|
1
|
Rojas-Carabali W, Agrawal R,
Gutierrez-Sinisterra L, Baxter SL, Cifuentes-González C, Wei YC,
Abisheganaden J, Kannapiran P, Wong S, Lee B, et al: Natural
language processing in medicine and ophthalmology: A review for the
21st-century clinician. Asia Pac J Ophthalmol (Phila).
13(100084)2024.PubMed/NCBI View Article : Google Scholar
|
|
2
|
Hirani R, Noruzi K, Khuram H, Hussaini AS,
Aifuwa EI, Ely KE, Lewis JM, Gabr AE, Smiley A, Tiwari RK and
Etienne M: Artificial intelligence and healthcare: A journey
through history, present innovations, and future possibilities.
Life (Basel). 14(557)2024.PubMed/NCBI View Article : Google Scholar
|
|
3
|
Aamir A, Iqbal A, Jawed F, Ashfaque F,
Hafsa H, Anas Z, Oduoye MO, Basit A, Ahmed S, Abdul Rauf S, et al:
Exploring the current and prospective role of artificial
intelligence in disease diagnosis. Ann Med Surg (Lond). 86:943–949.
2024.PubMed/NCBI View Article : Google Scholar
|
|
4
|
Bajwa J, Munir U, Nori A and Williams B:
Artificial intelligence in healthcare: Transforming the practice of
medicine. Future Healthc J. 8:e188–e194. 2021.PubMed/NCBI View Article : Google Scholar
|
|
5
|
Poterucha TJ, Jing L, Ricart RP,
Adjei-Mosi M, Finer J, Hartzel D, Kelsey C, Long A, Rocha D, Ruhl
JA, et al: Detecting structural heart disease from
electrocardiograms using AI. Nature. 644:221–230. 2025.PubMed/NCBI View Article : Google Scholar
|
|
6
|
Bhandari A: Revolutionizing radiology with
artificial intelligence. Cureus. 16(e72646)2024.PubMed/NCBI View Article : Google Scholar
|
|
7
|
Krishna NK, RS A and K S: Artificial
intelligence in radiology: Augmentation, not replacement. Cureus.
17(e86247)2025.PubMed/NCBI View Article : Google Scholar
|
|
8
|
Stafie CS, Sufaru IG, Ghiciuc CM, Stafie
II, Sufaru EC, Solomon SM and Hancianu M: Exploring the
intersection of artificial intelligence and clinical healthcare: A
multidisciplinary review. Diagnostics (Basel).
13(1995)2023.PubMed/NCBI View Article : Google Scholar
|
|
9
|
Langs G, Röhrich S, Hofmanninger J, Prayer
F, Pan J, Herold C and Prosch H: Machine learning: From radiomics
to discovery and routine. Radiologe. 58 (Suppl 1):S1–S6.
2018.PubMed/NCBI View Article : Google Scholar
|
|
10
|
Li X, Shen L, Xie X, Huang S, Xie Z, Hong
X and Yu J: Multi-resolution convolutional networks for chest X-ray
radiograph based lung nodule detection. Artif Intell Med.
103(101744)2020.PubMed/NCBI View Article : Google Scholar
|
|
11
|
Maiter A, Hocking K, Matthews S, Taylor J,
Sharkey M, Metherall P, Alabed S, Dwivedi K, Shahin Y, Anderson E,
et al: Evaluating the performance of artificial intelligence
software for lung nodule detection on chest radiographs in a
retrospective real-world UK population. BMJ Open.
13(e077348)2023.PubMed/NCBI View Article : Google Scholar
|
|
12
|
Wang S and Summers RM: Machine learning
and radiology. Med Image Anal. 16:933–951. 2012.PubMed/NCBI View Article : Google Scholar
|
|
13
|
Wang J, Wang T, Han R, Shi D and Chen B:
Artificial intelligence in cancer pathology: Applications,
challenges, and future directions. CytoJournal.
22(45)2025.PubMed/NCBI View Article : Google Scholar
|
|
14
|
Zhu M, Sali R, Baba F, Khasawneh H, Ryndin
M, Leveillee RJ, Hurwitz MD, Lui K, Dixon C and Zhang DY:
Artificial intelligence in pathologic diagnosis, prognosis and
prediction of prostate cancer. Am J Clin Exp Urol. 12:200–215.
2024.PubMed/NCBI View Article : Google Scholar
|
|
15
|
Bera K, Schalper KA, Rimm DL, Velcheti V
and Madabhushi A: Artificial intelligence in digital pathology-new
tools for diagnosis and precision oncology. Nat Rev Clin Oncol.
16:703–715. 2019.PubMed/NCBI View Article : Google Scholar
|
|
16
|
Ma Y, Jamdade S, Konduri L and Sailem H:
AI in histopathology explorer for comprehensive analysis of the
evolving AI landscape in histopathology. NPJ Digit Med.
8(156)2025.PubMed/NCBI View Article : Google Scholar
|
|
17
|
Jeong JJ, Tariq A, Adejumo T, Trivedi H,
Gichoya JW and Banerjee I: Systematic review of generative
adversarial networks (GANs) for medical image classification and
segmentation. J Digit Imaging. 35:137–152. 2022.PubMed/NCBI View Article : Google Scholar
|
|
18
|
Skandarani Y, Jodoin PM and Lalande A:
GANs for medical image synthesis: An empirical study. J Imaging.
9(69)2023.PubMed/NCBI View Article : Google Scholar
|
|
19
|
Behara K, Bhero E and Agee JT: AI in
dermatology: A comprehensive review into skin cancer detection.
PeerJ Comput Sci. 10(e2530)2024.PubMed/NCBI View Article : Google Scholar
|
|
20
|
Alotaibi A and AlSaeed D: Skin cancer
detection using transfer learning and deep attention mechanisms.
Diagnostics (Basel). 15(99)2025.PubMed/NCBI View Article : Google Scholar
|
|
21
|
Arshad M, Khan MA, Almujally NA, Alasiry
A, Marzougui M and Nam Y: Multiclass skin lesion classification and
localization from dermoscopic images using a novel network-level
fused deep architecture and explainable artificial intelligence.
BMC Med Inform Decis Mak. 25(215)2025.PubMed/NCBI View Article : Google Scholar
|
|
22
|
Han SS, Park I, Eun Chang S, Lim W, Kim
MS, Park GH, Chae JB, Huh CH and Na JI: Augmented intelligence
dermatology: Deep neural networks empower medical professionals in
diagnosing skin cancer and predicting treatment options for 134
skin disorders. J Invest Dermatol. 140:1753–1761. 2020.PubMed/NCBI View Article : Google Scholar
|
|
23
|
Wei ML, Tada M, So A and Torres R:
Artificial intelligence and skin cancer. Front Med (Lausanne).
11(1331895)2024.PubMed/NCBI View Article : Google Scholar
|
|
24
|
Jeong HK, Park C, Henao R and Kheterpal M:
Deep learning in dermatology: A systematic review of current
approaches, outcomes, and limitations. JID Innov.
