Crowdfunding is a financing method characterized by pooling together a large number of smaller contributions to support a specific initiative. The emergence of social media and numerous online platforms has allowed crowdfunding to flourish in popularity. Crowdfunding is an especially effective and popular tool in the realm of supporting charitable or ideological causes. Increasingly prevalent in this realm is crowdfunding for health care costs.Many top-earning campaigns on crowdfunding sites are medical in nature. These include campaigns to directly pay for an individual's medical care as well as those intending to accelerate research on and access to experimental interventions. Sophisticated search tools allow potential donors to sort through campaigns based on specific diseases, age, location, or background of patients.Crowdfunding campaigns can be highly effective. GoFundMe's most successful campaign to date has raised more than $2 million through more than 37 000 donations slated to help a young girl from VIEWPOINT

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67 Background: Recent advances in artificial intelligence (AI) carry underexplored practical and ethical implications for the practice of clinical oncology. As oncologic applications of AI proliferate, a framework for guiding their ethical implementations and equitable distribution will be crucial. Methods: We reviewed the current landscape of AI applications in oncology research and clinical practice by reviewing the current body of evidence in PubMed and Medline. Key ethical challenges and opportunities to address health equity are critically evaluated and highlighted. Ethical implications for patients, clinicians and society at large are delineated, with particular focus on the impact and ramifications of AI with respect to healthcare disparities and equity of oncology care delivery. Results: Growing concerns that AI may widen disparities in oncologic care by virtue of lack of affordability, inconsistent accessibility and biased machine-learning models are addressed. Although there is potential for AI to widen disparities in oncology care, using foresight in application, AI has the potential to (1) democratize access to specialized clinical knowledge, (2) improve the accuracy of predicting cancer susceptibility, recurrence and mortality, (3) prevent diagnostic errors in under-resourced settings, (4) minimize unintended bias and (5) enable access to tailored therapeutic options including clinical trials if appropriately deployed. Separately, AI can be harnessed to identify areas of underserved needs and optimize systems of health-information sharing and reimbursements as blockchain technology converges with AI. As AI advances it will have a larger presence in oncology research and clinical practice. Conclusions: A strategic framework integrating ethical standards and emphasizing equitable implementation can help ensure that the potential of AI to address disparities in oncology are maximally captured and its perils averted. Further work is being done on exploring these challenges and will be submitted as a manuscript.