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AI and cancer care

Warning graphic  Inclusion of tools in this guide does not imply endorsement by library staff.
You are encouraged to apply critical appraisal skills to assess the quality, accuracy, and suitability of all information and outputs.

On this page you will find:

AI Books

Translational Application of Artificial Intelligence in Healthcare
The Impact of Artificial Intelligence on Healthcare Industry
Scalable Artificial Intelligence for Healthcare
Multiple Perspectives on Artificial Intelligence in Healthcare
Machine Learning and Artificial Intelligence in Healthcare Systems : Tools and Techniques
Empire of AI: inside the reckless race for total domination
Data Science and Artificial Intelligence for Digital Healthcare
Artificial Intelligence-based Healthcare Systems
Artificial Intelligence Technology in Healthcare Security and Privacy Issues
Artificial Intelligence in Healthcare Information Systems—Security and Privacy Challenges
Artificial Intelligence in Healthcare Emphasis on Diabetes, Hypertension, and Depression Management
Artificial Intelligence in Healthcare and Medicine
Artificial Intelligence for Healthcare : Machine Learning and Diagnostics
Artificial Intelligence and Machine Learning in Healthcare

Education & training


Prompts & content generation tools

Useful for drafting text or brainstorming ideas, but not suitable for factual clinical decision‑making.


Research and literature review tools


Literature mapping


Strategy, policy & governance


Recordings

10 October 2024 by JHH Adult Medicine Grand Rounds

Dr Faisal Hayat - Clinical Trials Fellow, Oncology

Open Access logo Cho SI, Navarrete-Dechent C, Daneshjou R, Cho HS, Chang SE, Kim SH, Na JI, Han SS. Generation of a Melanoma and Nevus Data Set From Unstandardized Clinical Photographs on the Internet. JAMA Dermatol. 2023 Nov 1;159(11):1223-1231. doi: 10.1001/jamadermatol.2023.3521. PMID: 37792351; PMCID: PMC10551819.

Open Access logo Schaffter T, Buist DSM, Lee CI, Nikulin Y, Ribli D, Guan Y, Lotter W, Jie Z, Du H, Wang S, Feng J, Feng M, Kim HE, Albiol F, Albiol A, Morrell S, Wojna Z, Ahsen ME, Asif U, Jimeno Yepes A, Yohanandan S, Rabinovici-Cohen S, Yi D, Hoff B, Yu T, Chaibub Neto E, Rubin DL, Lindholm P, Margolies LR, McBride RB, Rothstein JH, Sieh W, Ben-Ari R, Harrer S, Trister A, Friend S, Norman T, Sahiner B, Strand F, Guinney J, Stolovitzky G; and the DM DREAM Consortium; Mackey L, Cahoon J, Shen L, Sohn JH, Trivedi H, Shen Y, Buturovic L, Pereira JC, Cardoso JS, Castro E, Kalleberg KT, Pelka O, Nedjar I, Geras KJ, Nensa F, Goan E, Koitka S, Caballero L, Cox DD, Krishnaswamy P, Pandey G, Friedrich CM, Perrin D, Fookes C, Shi B, Cardoso Negrie G, Kawczynski M, Cho K, Khoo CS, Lo JY, Sorensen AG, Jung H. Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms. JAMA Netw Open. 2020 Mar 2;3(3):e200265. doi: 10.1001/jamanetworkopen.2020.0265. Erratum in: JAMA Netw Open. 2020 Mar 2;3(3):e204429. doi: 10.1001/jamanetworkopen.2020.4429. PMID: 32119094; PMCID: PMC7052735.

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