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We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com. Back to Healio Key takeaways: Datasets used to train AI can have biased and inaccurate data, which can limit its utility. Validation and human oversight remain critical for AI to be used effectively. The internet went mainstream less than 40 years ago, yet it is hard to remember the world without it. Chadi Nabhan, MD, MBA, hematologist, medical oncologist, podcaster and author, believes the same will hold true for AI in the not-so-distant future.
\u201cThere\u2019s no way around it. AI is here to stay,\u201d Nabhan told Healio. \u201cHumans who are smart in using AI will advance better and faster in life and in career than humans who do not embrace AI.\u201d The same holds true in healthcare. Clinicians and institutions that best utilize AI will likely have the greatest impact on patients. Barriers remain to unlocking AI\u2019s full potential, though. AI models continue to be trained on biased and inaccurate data, which limits practical application to real-world patients, Nabhan and colleagues discussed at the third Collaboration for Outcomes Using Social Media in Oncology (COSMO) Conference in Chicago. \u201cIf you always believe everything that you\u2019re being told, you\u2019re going to make mistakes,\u201d Nabhan said. Healio spoke with Nabhan about AI bias and integrity, how it impacts equity and ways to improve it in the future. Healio: What is AI bias? Nabhan: The data that are being used in the algorithm to create the output representative of patients with cancer being seen in the real world. Most oncology datasets, especially datasets being used to train AI algorithms, are not random samples. Most of the datasets represent patients from large academic centers, patients who have undergone genomic testing, possibly patients who are insured, who had access to healthcare. If there were patients on clinical trials, they were healthy enough to get on clinical trials, and patients who have more longitudinal follow-ups. If we are training AI models on these data, the output may not be representative of all patients with cancer who are seen across community clinics in America. Let\u2019s say you have a model about managing prostate cancer, but that model was developed based on white men who are 75 years of age, can you realistically apply that model on a Black man who is 45 years old who has the same disease? I argue that you can\u2019t. Healio: What is the solution? Nabhan: Before we deploy the model, before we say that this is a great model and could be utilized in making decisions, we should validate it. There\u2019s a training cohort and a validation cohort. You characterize the model using this training population from the datasets that you have, but then you should validate it, and you should validate it in a cohort that represents patients from the real world. These are patients who are in community practices, could be seen in rural settings. They are different racial and ethnic groups. While many datasets include predominantly Caucasian patients, the U.S. Census suggests that 25% of the U.S. population is underrepresented minorities. That\u2019s very important to understand and take into context. Healio: What is AI integrity? Nabhan: Integrity asks the question of whether the data are correct. Imagine there is a patient in the electronic health records who underwent next-generation sequencing (NGS), and that patient is biomarker negative. That\u2019s patient A. In contrast, we have patient B who has never undergone NGS. AI could view these two patients as identically not having an actionable mutation. While this may appear true on the surface, patient A did have NGS, and was biomarker negative, but patient B never had the test done to begin with. The AI reported no actionable mutation on patient B because nothing was recorded in the EHR given the lack of testing . AI can\u2019t really understand that difference. They can only read the fact that these two patients don\u2019t have biomarkers. Absence of evidence in the medical record is not evidence of absence. Healio: How can that problem be solved? Nabhan: AI systems need to be trained to differentiate between unknown vs. negative. Let\u2019s say a clinical trial is looking for a patient that is biomarker negative. To decide on eligibility, the AI should be able to tell you if the eligibility cannot be determined because required molecular testing has not been performed vs. the patient is not eligible because the patient is indeed biomarker negative. That nuance is important and it does play a role in equity. Healio: Why would EHRs have inaccurate data? Nabhan: EHRs are messy. We use a lot of copy, paste and forward. We have a lot of outdated problem lists. A lot of physicians don\u2019t update the problem list on the patient every single time. I had a patient, who, when they were reviewing their records, noticed that I still said they were a smoker, and they were upset with me. They said, \u201cDoc, you know I quit 5 years ago.\u201d I apologized, but the reality is, for a lot of us in a very busy clinic, we don\u2019t always update this information, so it\u2019s missing. Sometimes there is incorrect staging. Sometimes you have incomplete treatment histories. Imagine a patient had a particular test done at a different hospital and it\u2019s not linked to the EHR at the current hospital. You don\u2019t have that information integrated into the existing records. Sometimes genomic records are integrated in the EHR. Sometimes they\u2019re coming through PDFs, and the nurse or the doctor forgot to scan it. If you have AI models trained on EHRs, you are held hostage based on the<\/p>\n","protected":false},"author":1,"featured_media":3571,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-3570","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/posts\/3570","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=3570"}],"version-history":[{"count":0,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/posts\/3570\/revisions"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=\/wp\/v2\/media\/3571"}],"wp:attachment":[{"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=3570"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=3570"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/drsoniafawad.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=3570"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}
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