3(100150)2022.PubMed/NCBI View Article : Google Scholar
|
|
25
|
Mevorach L, Farcomeni A, Pellacani G and
Cantisani C: A comparison of skin lesions' diagnoses between
AI-based image classification, an expert dermatologist, and a
non-expert. Diagnostics (Basel). 15(1115)2025.PubMed/NCBI View Article : Google Scholar
|
|
26
|
Brinker TJ, Hekler A, Enk AH, Klode J,
Hauschild A, Berking C, Schilling B, Haferkamp S, Schadendorf D,
Holland-Letz T, et al: Deep learning outperformed 136 of 157
dermatologists in a head-to-head dermoscopic melanoma image
classification task. Eur J Cancer. 113:47–54. 2019.PubMed/NCBI View Article : Google Scholar
|
|
27
|
Esteva A, Kuprel B, Novoa RA, Ko J,
Swetter SM, Blau HM and Thrun S: Dermatologist-level classification
of skin cancer with deep neural networks. Nature. 542:115–118.
2017.PubMed/NCBI View Article : Google Scholar
|
|
28
|
Lalmalani RM, Lim CXY and Oh CC:
Artificial intelligence in dermatopathology: A systematic review.
Clin Exp Dermatol. 50:251–259. 2025.PubMed/NCBI View Article : Google Scholar
|
|
29
|
Olawade DB, Weerasinghe K, Mathugamage
MDDE, Odetayo A, Aderinto N, Teke J and Boussios S: Enhancing
ophthalmic diagnosis and treatment with artificial intelligence.
Medicina (Kaunas). 61(433)2025.PubMed/NCBI View Article : Google Scholar
|
|
30
|
Goldhagen BE and Al-Khersan H: Diving deep
into deep learning: An update on artificial intelligence in retina.
Curr Ophthalmol Rep. 8:121–128. 2020.PubMed/NCBI View Article : Google Scholar
|
|
31
|
Abràmoff MD, Lavin PT, Birch M, Shah N and
Folk JC: Pivotal trial of an autonomous AI-based diagnostic system
for detection of diabetic retinopathy in primary care offices. NPJ
Digit Med. 1(39)2018.PubMed/NCBI View Article : Google Scholar
|
|
32
|
Rajpurkar P, Irvin J, Ball RL, Zhu K, Yang
B, Mehta H, Duan T, Ding D, Bagul A, Langlotz CP, et al: Deep
learning for chest radiograph diagnosis: A retrospective comparison
of the CheXNeXt algorithm to practicing radiologists. PLoS Med.
15(e1002686)2018.PubMed/NCBI View Article : Google Scholar
|
|
33
|
Hashim HT, Alhatemi AQM, Daraghma M, Ali
HT, Khan MA, Sulaiman FA, Ali ZH, Sahib MA, Al-Obaidi AD and
Al-Obaidi A: Artificial intelligence versus radiologists in
detecting early-stage breast cancer from mammograms: A
meta-analysis of paradigm shifts. Pol J Radiol. 90:e1–e8.
2025.PubMed/NCBI View Article : Google Scholar
|
|
34
|
Komatsu M, Teraya N, Natsume T, Harada N,
Takeda K and Hamamoto R: Clinical application of artificial
intelligence in ultrasound imaging for oncology. JMA J. 8:18–25.
2025.PubMed/NCBI View Article : Google Scholar
|
|
35
|
Alowais SA, Alghamdi SS, Alsuhebany N,
Alqahtani T, Alshaya A, Almohareb SN, Aldairem A, Alrashed M, Bin
Saleh K, Badreldin HA, et al: Revolutionizing healthcare: The role
of artificial intelligence in clinical practice. BMC Med Educ.
23(689)2023.PubMed/NCBI View Article : Google Scholar
|
|
36
|
Sjövall F, Lanckohr C and Bracht H: What's
new in therapeutic drug monitoring of antimicrobials? Intensive
Care Med. 49:857–859. 2023.PubMed/NCBI View Article : Google Scholar
|
|
37
|
Partin A, Brettin TS, Zhu Y, Narykov O,
Clyde A, Overbeek J and Stevens RL: Deep learning methods for drug
response prediction in cancer: Predominant and emerging trends.
Front Med (Lausanne). 10(1086097)2023.PubMed/NCBI View Article : Google Scholar
|
|
38
|
Han K, Cao P, Wang Y, Xie F, Ma J, Yu M,
Wang J, Xu Y, Zhang Y and Wan J: A review of approaches for
predicting drug-drug interactions based on machine learning. Front
Pharmacol. 12(814858)2022.PubMed/NCBI View Article : Google Scholar
|
|
39
|
Yuan S, Yang Z, Li J, Wu C and Liu S:
AI-powered early warning systems for clinical deterioration
significantly improve patient outcomes: A meta-analysis. BMC Med
Inform Decis Mak. 25(203)2025.PubMed/NCBI View Article : Google Scholar
|
|
40
|
Pimentel MAF, Redfern OC, Malycha J,
Meredith P, Prytherch D, Briggs J, Young JD, Clifton DA, Tarassenko
L and Watkinson PJ: Detecting deteriorating patients in the
hospital: Development and validation of a novel scoring system. Am
J Respir Crit Care Med. 204:44–52. 2021.PubMed/NCBI View Article : Google Scholar
|
|
41
|
Ye J, Woods D, Jordan N and Starren J: The
role of artificial intelligence for the application of integrating
electronic health records and patient-generated data in clinical
decision support. AMIA Jt Summits Transl Sci Proc. 2024:459–467.
2024.PubMed/NCBI
|
|
42
|
Haug CJ and Drazen JM: Artificial
intelligence and machine learning in clinical medicine. N Engl J
Med. 388:1201–1208. 2023.PubMed/NCBI View Article : Google Scholar
|
|
43
|
Abubaker Bagabir S, Ibrahim NK, Abubaker
Bagabir H and Hashem Ateeq R: Covid-19 and artificial intelligence:
Genome sequencing, drug development and vaccine discovery. J Infect
Public Health. 15:289–296. 2022.PubMed/NCBI View Article : Google Scholar
|
|
44
|
Krishnan A, Zhang R, Yao V, Theesfeld CL,
Wong AK, Tadych A, Volfovsky N, Packer A, Lash A and Troyanskaya
OG: Genome-wide prediction and functional characterization of the
genetic basis of autism spectrum disorder. Nat Neurosci.
19:1454–1462. 2016.PubMed/NCBI View Article : Google Scholar
|
|
45
|
Tran TTV, Surya Wibowo A, Tayara H and
Chong KT: Artificial intelligence in drug toxicity prediction:
Recent advances, challenges, and future perspectives. J Chem Inf
Model. 63:2628–2643. 2023.PubMed/NCBI View Article : Google Scholar
|
|
46
|
Singh DP and Kaushik B: A systematic
literature review for the prediction of anticancer drug response
using various machine-learning and deep-learning techniques. Chem
Biol Drug Des. 101:175–194. 2023.PubMed/NCBI View Article : Google Scholar
|
|
47
|
Ansari MS, Alok AK, Jain D, Rana S, Gupta
S, Salwan R and Venkatesh S: Predictive model based on health data
analysis for risk of readmission in disease-specific cohorts.
