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Predicting the treatment effect for an individual patient has the potential to transform treatment of patients and drastically improve patients outcomes. In this work, we illustrate the potential for ML and AI methods to yield useful predictions of individual treatment effects. Using the predicted individual treatment effects (PITE) framework which uses baseline covariates (features) to predict whether a treatment is expected to yield benefit for a given patient compared to an alternative intervention we provide an illustration of the potential of such approaches and provide a detailed discussion of opportunities for further research and open challenges when seeking to predict individual treatment effects.<\/jats:p>","DOI":"10.1007\/s13218-023-00827-4","type":"journal-article","created":{"date-parts":[[2024,1,22]],"date-time":"2024-01-22T12:02:57Z","timestamp":1705924977000},"page":"27-32","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Predicting Individual Treatment Effects: Challenges and Opportunities for Machine Learning and Artificial Intelligence"],"prefix":"10.1007","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1096-188X","authenticated-orcid":false,"given":"Thomas","family":"Jaki","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Chang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Alena","family":"Kuhlemeier","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"name":"The Pooled Resource Open-Access ALS Clinical Trials Consortium","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M. 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