakumar R, Lue B and Kundu S: FDA
approval of artificial intelligence and machine learning devices in
radiology: A systematic review. JAMA Netw Open.
8(e2542338)2025.PubMed/NCBI View Article : Google Scholar
|
|
17
|
Evangelou K, Kotsantis I, Kalyvas A,
Kyriazoglou A, Economopoulou P, Velonakis G, Gavra M, Psyrri A,
Boviatsis EJ and Stavrinou LC: Artificial intelligence in the
diagnosis and treatment of brain gliomas. Biomedicines.
13(2285)2025.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Kelly CJ, Karthikesalingam A, Suleyman M,
Corrado G and King D: Key challenges for delivering clinical impact
with artificial intelligence. BMC Med. 17(195)2019.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Roberts M, Driggs D, Thorpe M, Gilbey J,
Yeung M, Ursprung S, Aviles-Rivero AI, Etmann C, McCague C, Beer L,
et al: Common pitfalls and recommendations for using machine
learning to detect and prognosticate for COVID-19 using chest
radiographs and CT scans. Nat Mach Intell. 3:199–217. 2021.
|
|
20
|
Zech JR, Badgeley MA, Liu M, Costa AB,
Titano JJ and Oermann EK: Variable generalization performance of a
deep learning model to detect pneumonia in chest radiographs: A
cross-sectional study. PLoS Med. 15(e1002683)2018.PubMed/NCBI View Article : Google Scholar
|
|
21
|
Tran AT, Zeevi T and Payabvash S:
Strategies to Improve the Robustness and Generalizability of Deep
Learning Segmentation and Classification in Neuroimaging.
BioMedInformatics. 5(20)2025.PubMed/NCBI View Article : Google Scholar
|
|
22
|
Chaddad A, Peng J, Xu J and Bouridane A:
Survey of explainable AI techniques in healthcare. Sensors (Basel).
23(634)2023.PubMed/NCBI View Article : Google Scholar
|
|
23
|
Yang M, Huang D, Wan W and Jin M:
Federated learning for privacy-preserving medical data sharing in
drug development. Appl Comput Eng. 134:80–84. 2025.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Iizuka O, Kanavati F, Kato K, Rambeau M,
Arihiro K and Tsuneki M: Deep learning models for histopathological
classification of gastric and colonic epithelial tumours. Sci Rep.
10(1504)2020.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Butt MA, Kaleem MF, Bilal M and Hanif MS:
Using multi-label ensemble CNN classifiers to mitigate labelling
inconsistencies in patch-level Gleason grading. PLoS One.
19(e0304847)2024.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Wang X, Jiang Y, Yang S, Wang F, Zhang X,
Wang W, Chen Y, Wu X, Xiang J, Li Y, et al: Foundation model for
predicting prognosis and adjuvant therapy benefit from digital
pathology in GI cancers. J Clin Oncol. 43:3468–3481.
2025.PubMed/NCBI View Article : Google Scholar
|
|
27
|
Qaiser T, Lee CY, Vandenberghe M, Yeh J,
Gavrielides MA, Hipp J, Scott M and Reischl J: Usability of deep
learning and H&E images predict disease outcome-emerging tool
to optimize clinical trials. NPJ Precis Oncol. 6(37)2022.PubMed/NCBI View Article : Google Scholar
|
|
28
|
Hsu CY, Askar S, Alshkarchy SS, Nayak PP,
Attabi KAL, Khan MA, Mayan JA, Sharma MK, Islomov S and Soleimani
Samarkhazan H: AI-driven multi-omics integration in precision
oncology: Bridging the data deluge to clinical decisions. Clin Exp
Med. 26(29)2025.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Lekadir K, Feragen A, Fofanah AJ, Frangi
AF, Buyx A, Emelie A, Lara A, Porras AR, Chan AW, Navarro A, et al:
FUTURE-AI: International consensus guideline for trustworthy and
deployable artificial intelligence in healthcare. arXiv:
2309.12325, 2023.
