On this episode I discuss how artificial intelligence-based tools can be used for the fast screening and the accurate forecasting of survival prognosis in cervical cancer.

References

Wang, CW., Liou, YA., Lin, YJ. et al. Artificial intelligence-assisted fast screening cervical high grade squamous intraepithelial lesion and squamous cell carcinoma diagnosis and treatment planning. Sci Rep 11, 16244 (2021).

Fowler JR, Maani EV, Jack BW. Cervical Cancer.

Massad, L. S. et al. 2012 updated consensus guidelines for the management of abnormal cervical cancer screening tests and cancer precursors. J. Low Genit. Tract. Dis. 17, S1–S27 (2013).

Ding, D., Lang, T., Zou, D. et al. Machine learning-based prediction of survival prognosis in cervical cancer. BMC Bioinformatics 22, 331 (2021). 

Dagogo-Jack I, Shaw AT. Tumour heterogeneity and resistance to cancer therapies. Nat Rev Clin Oncol. 2018;15:81–94.

On this episode I discuss how artificial intelligence has the potential to both improve the access to healthcare information and healthcare quality in humanitarian health crisis.

References

Fernandez-Luque, Luis, and Muhammad Imran. "Humanitarian Health Computing Using Artificial Intelligence and Social Media: a Narrative Literature Review." International Journal of Medical Informatics, vol. 114, 2018, pp. 136-142.

Imran, Muhammad & Castillo, Carlos & Lucas, Ji & Meier, Patrick & Vieweg, Sarah. (2014). AIDR: Artificial Intelligence for Disaster Response. 10.1145/2567948.2577034

Guo, Jonathan, and Bin Li. “The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries.” Health equity vol. 2,1 174-181. 1 Aug. 2018, doi:10.1089/heq.2018.0037

Caprara, Robert et al. “A platform for gastric cancer screening in low- and middle-income countries.” IEEE transactions on bio-medical engineering vol. 62,5 (2015): 1324-32. doi:10.1109/TBME.2014.2386309

Escalante, Hugo Jair et al. “Acute leukemia classification by ensemble particle swarm model selection.” Artificial intelligence in medicine vol. 55,3 (2012): 163-75. doi:10.1016/j.artmed.2012.03.005

On this episode I discuss how AI has the potential to transform the way surgery is taught and practiced.

References

Hashimoto, Daniel A et al. “Artificial Intelligence in Surgery: Promises and Perils.” Annals of surgery vol. 268,1 (2018): 70-76. doi:10.1097/SLA.0000000000002693

Panesar, Sandip MD, MSc∗; Cagle, Yvonne MD†,§; Chander, Divya MD, PhD‡,§; Morey, Jose MD§,||,¶,#,∗∗,††; Fernandez-Miranda, Juan MD∗; Kliot, Michel MD∗Artificial Intelligence and the Future of Surgical Robotics, Annals of Surgery: August 2019 - Volume 270 - Issue 2 - p 223-226 doi: 10.1097/SLA.0000000000003262

Liow, Ming Han Lincoln et al. “THINK surgical TSolution-One® (Robodoc) total knee arthroplasty.” SICOT-J vol. 3 (2017): 63. doi:10.1051/sicotj/2017052

D'Souza, Marissa et al. “Robotic-Assisted Spine Surgery: History, Efficacy, Cost, And Future Trends.” Robotic surgery (Auckland) vol. 6 9-23. 7 Nov. 2019, doi:10.2147/RSRR.S190720

Loftus, Tyler J et al. “Artificial Intelligence and Surgical Decision-making.” JAMA surgery vol. 155,2 (2020): 148-158. doi:10.1001/jamasurg.2019.4917

On this episode I discuss how AI has been leveraged for dermatological applications.

References:

De, Abhishek et al. “Use of Artificial Intelligence in Dermatology.” Indian journal of dermatology vol. 65,5 (2020): 352-357. doi:10.4103/ijd.IJD_418_20

Chan, Stephanie et al. “Machine Learning in Dermatology: Current Applications, Opportunities, and Limitations.” Dermatology and therapy vol. 10,3 (2020): 365-386. doi:10.1007/s13555-020-00372-0

Udrea, A et al. “Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms.” Journal of the European Academy of Dermatology and Venereology : JEADV vol. 34,3 (2020): 648-655. doi:10.1111/jdv.15935

Gomolin, Arieh et al. “Artificial Intelligence Applications in Dermatology: Where Do We Stand?.” Frontiers in medicine vol. 7 100. 31 Mar. 2020, doi:10.3389/fmed.2020.00100

Esteva, Andre et al. “Dermatologist-level classification of skin cancer with deep neural networks.” Nature vol. 542,7639 (2017): 115-118. doi:10.1038/nature21056

Emam, S et al. “Predicting the long-term outcomes of biologics in patients with psoriasis using machine learning.” The British journal of dermatology vol. 182,5 (2020): 1305-1307. doi:10.1111/bjd.18741

On this episode I discuss the use of AI-based approaches for the development and discovery of effective vaccines.