Perspect Health Inf Manag. 18(Spring)(1j)2021.PubMed/NCBI
|
|
48
|
Crossnohere NL, Elsaid M, Paskett J,
Bose-Brill S and Bridges JFP: Guidelines for artificial
intelligence in medicine: Literature review and content analysis of
frameworks. J Med Internet Res. 24(e36823)2022.PubMed/NCBI View Article : Google Scholar
|
|
49
|
Barmaz Y and Ménard T: Bayesian modeling
for the detection of adverse events underreporting in clinical
trials. Drug Saf. 44:949–955. 2021.PubMed/NCBI View Article : Google Scholar
|
|
50
|
Wang Y, Coiera E and Magrabi F: Can
unified medical language system-based semantic representation
improve automated identification of patient safety incident reports
by type and severity? J Am Med Inform Assoc. 27:1502–1509.
2020.PubMed/NCBI View Article : Google Scholar
|
|
51
|
De Micco F, Di Palma G, Ferorelli D, De
Benedictis A, Tomassini L, Tambone V, Cingolani M and Scendoni R:
Artificial intelligence in healthcare: Transforming patient safety
with intelligent systems-A systematic review. Front Med.
11(1522554)2025.PubMed/NCBI View Article : Google Scholar
|
|
52
|
Corny J, Rajkumar A, Martin O, Dode X,
Lajonchère JP, Billuart O, Bézie Y and Buronfosse A: A machine
learning-based clinical decision support system to identify
prescriptions with a high risk of medication error. J Am Med Inform
Assoc. 27:1688–1694. 2020.PubMed/NCBI View Article : Google Scholar
|
|
53
|
Zheng Y, Rowell B, Chen Q, Kim JY, Kontar
RA, Yang XJ and Lester CA: Designing human-centered AI to prevent
medication dispensing errors: Focus group study with pharmacists.
JMIR Form Res. 7(e51921)2023.PubMed/NCBI View
Article : Google Scholar
|
|
54
|
Gan TRX, Tan LWJ, Egermark M, Truong ATL,
Kumar K, Tan SB, Tang S, Blasiak A, Goh BC, Ngiam KY and Ho D:
AI-assisted warfarin dose optimisation with CURATE.AI for clinical
impact: Retrospective data analysis. Bioeng Transl Med.
10(e10757)2025.PubMed/NCBI View Article : Google Scholar
|
|
55
|
Tan BKJ, Teo CB, Tadeo X, Peng S, Soh HPL,
Du SX, Luo VWY, Bandla A, Sundar R, Ho D, Kee TW and Blasia A:
Personalised, rational, efficacy-driven cancer drug dosing via an
artificial intelligence system (PRECISE): A protocol for the
PRECISE CURATE.AI pilot clinical trial. Front Digit Health.
3(635524)2021.PubMed/NCBI View Article : Google Scholar
|
|
56
|
Wah JNK: Revolutionizing surgery: AI and
robotics for precision, risk reduction, and innovation. J Robot
Surg. 19(47)2025.PubMed/NCBI View Article : Google Scholar
|
|
57
|
Hashimoto DA, Rosman G, Rus D and Meireles
OR: Artificial intelligence in surgery: Promises and perils. Ann
Surg. 268:70–76. 2018.PubMed/NCBI View Article : Google Scholar
|
|
58
|
Cizmic A, Mitra AT, Preukschas AA, Kemper
M, Melling NT, Mann O, Markar S, Hackert T and Nickel F: Artificial
intelligence for intraoperative video analysis in robotic-assisted
esophagectomy. Surg Endosc. 39:2774–2783. 2025.PubMed/NCBI View Article : Google Scholar
|
|
59
|
Esposito C, Masieri L, Di Mento C, Cerulo
M, Del Conte F, Coppola V, Esposito G, Tedesco F, Chiodi A,
Carraturo F, et al: Seven years of pediatric robotic-assisted
surgery: insights from 105 procedures. J Robot Surg.
19(157)2025.PubMed/NCBI View Article : Google Scholar
|
|
60
|
Xiao X, Wang X, Meng B, Pan X and Zhao H:
Comparison of robotic AI-assisted and manual pedicle screw fixation
for treating thoracolumbar fractures: A retrospective controlled
trial. Front Bioeng Biotechnol. 13(1491775)2025.PubMed/NCBI View Article : Google Scholar
|
|
61
|
Guni A, Varma P, Zhang J, Fehervari M and
Ashrafian H: Artificial intelligence in surgery: The future is now.
Eur Surg Res: Jan 22, 2024 (Epub ahead of print).
|
|
62
|
Reismann J, Romualdi A, Kiss N, Minderjahn
MI, Kallarackal J, Schad M and Reismann M: Diagnosis and
classification of pediatric acute appendicitis by artificial
intelligence methods: An investigator-independent approach. PLoS
One. 14(e0222030)2019.PubMed/NCBI View Article : Google Scholar
|
|
63
|
El Hechi MW, Maurer LR, Levine J, Zhuo D,
El Moheb M, Velmahos GC, Dunn J, Bertsimas D and Kaafarani HM:
Validation of the artificial intelligence-based predictive optimal
trees in emergency surgery risk (POTTER) calculator in emergency
general surgery and emergency laparotomy patients. J Am Coll Surg.
232:912–919.e1. 2021.PubMed/NCBI View Article : Google Scholar
|
|
64
|
Abbasi N and Hussain HK: Integration of
artificial intelligence and smart technology: AI-driven robotics in
surgery: Precision and efficiency. Journal of Artificial
Intelligence General Science (JAIGS) 5: 381-390. 2024. https://doi.org/10.60087/jaigs.v5i1.207.
|
|
65
|
Urrea C, Garcia-Garcia Y, Kern J and
Rodriguez-Guillen R: Neuro-visual adaptive control for precision in
robot-assisted surgery. Technologies. 13(135)2025.
|
|
66
|
Zhou XY, Guo Y, Shen M and Yang GZ:
Application of artificial intelligence in surgery. Front Med.
14:417–430. 2020.PubMed/NCBI View Article : Google Scholar
|
|
67
|
Rank N, Pfahringer B, Kempfert J, Stamm C,
Kühne T, Schoenrath F, Falk V, Eickhoff C and Meyer A:
Deep-learning-based real-time prediction of acute kidney injury
outperforms human predictive performance. NPJ Digit Med.
3(139)2020.PubMed/NCBI View Article : Google Scholar
|
|
68
|
Ismail Fawaz H, Forestier G, Weber J,
Idoumghar L and Muller P: Accurate and interpretable evaluation of
surgical skills from kinematic data using fully convolutional
neural networks. Int J Comput Assist Radiol Surg. 14:1611–1617.
2019.PubMed/NCBI View Article : Google Scholar
|
|
69
|
Han J, Davids J, Ashrafian H, Darzi A,
Elson DS and Sodergren M: A systematic review of robotic surgery:
From supervised paradigms to fully autonomous robotic approaches.