|
|
30
|
Elemento O, Khozin S and Sternberg CN: The
use of artificial intelligence for cancer therapeutic
decision-making. NEJM AI. 2(10.1056/aira2401164)2025.PubMed/NCBI View Article : Google Scholar
|
|
31
|
Shao J, Ma J, Zhang Q, Li W and Wang C:
Predicting gene mutation status via artificial intelligence
technologies based on multimodal integration (MMI) to advance
precision oncology. Semin Cancer Biol. 91:1–15. 2023.PubMed/NCBI View Article : Google Scholar
|
|
32
|
Awuah WA, Ben-Jaafar A, Roy S,
Nkrumah-Boateng PA, Tan JK, Abdul-Rahman T and Atallah O:
Predicting survival in malignant glioma using artificial
intelligence. Eur J Med Res. 30(61)2025.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Bera K, Braman N, Gupta A, Velcheti V and
Madabhushi A: Predicting cancer outcomes with radiomics and
artificial intelligence in radiology. Nat Rev Clin Oncol.
19:132–146. 2022.PubMed/NCBI View Article : Google Scholar
|
|
34
|
Wang S, Zhang H, Liu Z and Liu Y: A novel
deep learning method to predict lung cancer long-term survival with
biological knowledge incorporated gene expression images and
clinical data. Front Genet. 13(800853)2022.PubMed/NCBI View Article : Google Scholar
|
|
35
|
Akselrod-Ballin A, Chorev M, Shoshan Y,
Spiro A, Hazan A, Melamed R, Barkan E, Herzel E, Naor S, Karavani
E, et al: Predicting breast cancer by applying deep learning to
linked health records and mammograms. Radiology. 292:331–342.
2019.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Chiu YC, Chen HIH, Zhang T, Zhang S,
Gorthi A, Wang LJ, Huang Y and Chen Y: Predicting drug response of
tumors from integrated genomic profiles by deep neural networks.
BMC Med Genomics. 12 (Suppl 1)(S18)2019.PubMed/NCBI View Article : Google Scholar
|
|
37
|
Huynh BN, Groendahl AR, Tomic O, Liland
KH, Knudtsen IS, Hoebers F, van Elmpt W, Malinen E, Dale E and
Futsaether CM: Head and neck cancer treatment outcome prediction: A
comparison between machine learning with conventional radiomics
features and deep learning radiomics. Front Med (Lausanne).
10(1217037)2023.PubMed/NCBI View Article : Google Scholar
|
|
38
|
Luo Y, Tseng HH, Cui S, Wei L, Ten Haken
RK and El Naqa I: Balancing accuracy and interpretability of
machine learning approaches for radiation treatment outcomes
modeling. BJR Open. 1(20190021)2019.PubMed/NCBI View Article : Google Scholar
|
|
39
|
Sinha T, Khan A, Awan M, Bokhari SFH, Ali
K, Amir M, Jadhav AN, Bakht D, Puli ST and Burhanuddin M:
Artificial intelligence and machine learning in predicting the
response to immunotherapy in non-small cell lung carcinoma: A
systematic review. Cureus. 16(e61220)2024.PubMed/NCBI View Article : Google Scholar
|
|
40
|
Pesapane F, Nicosia L, D'Amelio L,
Quercioli G, Pannarale MR, Priolo F, Marinucci I, Farina MG, Penco
S, Dominelli V, et al: Artificial intelligence-driven
personalization in breast cancer screening: From population models
to individualized protocols. Cancers (Basel).
17(2901)2025.PubMed/NCBI View Article : Google Scholar
|
|
41
|
Ennab M and Mcheick H: Enhancing
interpretability and accuracy of AI models in healthcare: A
comprehensive review on challenges and future directions. Front
Robot AI. 11(1444763)2024.PubMed/NCBI View Article : Google Scholar
|
|
42
|
Niu S, Ma J, Yin Q, Wang Z, Bai L and Yang
X: Modelling patient longitudinal data for clinical decision
support: A case study on emerging AI healthcare Technologies. Inf
Syst Front. 27:409–427. 2025.
|
|
43
|
Sartori F, Codicè F, Caranzano I, Rollo C,
Birolo G, Fariselli P and Pancotti C: A comprehensive review of
deep learning applications with multi-omics data in cancer
research. Genes (Basel). 16(648)2025.PubMed/NCBI View Article : Google Scholar
|
|
44
|
Clayton EA, Pujol TA, McDonald JF and Qiu
P: Leveraging TCGA gene expression data to build predictive models
for cancer drug response. BMC Bioinformatics. 21 (Suppl
14)(S364)2020.PubMed/NCBI View Article : Google Scholar
|
|
45
|
Liu X, Song C, Huang F, Fu H, Xiao W and
Zhang W: GraphCDR: A graph neural network method with contrastive
learning for cancer drug response prediction. Brief Bioinform.
