REFERENCES

1. Schnelldorfer T, Gumbs AA, Tolkoff J, et al. White paper: requirements for routine data recording in the operating room. Art Int Surg 2024;4:7-22.

2. Kline A, Wang H, Li Y, et al. Multimodal machine learning in precision health: a scoping review. NPJ Digit Med 2022;5:171.

3. Cestonaro C, Delicati A, Marcante B, Caenazzo L, Tozzo P. Defining medical liability when artificial intelligence is applied on diagnostic algorithms: a systematic review. Front Med 2023;10:1305756.

4. Oliva A, Grassi S, Vetrugno G, et al. Management of medico-legal risks in digital health era: a scoping review. Front Med 2022;8:821756.

5. Bélisle-Pipon JC, Couture V, Roy MC, Ganache I, Goetghebeur M, Cohen IG. What makes artificial intelligence exceptional in health technology assessment? Front Artif Intell 2021;4:736697.

6. Cecchi R, Haja TM, Calabrò F, Fasterholdt I, Rasmussen BSB. Artificial intelligence in healthcare: why not apply the medico-legal method starting with the Collingridge dilemma? Int J Legal Med 2024.

7. Fattore G, Maniadakis N, Mantovani LG, Boriani G. Health technology assessment: what is it? Current status and perspectives in the field of electrophysiology. Europace 2011;13 Suppl_2:ii49-53.

8. Fasterholdt I, Kjølhede T, Naghavi-Behzad M, et al. Model for assessing the value of artificial intelligence in medical imaging (MAS-AI). Int J Technol Assess Health Care 2022;38:e74.

Artificial Intelligence Surgery
ISSN 2771-0408 (Online)
Follow Us

Portico

All published articles will be preserved here permanently:

https://www.portico.org/publishers/oae/

Portico

All published articles will be preserved here permanently:

https://www.portico.org/publishers/oae/