Int J Med Robot. 18(e2358)2022.PubMed/NCBI View Article : Google Scholar
|
|
70
|
Wang J, Zhang L, Huang Y, Zhao J and Bella
F: Safety of autonomous vehicles. J Adv Transp: Oct 6, 2020 (Epub
ahead of print).
|
|
71
|
Connor MJ, Dasgupta P, Ahmed HU and Raza
A: Autonomous surgery in the era of robotic urology: Friend or foe
of the future surgeon? Nat Rev Urol. 17:643–649. 2020.PubMed/NCBI View Article : Google Scholar
|
|
72
|
Madani A, Namazi B, Altieri MS, Hashimoto
DA, Rivera AM, Pucher PH, Navarrete-Welton A, Sankaranarayanan G,
Brunt LM, Okrainec A and Alseidi A: Artificial intelligence for
intraoperative guidance: Using semantic segmentation to identify
surgical anatomy during laparoscopic cholecystectomy. Ann Surg.
276:363–369. 2022.PubMed/NCBI View Article : Google Scholar
|
|
73
|
Garcia Nespolo R, Yi D, Cole E, Valikodath
N, Luciano C and Leiderman YI: Evaluation of artificial
intelligence-based intraoperative guidance tools for
phacoemulsification cataract surgery. JAMA Ophthalmol. 140:170–177.
2022.PubMed/NCBI View Article : Google Scholar
|
|
74
|
Li A, Javidan AP, Namazi B, Madani A and
Forbes TL: Development of an artificial intelligence tool for
intraoperative guidance during endovascular abdominal aortic
aneurysm repair. Ann Vasc Surg. 99:96–104. 2023.PubMed/NCBI View Article : Google Scholar
|
|
75
|
Patel RJ, Lee AM, Hallsten J, Lane JS,
Barleben AR and Malas MB: Use of surgical augmented intelligence
maps can reduce radiation and improve safety in the endovascular
treatment of complex aortic aneurysms. J Vasc Surg. 77:982–990.e2.
2023.PubMed/NCBI View Article : Google Scholar
|
|
76
|
Choi JH, Diab A, Tsay K, Kuruvilla D,
Ganam S, Saad A, Docimo S Jr, Sujka JA and DuCoin CG: The evidence
behind robot-assisted abdominopelvic surgery: A meta-analysis of
randomized controlled trials. Surg Endosc. 38:2371–2382.
2024.PubMed/NCBI View Article : Google Scholar
|
|
77
|
Wen R, Zheng K, Zhang Q, Zhou L, Liu Q, Yu
G, Gao X, Hao L, Lou Z and Zhang W: Machine learning-based random
forest predicts anastomotic leakage after anterior resection for
rectal cancer. J Gastrointest Oncol. 12:921–932. 2021.PubMed/NCBI View Article : Google Scholar
|
|
78
|
Azimi K, Honaker MD, Chalil Madathil S and
Khasawneh MT: Post-operative infection prediction and risk factor
analysis in colorectal surgery using data mining techniques: A
pilot study. Surg Infect (Larchmt). 21:784–792. 2020.PubMed/NCBI View Article : Google Scholar
|
|
79
|
Shahi A, Bajaj G, GolharSathawane R,
Mendhe D and Dogra A: Integrating robot-assisted surgery and AI for
improved healthcare outcomes. In: Proc 2024 Ninth Int Conf Sci
Technol Eng Math (ICONSTEM). IEEE, Piscataway, NY, pp1-5, 2024.
|
|
80
|
Lai TJ, Heggie R, Kamaruzaman HF, Bouttell
J and Boyd K: Economic evaluations of robotic-assisted surgery:
methods, challenges and opportunities. Appl Health Econ Health
Policy. 23:35–49. 2025.PubMed/NCBI View Article : Google Scholar
|
|
81
|
Salam A, Wireko AA, Jiffry R, Ng JC, Patel
H, Zahid MJ, Mehta A, Huang H, Abdul-Rahman T and Isik A: The
impact of natural disasters on healthcare and surgical services in
low- and middle-income countries. Ann Med Surg (Lond).
85:3774–3777. 2023.PubMed/NCBI View Article : Google Scholar
|
|
82
|
Bellos T, Manolitsis I, Katsimperis S,
Juliebø-Jones P, Feretzakis G, Mitsogiannis I, Varkarakis I, Somani
BK and Tzelves L: Artificial intelligence in urologic robotic
oncologic surgery: A narrative review. Cancers (Basel).
16(1775)2024.PubMed/NCBI View Article : Google Scholar
|
|
83
|
Asciak L, Kyeremeh J, Luo X, Kazakidi A,
Connolly P, Picard F, O'Neill K, Tsaftaris SA, Stewart GD and Shu
W: Digital twin assisted surgery, concept, opportunities, and
challenges. NPJ Digit Med. 8(32)2025.PubMed/NCBI View Article : Google Scholar
|
|
84
|
Knudsen JE, Ghaffar U, Ma R and Hung AJ:
Clinical applications of artificial intelligence in robotic
surgery. J Robot Surg. 18(102)2024.PubMed/NCBI View Article : Google Scholar
|
|
85
|
Wah JNK: The rise of robotics and
AI-assisted surgery in modern healthcare. J Robot Surg.
19(311)2025.PubMed/NCBI View Article : Google Scholar
|
|
86
|
Asada T, Simon CZ, Lu AZ, Adida S, Dupont
M, Parel PM, Zhang J, Bhargava S, Morse KW, Dowdell JE, et al:
Robot-navigated pedicle screw insertion can reduce intraoperative
blood loss and length of hospital stay: Analysis of 1,633 patients
utilizing propensity score matching. Spine J. 24:118–124.
2024.PubMed/NCBI View Article : Google Scholar
|
|
87
|
Battaglia E, Boehm J, Zheng Y, Jamieson
AR, Gahan J and Majewicz Fey A: Rethinking autonomous surgery:
Focusing on enhancement over autonomy. Eur Urol Focus. 7:696–705.
2021.PubMed/NCBI View Article : Google Scholar
|
|
88
|
Nkenguye W: Artificial intelligence in
surgical care for low- and middle-income countries: Challenges,
opportunities, and the path forward. Surg Pract Sci.
22(100290)2025.PubMed/NCBI View Article : Google Scholar
|
|
89
|
Chepkoech M, Malila B and Mwangama J:
Telementoring for surgical training in low-resource settings: A
systematic review of current systems and the emerging role of 5G,
AI, and XR. J Robot Surg. 19(525)2025.PubMed/NCBI View Article : Google Scholar
|
|
90
|
Takeuchi M and Kitagawa Y: Artificial
intelligence and surgery. Ann Gastroenterol Surg. 8:4–5.
2023.PubMed/NCBI View Article : Google Scholar
|
|
91
|
Min Z, Lai J and Ren H: Innovating
robot-assisted surgery through large vision models. Nat Rev Electr
Eng. 2:350–363. 2025.
|
|
92
|
Lee S, Chu Y, Ryu J, Park YJ, Yang S and
Koh SB: Artificial intelligence for detection of
cardiovascular-related diseases from wearable devices: A systematic
review and meta-analysis. Yonsei Med J. 63 (Suppl):S93–S107.
2022.PubMed/NCBI View Article : Google Scholar
|
|
93
|
Patel PM, Green M, Tram J, Wang E, Murphy
MZ, Abd-Elsayed A and Chakravarthy K: Beyond the pain management
clinic: The role of AI-integrated remote patient monitoring in
chronic disease management-a narrative review. J Pain Res.