23(bbab457)2021.PubMed/NCBI View Article : Google Scholar
|
|
46
|
Ali M and Aittokallio T: Machine learning
and feature selection for drug response prediction in precision
oncology applications. Biophys Rev. 11:31–39. 2019.PubMed/NCBI View Article : Google Scholar
|
|
47
|
Kalinin AA, Higgins GA, Reamaroon N,
Soroushmehr S, Allyn-Feuer A, Dinov ID, Najarian K and Athey BD:
Deep learning in pharmacogenomics: From gene regulation to patient
stratification. Pharmacogenomics. 19:629–650. 2018.PubMed/NCBI View Article : Google Scholar
|
|
48
|
Sharifi-Noghabi H, Jahangiri-Tazehkand S,
Smirnov P, Hon C, Mammoliti A, Nair SK, Mer AS, Ester M and
Haibe-Kains B: Drug sensitivity prediction from cell line-based
pharmacogenomics data: Guidelines for developing machine learning
models. Brief Bioinform. 22(bbab294)2021.PubMed/NCBI View Article : Google Scholar
|
|
49
|
Beam AL and Kohane IS: Big data and
machine learning in health care. JAMA. 319:1317–1318.
2018.PubMed/NCBI View Article : Google Scholar
|
|
50
|
Vamathevan J, Clark D, Czodrowski P,
Dunham I, Ferran E, Lee G, Li B, Madabhushi A, Shah P, Spitzer M
and Zhao S: Applications of machine learning in drug discovery and
development. Nat Rev Drug Discov. 18:463–477. 2019.PubMed/NCBI View Article : Google Scholar
|
|
51
|
Holzinger A, Langs G, Denk H, Zatloukal K
and Müller H: Causability and explainability of artificial
intelligence in medicine. Wiley Interdiscip Rev Data Min Knowl
Discov. 9(e1312)2019.PubMed/NCBI View Article : Google Scholar
|
|
52
|
Damilakis J and Stratakis J: Descriptive
overview of AI applications in x-ray imaging and radiotherapy. J
Radiol Prot. 44(041001)2024.PubMed/NCBI View Article : Google Scholar
|
|
53
|
Psoroulas S, Paunoiu A, Corradini S,
Hörner-Rieber J and Tanadini-Lang S: MR-linac: Role of artificial
intelligence and automation. Strahlenther Onkol. 201:298–305.
2025.PubMed/NCBI View Article : Google Scholar
|
|
54
|
Smolders A, Lomax A, Weber DC and
Albertini F: Deep learning based uncertainty prediction of
deformable image registration for contour propagation and dose
accumulation in online adaptive radiotherapy. Phys Med Biol.
68(245027)2023.PubMed/NCBI View Article : Google Scholar
|
|
55
|
Chen X, Men K, Li Y, Yi J and Dai J: A
feasibility study on an automated method to generate
patient-specific dose distributions for radiotherapy using deep
learning. Med Phys. 46:56–64. 2019.PubMed/NCBI View Article : Google Scholar
|
|
56
|
Li C, Guo Y, Lin X, Feng X, Xu D and Yang
R: Deep reinforcement learning in radiation therapy planning
optimization: A comprehensive review. Phys Med.