Black, Steve et al. “Transforming vaccine development.” Seminars in immunology vol. 50 (2020): 101413. doi:10.1016/j.smim.2020.101413

Ong, Edison et al. “COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.” Frontiers in immunology vol. 11 1581. 3 Jul. 2020, doi:10.3389/fimmu.2020.01581

Tomic, Adriana et al. “SIMON, an Automated Machine Learning System, Reveals Immune Signatures of Influenza Vaccine Responses.” Journal of immunology (Baltimore, Md. : 1950) vol. 203,3 (2019): 749-759. doi:10.4049/jimmunol.1900033

Moxon, Richard et al. “Editorial: Reverse Vaccinology.” Frontiers in immunology vol. 10 2776. 3 Dec. 2019, doi:10.3389/fimmu.2019.02776

He, Yongqun et al. “Vaxign: the first web-based vaccine design program for reverse vaccinology and applications for vaccine development.” Journal of biomedicine & biotechnology vol. 2010 (2010): 297505. doi:10.1155/2010/297505

Ong, Edison et al. “Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.” Bioinformatics (Oxford, England) vol. 36,10 (2020): 3185-3191. doi:10.1093/bioinformatics/btaa119

Yang, Brian et al. “Protegen: a web-based protective antigen database and analysis system.” Nucleic acids research vol. 39,Database issue (2011): D1073-8. doi:10.1093/nar/gkq944

Yang, Z., Bogdan, P. & Nazarian, S. An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study. Sci Rep 11, 3238 (2021). https://doi.org/10.1038/s41598-021-81749-9

Tomar, Namrata, and Rajat K De. “Immunoinformatics: an integrated scenario.” Immunology vol. 131,2 (2010): 153-68. doi:10.1111/j.1365-2567.2010.03330.x

Keshavarzi Arshadi, Arash et al. “Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development.” Frontiers in artificial intelligence vol. 3 65. 18 Aug. 2020, doi:10.3389/frai.2020.00065

Wu, Jingcheng et al. “DeepHLApan: A Deep Learning Approach for Neoantigen Prediction Considering Both HLA-Peptide Binding and Immunogenicity.” Frontiers in immunology vol. 10 2559. 1 Nov. 2019, doi:10.3389/fimmu.2019.02559

I discuss how AI has the potential to redefine the diagnosis and understanding of mental illnesses.

References:

Tate AE, McCabe RC, Larsson H, Lundstrom S, Lichtenstein P, Kuja-Halkola R. Predicting mental health problems in adolescence using machine learning techniques. PLoS One. 2020;15(4):e0230389.

Rutter M, Kim-Cohen J, Maughan B. Continuities and discontinuities in psychopathology between childhood and adult life. J Child Psychol Psychiatry. 2006;47(3-4):276-95.

Pettersson E, Anckarsater H, Gillberg C, Lichtenstein P. Different neurodevelopmental symptoms have a common genetic etiology. J Child Psychol Psychiatry. 2013;54(12):1356-65.

Dwyer DB, Falkai P, Koutsouleris N. Machine Learning Approaches for Clinical Psychology and Psychiatry. Annu Rev Clin Psychol. 2018;14:91-118

Dekker I, De Jong EM, Schippers MC, De Bruijn-Smolders M, Alexiou A, Giesbers B. Optimizing Students' Mental Health and Academic Performance: AI-Enhanced Life Crafting. Front Psychol. 2020;11:1063.

Cosic K, Popovic S, Sarlija M, Kesedzic I, Jovanovic T. Artificial intelligence in prediction of mental health disorders induced by the COVID-19 pandemic among health care workers. Croat Med J. 2020;61(3):279-88.

Su C, Xu Z, Pathak J, Wang F. Deep learning in mental health outcome research: a scoping review. Transl Psychiatry. 2020;10(1):116.