17:4223–4237. 2024.PubMed/NCBI View Article : Google Scholar
|
|
94
|
Siranart N, Deepan N, Techasatian W,
Phutinart S, Sowalertrat W, Kaewkanha P, Pajareya P, Tokavanich N,
Prasitlumkum N and Chokesuwattanaskul R: Diagnostic accuracy of
artificial intelligence in detecting left ventricular hypertrophy
by electrocardiograph: A systematic review and meta-analysis. Sci
Rep. 14(15882)2024.PubMed/NCBI View Article : Google Scholar
|
|
95
|
He B, Kwan AC, Cho JH, Yuan N, Pollick C,
Shiota T, Ebinger J, Bello NA, Wei J, Josan K, et al: Blinded,
randomized trial of sonographer versus AI cardiac function
assessment. Nature. 616:520–524. 2023.PubMed/NCBI View Article : Google Scholar
|
|
96
|
Jonas R, Earls J, Marques H, Chang HJ,
Choi JH, Doh JH, Her AY, Koo BK, Nam CW, Park HB, et al:
Relationship of age, atherosclerosis and angiographic stenosis
using artificial intelligence. Open Heart.
8(e001832)2021.PubMed/NCBI View Article : Google Scholar
|
|
97
|
Cho Y, Yoon M, Kim J, Lee JH, Oh IY, Lee
CJ, Kang SM and Choi DJ: Artificial intelligence-based
electrocardiographic biomarker for outcome prediction in patients
with acute heart failure: prospective cohort study. J Med Internet
Res. 26(e52139)2024.PubMed/NCBI View
Article : Google Scholar
|
|
98
|
Maille B, Wilkin M, Million M, Resseguier
N, Franceschi F, Koutbi-Franceschi L, Hourdain J, Martinez E,
Zabern M, Gardella C, et al: Smartwatch electrocardiogram and
artificial intelligence for assessing cardiac-rhythm safety of drug
therapy in the COVID-19 pandemic. The QT-logs study. Int J Cardiol.
331:333–339. 2021.PubMed/NCBI View Article : Google Scholar
|
|
99
|
Van der Harst P and Nathoe H: Transforming
cardiology with AI: The eko CORE 500 digital stethoscope. Neth
Heart J. 33:141–142. 2025.PubMed/NCBI View Article : Google Scholar
|
|
100
|
Backhaus SJ, Aldehayat H, Kowallick JT,
Evertz R, Lange T, Kutty S, Bigalke B, Gutberlet M, Hasenfuß G,
Thiele H, et al: Artificial intelligence fully automated myocardial
strain quantification for risk stratification following acute
myocardial infarction. Sci Rep. 12(12220)2022.PubMed/NCBI View Article : Google Scholar
|
|
101
|
Kolk MZH, Ruipérez-Campillo S, Wilde AAM,
Knops RE, Narayan SM and Tjong FVY: Prediction of sudden cardiac
death using artificial intelligence: Current status and future
directions. Heart Rhythm. 22:756–766. 2025.PubMed/NCBI View Article : Google Scholar
|
|
102
|
Kim Y, Yoon HJ, Suh J, Kang SH, Lim YH,
Jang DH, Park JH, Shin ES, Bae JW, Lee JH, et al: Artificial
intelligence-based fully automated quantitative coronary
angiography vs optical coherence tomography-guided PCI: The FLASH
trial. JACC Cardiovasc Interv. 18:187–197. 2024.PubMed/NCBI View Article : Google Scholar
|
|
103
|
Lee H, Kim HJ, Chang HW, Kim DJ, Mo J and
Kim JE: Development of a system to support warfarin dose decisions
using deep neural networks. Sci Rep. 11(14745)2021.PubMed/NCBI View Article : Google Scholar
|
|
104
|
Gautam N, Ghanta SN, Mueller J, Mansour M,
Chen Z, Puente C, Ha YM, Tarun T, Dhar G, Sivakumar K, et al:
Artificial intelligence, wearables and remote monitoring for heart
failure: Current and future applications. Diagnostics.
12(2964)2022.PubMed/NCBI View Article : Google Scholar
|
|
105
|
Cheema BS, Walter J, Narang A and Thomas
JD: Artificial intelligence-enabled POCUS in the COVID-19 ICU: A
New Spin on Cardiac Ultrasound. JACC Case Rep. 3:258–263.
2021.PubMed/NCBI View Article : Google Scholar
|
|
106
|
Deisenhofer I, Albenque JP, Busch S,
Gitenay E, Mountantonakis SE, Roux A, Horvilleur J, Bakouboula B,
Oza S, Abbey S, et al: Artificial intelligence for individualized
treatment of persistent atrial fibrillation: A randomized
controlled trial. Nat Med. 31:1286–1293. 2025.PubMed/NCBI View Article : Google Scholar
|
|
107
|
U.S. Food, Drug Administration. 510(k)
premarket notification: EchoGo Core (K191171). Food and Drug
Administration, 2019. Silver Spring, Maryland. Available from:
https://www.accessdata.fda.gov/scripts/cdrh/cfdocs/cfpmn/pmn.cfm?ID=K191171.
|
|
108
|
Mahdavi M, Thomas N, Flood C, Stewart-Lord
A, Baillie L, Grisan E, Callaghan P, Panayotova R, Hothi SS,
Griffith V, et al: Evaluating artificial intelligence-driven stress
echocardiography analysis system (EASE study): A mixed method
study. BMJ Open. 14(e079617)2024.PubMed/NCBI View Article : Google Scholar
|
|
109
|
Shapiro J, Reichard A and Muck PE: New
diagnostic tools for pulmonary embolism detection. Methodist
Debakey Cardiovasc J. 20:5–12. 2024.PubMed/NCBI View Article : Google Scholar
|
|
110
|
U.S. Food, Drug Administration. Artificial
Intelligence-Enabled Medical Devices. Food and Drug Administration,
2025. Silver Spring, Maryland. Available from: https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices.
|
|
111
|
Laletin V, Ayobi A, Chang PD, Chow DS,
Soun JE, Junn JC, Scudeler M, Quenet S, Tassy M, Avare C, et al:
Diagnostic performance of a deep learning-powered application for
aortic dissection triage prioritization and classification.
Diagnostics (Basel). 14(1877)2024.PubMed/NCBI View Article : Google Scholar
|
|
112
|
Chinnaiyan KM, Akasaka T, Amano T, Bax JJ,
Blanke P, De Bruyne B, Kawasaki T, Leipsic J, Matsuo H, Morino Y,
et al: Rationale, design and goals of the HeartFlow assessing
diagnostic value of non-invasive FFRCT in Coronary Care (ADVANCE)
registry. J Cardiovasc Comput Tomogr. 11:62–67. 2017.PubMed/NCBI View Article : Google Scholar
|
|
113
|
El Arab RA, Abu-Mahfouz MS, Abuadas FH,
Alzghoul H, Almari M, Ghannam A and Seweid MM: Bridging the gap:
From AI success in clinical trials to real-world healthcare
implementation-a narrative review. Healthcare (Basel).