125(104498)2024.PubMed/NCBI View Article : Google Scholar
|
|
57
|
Akpinar MH, Sengur A, Salvi M, Seoni S,
Faust O, Mir H, Molinari F and Acharya UR: Synthetic data
generation via generative adversarial networks in healthcare: A
systematic review of image- and signal-based studies. IEEE Open J
Eng Med Biol. 6:183–192. 2024.PubMed/NCBI View Article : Google Scholar
|
|
58
|
Chen Y, Clayton EW, Novak LL, Anders S and
Malin B: Human-centered design to address biases in artificial
intelligence. J Med Internet Res. 25(e43251)2023.PubMed/NCBI View Article : Google Scholar
|
|
59
|
Abràmoff MD, Tarver ME, Loyo-Berrios N,
Trujillo S, Char D, Obermeyer Z and Eydelman MB: Foundational
Principles of Ophthalmic Imaging and Algorithmic Interpretation
Working Group of the Collaborative Community for Ophthalmic Imaging
Foundation. Washington D.C..Maisel WH: Considerations for
addressing bias in artificial intelligence for health equity. NPJ
Digit Med. 6(170)2023.PubMed/NCBI View Article : Google Scholar
|
|
60
|
Tejani AS, Ng YS, Xi Y and Rayan JC:
Understanding and mitigating bias in imaging artificial
intelligence. Radiographics. 44(e230067)2024.PubMed/NCBI View Article : Google Scholar
|
|
61
|
Ross C and Swetlitz I: IBM's Watson
supercomputer recommended ‘unsafe and incorrect’ cancer treatments,
internal documents show. STAT, Boston, MA, 2018.
|
|
62
|
Strickland E: IBM Watson, heal thyself:
How IBM overpromised and underdelivered on AI health care. IEEE
Spectr. 56:24–31. 2019.
|
|
63
|
Séroussi B, Laouénan C, Gligorov J, Uzan
S, Mentré F and Bouaud J: Which breast cancer decisions remain
non-compliant with guidelines despite the use of computerised
decision support? Br J Cancer. 109:1147–1156. 2013.PubMed/NCBI View Article : Google Scholar
|
|
64
|
Jandoubi B and Akhloufi MA: Multimodal
artificial intelligence in medical diagnostics. Information.
16(591)2025.
|
|
65
|
Tak D, Garomsa BA, Chaunzwa TL,
Zapaishchykova A, Climent Pardo JC, Ye Z, Zielke J, Ravipati Y,
Vajapeyam S, Mahootiha M, et al: A foundation model for generalized
brain MRI analysis. medRxiv [Preprint]: 2024.12.02.24317992,
2024.
|
|
66
|
Yan S, Yu Z, Primiero C, Vico-Alonso C,
Wang Z, Yang L, Tschandl P, Hu M, Ju L, Tan G, et al: A multimodal
vision foundation model for clinical dermatology. Nat Med.
31:2691–2702. 2025.PubMed/NCBI View Article : Google Scholar
|
|
67
|
Ding T, Wagner SJ, Song AH, Chen RJ, Lu
MY, Zhang A, Vaidya AJ, Jaume G, Shaban M, Kim A, et al: A
multimodal whole-slide foundation model for pathology. Nat Med.
31:3749–3761. 2025.PubMed/NCBI View Article : Google Scholar
|
|
68
|
Hao R, Chang WC, Hu J and Gao M: Federated
Learning-Driven Health Risk Prediction on Electronic Health Records
Under Privacy. Constraints. Preprints: https://doi.org/10.20944/preprints202510.1471.v1.
|
|
69
|
Mu J, Kadoch M, Yuan T, Lv W, Liu Q and Li
B: Explainable federated medical image analysis through causal
learning and blockchain. IEEE J Biomed Health Inform. 28:3206–3218.
2024.PubMed/NCBI View Article : Google Scholar
|
|
70
|
Rezaeian O, Bayrak AE and Asan O:
Explainability and AI confidence in clinical decision support
systems: Effects on trust, diagnostic performance, and cognitive
load in breast cancer care. arXiv: https://doi.org/10.48550/arXiv.2501.16693.
|
|
71
|
Salimparsa M, Sedig K, Lizotte DJ,
Abdullah SS, Chalabianloo N and Muanda FT: Explainable AI for
clinical decision support systems: Literature review, key gaps, and
research synthesis. Informatics. 12(119)2025.PubMed/NCBI View Article : Google Scholar
|
|
72
|
Marey A, Ambrozaite O, Afifi A, Agarwal R,
Chellappa R, Adeleke S and Umair M: A perspective on AI
implementation in medical imaging in LMICs: Challenges, priorities,
and strategies. Eur Radiol: October 23, 2025 (Epub ahead of
print).
|
|
73
|
Kaushik A, Barcellona C, Mandyam NK, Tan
SY and Tromp J: Challenges and opportunities for data sharing
related to artificial intelligence tools in health care in low- and
middle-income countries: Systematic review and case study from
Thailand. J Med Internet Res. 27(e58338)2025.PubMed/NCBI View
Article : Google Scholar
|