13(701)2025.PubMed/NCBI View Article : Google Scholar
|
|
114
|
Jeong J, Kim S, Pan L, Hwang D, Kim D,
Choi J, Kwon Y, Yi P, Jeong J and Yoo SJ: Reducing the workload of
medical diagnosis through artificial intelligence: A narrative
review. Medicine (Baltimore). 104(e41470)2025.PubMed/NCBI View Article : Google Scholar
|
|
115
|
Johnson KB, Wei WQ, Weeraratne D, Frisse
ME, Misulis K, Rhee K, Zhao J and Snowdon JL: Precision medicine,
AI, and the future of personalized health care. Clin Transl Sci.
14:86–93. 2021.PubMed/NCBI View Article : Google Scholar
|
|
116
|
Jundaeng J, Chamchong R and Nithikathkul
C: Artificial intelligence-powered innovations in periodontal
diagnosis: A new era in dental healthcare. Front Med Technol.
6(1469852)2025.PubMed/NCBI View Article : Google Scholar
|
|
117
|
Daemi A, Kalami S, Tahiraga RG,
Ghanbarpour O, Barghani MRR, Hooshiar MH, Özbolat G and Yönden Z:
Revolutionizing personalized medicine using artificial
intelligence: A meta-analysis of predictive diagnostics and their
impacts on drug development. Clin Exp Med. 25(255)2025.PubMed/NCBI View Article : Google Scholar
|
|
118
|
Wei Q, Pan S, Liu X, Hong M, Nong C and
Zhang W: The integration of AI in nursing: Addressing current
applications, challenges, and future directions. Front Med
(Lausanne). 12(1545420)2025.PubMed/NCBI View Article : Google Scholar
|
|
119
|
Ni Y and Jia F: A scoping review of
AI-driven digital interventions in mental health care: Mapping
applications across screening, support, monitoring, prevention, and
clinical education. Healthcare (Basel). 13(1205)2025.PubMed/NCBI View Article : Google Scholar
|
|
120
|
Bhuyan SS, Sateesh V, Mukul N, Galvankar
A, Mahmood A, Nauman M, Rai A, Bordoloi K, Basu U and Samuel J:
Generative artificial intelligence use in healthcare: Opportunities
for clinical excellence and administrative efficiency. J Med Syst.
49(10)2025.PubMed/NCBI View Article : Google Scholar
|
|
121
|
Villanueva-Miranda I, Xiao G and Xie Y:
Artificial intelligence in early warning systems for infectious
disease surveillance: A systematic review. Front Public Health.
13(1609615)2025.PubMed/NCBI View Article : Google Scholar
|
|
122
|
Lavoie-Gagne O, Woo JJ, Williams RJ III,
Nwachukwu BU, Kunze KN and Ramkumar PN: Artificial intelligence as
a tool to mitigate administrative burden, optimize billing, reduce
insurance- and credentialing-related expenses, and improve quality
assurance within health care systems. Arthroscopy. 41:3270–3275.
2025.PubMed/NCBI View Article : Google Scholar
|
|
123
|
Kakatum Rao S, Gupta P, Mohammed A, Zakhmi
K, Ranjan Mohanty M and Prasad Jalaja P: The impact of artificial
intelligence on financial systems in healthcare: A systematic
review of economic evaluation studies. Cureus.
17(e86279)2025.PubMed/NCBI View Article : Google Scholar
|
|
124
|
Mittelstadt BD and Floridi L: The ethics
of big data: Current and foreseeable issues in biomedical contexts.
Sci Eng Ethics. 22:303–341. 2016.PubMed/NCBI View Article : Google Scholar
|
|
125
|
Drew BJ, Harris P, Zègre-Hemsey JK,
Mammone T, Schindler D, Salas-Boni R, Bai Y, Tinoco A, Ding Q and
Hu X: Insights into the problem of alarm fatigue with physiologic
monitor devices: A comprehensive observational study of consecutive
intensive care unit patients. PLoS One. 9(e110274)2014.PubMed/NCBI View Article : Google Scholar
|
|
126
|
Rajkomar A, Hardt M, Howell MD, Corrado G
and Chin MH: . Ensuring fairness in machine learning to advance
health equity. Ann Intern Med. 169:866–872. 2018.PubMed/NCBI View Article : Google Scholar
|
|
127
|
Char DS, Shah NH and Magnus D:
Implementing machine learning in health care-addressing ethical
challenges. N Engl J Med. 378:981–983. 2018.PubMed/NCBI View Article : Google Scholar
|
|
128
|
Božić V: Artificial Intelligence and
Waiting Lists in Hospitals. ResearchGate, Berlin, 2023.
|
|
129
|
Jiang L, Wu Z, Xu X, Zhan Y, Jin X, Wang L
and Qiu Y: Opportunities and challenges of artificial intelligence
in the medical field: Current application, emerging problems, and
problem-solving strategies. J Int Med Res.
49(3000605211000157)2021.PubMed/NCBI View Article : Google Scholar
|
|
130
|
Basu T, Engel-Wolf S and Menzer O: The
ethics of machine learning in medical sciences: Where do we stand
today? Indian J Dermatol. 65:358–364. 2020.PubMed/NCBI View Article : Google Scholar
|
|
131
|
SFR-IA Group; CERF; French Radiology
Community. Artificial intelligence and medical imaging 2018: French
radiology community white paper. Diagn Interv Imaging. 99:727–742.
2018.PubMed/NCBI View Article : Google Scholar
|
|
132
|
Nabi J: How bioethics can shape artificial
intelligence and machine learning. Hastings Cent Rep. 48:10–13.
2018.PubMed/NCBI View Article : Google Scholar
|
|
133
|
Chinta SV, Wang Z, Palikhe A, Zhang X,
Kashif A, Smith MA, Liu J and Zhang W: AI-driven healthcare:
Fairness in AI healthcare: A survey. PLoS Digit Health.
4(e0000864)2025.PubMed/NCBI View Article : Google Scholar
|
|
134
|
Mitchell C and Ploem C: Legal challenges
for the implementation of advanced clinical digital decision
support systems in Europe. J Clin Transl Res. 3 (Suppl
3):S424–S430. 2018.PubMed/NCBI
|
|
135
|
Geis JR, Brady AP, Wu CC, Spencer J,
Ranschaert E, Jaremko JL, Langer SG, Borondy Kitts A, Birch J,
Shields WF, et al: Ethics of artificial intelligence in radiology:
Summary of the joint European and North American multisociety
statement. Radiology. 293:436–440. 2019.PubMed/NCBI View Article : Google Scholar
|
|
136
|
Ooi SKG, Makmur A, Soon AYQ, Fook-Chong S,
Liew C, Sia SY, Ting YH and Lim CY: . Attitudes toward artificial
intelligence in radiology with learner needs assessment within
radiology residency programmes: A national multi-programme survey.
Singapore Med J. 62:126–134. 2021.PubMed/NCBI View Article : Google Scholar
|
|
137
|
Marks M and Haupt CE: AI chatbots, health
privacy, and challenges to HIPAA compliance. JAMA. 330:309–310.
2023.PubMed/NCBI View Article : Google Scholar
|
|
138
|
McCartney M: Margaret McCartney: AI in
medicine must be rigorously tested. BMJ. 361(k1752)2018.PubMed/NCBI View Article : Google Scholar
|
|
139
|
Golbus JR, Price WN II and Nallamothu BK:
Privacy gaps for digital cardiology data: Big problems with big
data. Circulation. 141:613–615. 2020.PubMed/NCBI View Article : Google Scholar
|
|
140
|
Natali C, Marconi L, Dias Duran LD,
Miglioretti M and Cabitza F: AI-induced deskilling in medicine: A
mixed method literature review for setting a new research agenda.
Artif Intell Rev. 58(356)2025.
|
|
141
|
Ademola A, George C and Mapp G: Addressing
the interoperability of electronic health records: the technical
and semantic interoperability, preserving privacy and security
framework. Appl Syst Innov. 7(116)2024.
|
|
142
|
Pham T: Ethical and legal considerations
in healthcare AI: Innovation and policy for safe and fair use. R
Soc Open Sci. 12(241873)2025.PubMed/NCBI View Article : Google Scholar
|
|
143
|
Braun M, Hummel P, Beck S and Dabrock P:
Primer on an ethics of AI-based decision support systems in the
clinic. J Med Ethics. 47(e3)2020.PubMed/NCBI View Article : Google Scholar
|
|
144
|
Froicu EM, Creangă-Murariu I, Afrăsânie
VA, Gafton B, Alexa-Stratulat T, Miron L, Pușcașu DM, Poroch V,
Bacoanu G, Radu I and Marinca MV: Artificial intelligence and
decision-making in oncology: A review of ethical, legal, and
informed consent challenges. Curr Oncol Rep. 27:1002–1012.
2025.PubMed/NCBI View Article : Google Scholar
|
|
145
|
Farhud DD and Zokaei S: Ethical issues of
artificial intelligence in medicine and healthcare. Iran J Public
Health. 50:i–v. 2021.PubMed/NCBI View Article : Google Scholar
|
|
146
|
Weiner EB, Dankwa-Mullan I, Nelson WA and
Hassanpour S: Ethical challenges and evolving strategies in the
integration of artificial intelligence into clinical practice. PLoS
Digit Health. 4(e0000810)2025.PubMed/NCBI View Article : Google Scholar
|
|
147
|
Hasanzadeh F, Josephson CB, Waters G,
Adedinsewo D, Azizi Z and White JA: Bias recognition and mitigation
strategies in artificial intelligence healthcare applications. NPJ
Digit Med. 8(154)2025.PubMed/NCBI View Article : Google Scholar
|
|
148
|
Chau M, Rahman MG and Debnath T: From
black box to clarity: Strategies for effective AI informed consent
in healthcare. Artif Intell Med. 103(103169)2025.PubMed/NCBI View Article : Google Scholar
|
|
149
|
Savulescu J, Giubilini A, Vandersluis R
and Mishra A: Ethics of artificial intelligence in medicine.
Singapore Med J. 65:150–158. 2024.PubMed/NCBI View Article : Google Scholar
|
|
150
|
Kiseleva A, Kotzinos D and De Her P:
Transparency of AI in healthcare as a multilayered system of
accountabilities: between legal requirements and technical
limitations. Front Artif Intell. 5(879603)2022.PubMed/NCBI View Article : Google Scholar
|
|
151
|
Cross JL, Choma MA and Onofrey JA: Bias in
medical AI: implications for clinical decision-making. PLOS Digit
Health. 3(e0000651)2024.PubMed/NCBI View Article : Google Scholar
|
|
152
|
Koçak B, Ponsiglione A, Stanzione A,
Bluethgen C, Santinha J, Ugga L, Huisman M, Klontzas ME, Cannella R
and Cuocolo R: Bias in artificial intelligence for medical imaging:
Fundamentals, detection, avoidance, mitigation, challenges, ethics,
and prospects. Diagn Interv Radiol. 31:75–88. 2025.PubMed/NCBI View Article : Google Scholar
|
|
153
|
Nasir M, Siddiqui K and Ahmed S:
Ethical-legal implications of AI-powered healthcare in critical
perspective. Front Artif Intell. 8(1619463)2025.PubMed/NCBI View Article : Google Scholar
|
|
154
|
Naik N, Hameed BMZ, Shetty DK, Swain D,
Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K, et al:
Legal and ethical consideration in artificial intelligence in
healthcare: Who takes responsibility? Front Surg.
9(862322)2022.PubMed/NCBI View Article : Google Scholar
|
|
155
|
Nouis SC, Uren V and Jariwala S:
Evaluating accountability, transparency, and bias in AI-assisted
healthcare decision-making: A qualitative study of healthcare
professionals' perspectives in the UK. BMC Med Ethics.
26(89)2025.PubMed/NCBI View Article : Google Scholar
|
|
156
|
Elendu C, Amaechi DC, Elendu TC, Jingwa
KA, Okoye OK, John Okah M, Ladele JA, Farah AH and Alimi HA:
Ethical implications of AI and robotics in healthcare: A review.
Medicine (Baltimore). 102(e36671)2023.PubMed/NCBI View Article : Google Scholar
|
|
157
|
Mennella C, Maniscalco U, De Pietro G and
Esposito M: Ethical and regulatory challenges of AI technologies in
healthcare: A narrative review. Heliyon. 10(e26297)2024.PubMed/NCBI View Article : Google Scholar
|
|
158
|
Solaiman B and Dimitropoulos G: The legal
considerations of AI-blockchain for securing health data. In:
Research Handbook on Health, AI and the Law, pp130-149, 2024.
|
|
159
|
Karim MR, Islam T, Shajalal M, Beyan O,
Lange C, Cochez M, Rebholz-Schuhmann D and Decker S: Explainable AI
for bioinformatics: Methods, tools and applications. Brief
Bioinform. 24(bbad236)2023.PubMed/NCBI View Article : Google Scholar
|
|
160
|
Muralidharan V, Adewale BA, Huang CJ, Nta
MT, Ademiju PO, Pathmarajah P, Hang MK, Adesanya O, Abdullateef RO,
Babatunde AO, et al: A scoping review of reporting gaps in
FDA-approved AI medical devices. Npj Digit Med.
7(273)2024.PubMed/NCBI View Article : Google Scholar
|
|
161
|
Varghese C, Harrison EM, O'Grady G and
Topol EJ: Artificial intelligence in surgery. Nat Med.
30:1257–1268. 2024.PubMed/NCBI View Article : Google Scholar
|
|
162
|
Dembrower K, Crippa A, Colón E, Eklund M
and Strand F: ScreenTrustCAD Trial Consortium. Artificial
intelligence for breast cancer detection in screening mammography
in Sweden: A prospective, population-based, paired-reader,
non-inferiority study. Lancet Digit Health. 5:e703–e711.
2023.PubMed/NCBI View Article : Google Scholar
|
|
163
|
Lång K, Josefsson V, Larsson AM, Larsson
S, Högberg C, Sartor H, Hofvind S, Andersson I and Rosso A:
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. 24:936–944. 2023.PubMed/NCBI View Article : Google Scholar
|
|
164
|
Mitsala A, Tsalikidis C, Pitiakoudis M,
Simopoulos C and Tsaroucha AK: Artificial intelligence in
colorectal cancer screening, diagnosis and treatment: A new Era.
Curr Oncol. 28:1581–1607. 2021.PubMed/NCBI View Article : Google Scholar
|
|
165
|
Schreuder A, Scholten ET, Van Ginneken B
and Jacobs C: Artificial intelligence for detection and
characterization of pulmonary nodules in lung cancer CT screening:
Ready for practice? Transl Lung Cancer Res. 10:2378–2388.
2021.PubMed/NCBI View Article : Google Scholar
|
|
166
|
Dembrower K, Wåhlin E, Liu Y, Salim M,
Smith K, Lindholm P, Eklund M and Strand F: Effect of artificial
intelligence-based triaging of breast cancer screening mammograms
on cancer detection and radiologist workload: A retrospective
simulation study. Lancet Digit Health. 2:e468–e474. 2020.PubMed/NCBI View Article : Google Scholar
|
|
167
|
Cai Z, Poulos RC, Liu J and Zhong Q:
Machine learning for multi-omics data integration in cancer.
iScience. 25(103798)2022.PubMed/NCBI View Article : Google Scholar
|
|
168
|
Tao K, Bian Z, Zhang Q, Guo X, Yin C, Wang
Y, Zhou K, Wan S, Shi M, Bao D, et al: Machine learning-based
genome-wide interrogation of somatic copy number aberrations in
circulating tumor DNA for early detection of hepatocellular
carcinoma. EBioMedicine. 56(102811)2020.PubMed/NCBI View Article : Google Scholar
|
|
169
|
Lin M, Guo J, Gu Z, Tang W, Tao H, You S,
Jia D, Sun Y and Jia P: Machine learning and multi-omics
integration: Advancing cardiovascular translational research and
clinical practice. J Transl Med. 23(388)2025.PubMed/NCBI View Article : Google Scholar
|
|
170
|
Mugahid D, Lyon J, Demurjian C, Eolin N,
Whittaker C, Godek M, Lauffenburger D, Fortune S and Levine S: A
practical guide to FAIR data management in the age of multi-OMICS
and AI. Front Immunol. 15(1439434)2025.PubMed/NCBI View Article : Google Scholar
|
|
171
|
Wen G and Li L: Federated transfer
learning with differential privacy for multi-omics survival
analysis. Brief Bioinform. 26(bbaf166)2025.PubMed/NCBI View Article : Google Scholar
|
|
172
|
Palaniappan K, Lin YET and Vogel S: Global
regulatory frameworks for the use of artificial intelligence (AI)
in the healthcare services sector. Healthcare (Basel).
12(562)2024.PubMed/NCBI View Article : Google Scholar
|
|
173
|
Aboy M, Minssen T and Vayena E: Navigating
the EU AI Act: Implications for regulated digital medical products.
NPJ Digit Med. 7(237)2024.PubMed/NCBI View Article : Google Scholar
|
|
174
|
Babic B, Glenn Cohen I, Stern AD, Li Y and
Ouellet M: A general framework for governing marketed AI/ML medical
devices. NPJ Digit Med. 8(328)2025.PubMed/NCBI View Article : Google Scholar
|
|
175
|
Rong G, Mendez A, Bou Assi E, Zhao B and
Sawan M: Artificial Intelligence in Healthcare: Review and
Prediction Case Studies. Engineering (Beijing). 6:291–301.
2020.
|
|
176
|
Van Buchem MM, Boosman H, Bauer MP, Kant
IMJ, Cammel SA and Steyerberg EW: The digital scribe in clinical
practice: A scoping review and research agenda. Npj Digit Med.
4(57)2021.PubMed/NCBI View Article : Google Scholar
|
|
177
|
Park BJ, Hunt SJ, Martin C III, Nadolski
GJ, Wood BJ and Gade TP: Augmented and mixed reality: Technologies
for enhancing the future of IR. J Vasc Interv Radiol. 31:1074–1082.
2020.PubMed/NCBI View Article : Google Scholar
|
|
178
|
Chopra G and Ahmed S: Artificial
intelligence and machine learning-assisted robotic surgery: current
trends and future scope. Elsevier eBooks, pp23-29, 2024.
|
|
179
|
Liu Y, Wu X, Sang Y, Zhao C, Wang Y, Shi B
and Fan Y: Evolution of surgical robot systems enhanced by
artificial intelligence: A review. Adv Intell Syst.
6(2300268)2024.
|
|
180
|
Mehta A, Cheng Ng J, Andrew Awuah W, Huang
H, Kalmanovich J, Agrawal A, Abdul-Rahman T, Hasan MM, Sikora V and
Isik A: Embracing robotic surgery in low- and middle-income
countries: potential benefits, challenges, and scope in the future.
Ann Med Surg (Lond). 84(104803)2022.PubMed/NCBI View Article : Google Scholar
|
|
181
|
Biswas P, Sikander S and Kulkarni P:
Recent advances in robot-assisted surgical systems. Biomed Eng Adv.
6(100109)2023.
|
|
182
|
Haltaufderheide J, Pfisterer-Heise S,
Pieper D and Ranisch R: The ethical landscape of robot-assisted
surgery: A systematic review. J Robot Surg. 19(102)2025.PubMed/NCBI View Article : Google Scholar
|
|
183
|
Wood EA, Ange BL and Miller DD: Are we
ready to integrate artificial intelligence literacy into Medical
school curriculum: Students and faculty survey. J Med Educ Curric
Dev. 8(23821205211024078)2021.PubMed/NCBI View Article : Google Scholar
|
|
184
|
Jackson P, Ponath Sukumaran G, Babu C,
Tony MC, Jack DS, Reshma VR, Davis D, Kurian N and John A:
Artificial intelligence in medical education: Perception among
medical students. BMC Med Educ. 24(804)2024.PubMed/NCBI View Article : Google Scholar
|
|
185
|
Warraich HJ, Tazbaz T and Califf RM: FDA
perspective on the regulation of artificial intelligence in health
care and biomedicine. JAMA. 333:241–247. 2025.PubMed/NCBI View Article : Google Scholar
|
|
186
|
Isch EL, Monzy J, Thota B, Somers S, Self
DM and Caterson E: Assessing AI accuracy in generating CPT codes
from surgical operative notes. J Craniofac Surg. 36:1584–1587.
2025.PubMed/NCBI View Article : Google Scholar
|
|
187
|
Sun E and Littenberg G: Reimbursement and
regulatory landscape for artificial intelligence in medical
technology. Gastrointest Endosc Clin N Am. 35:469–484.
2025.PubMed/NCBI View Article : Google Scholar
|
|
188
|
Shah SJ, Devon-Sand A, Ma SP, Jeong Y,
Crowell T, Smith M, Liang AS, Delahaie C, Hsia C, Shanafelt T, et
al: Ambient artificial intelligence scribes: Physician burnout and
perspectives on usability and documentation burden. J Am Med Inform
Assoc. 32:375–380. 2025.PubMed/NCBI View Article : Google Scholar
|
|
189
|
Duggan MJ, Gervase J, Schoenbaum A, Hanson
W, Howell JT III, Sheinberg M and Johnson KB: Clinician experiences
with ambient scribe technology to assist with documentation burden
and efficiency. JAMA Netw Open. 8(e2460637)2025.PubMed/NCBI View Article : Google Scholar
|
