{"683116":{"#nid":"683116","#data":{"type":"news","title":"AI in Healthcare Could Save Lives and Money \u2014 But Change Won\u2019t Happen\u00a0Overnight","body":[{"value":"\u003Cdiv class=\u0022theconversation-article-body\u0022\u003E\u003Cp\u003EImagine walking into your doctor\u2019s office feeling sick \u2013 and rather than flipping through pages of your medical history or running tests that take days, your doctor instantly pulls together data from your health records, genetic profile and wearable devices to help decipher what\u2019s wrong.\u003C\/p\u003E\u003Cp\u003EThis kind of rapid diagnosis is one of the big promises of artificial intelligence for use in health care. Proponents of the technology say that over the coming decades, AI has the potential to save hundreds of thousands, \u003Ca href=\u0022https:\/\/www.weforum.org\/stories\/2023\/06\/emerging-tech-like-ai-are-poised-to-make-healthcare-more-accurate-accessible-and-sustainable\/\u0022\u003Eeven millions of lives\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003EWhat\u2019s more, a 2023 study found that if the health care industry significantly increased its use of AI, up to \u003Ca href=\u0022https:\/\/www.healthcaredive.com\/news\/artificial-intelligence-healthcare-savings-harvard-mckinsey-report\/641163\/\u0022\u003EUS$360 billion annually could be saved\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003EBut though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.\u003C\/p\u003E\u003Cp\u003EA 2024 American Medical Association survey found that 66% of U.S. physicians had used AI tools in some capacity, up from 38% in 2023. But most of it was for \u003Ca href=\u0022https:\/\/www.ama-assn.org\/press-center\/ama-press-releases\/ama-physician-enthusiasm-grows-health-care-ai#:%7E\u0022\u003Eadministrative or low-risk support\u003C\/a\u003E. And although 43% of U.S. health care organizations had added or expanded AI use in 2024, many implementations \u003Ca href=\u0022https:\/\/www.advisory.com\/daily-briefing\/2025\/02\/17\/ai-use\u0022\u003Eare still exploratory\u003C\/a\u003E, particularly when it comes to medical decisions and diagnoses.\u003C\/p\u003E\u003Cp\u003EI\u2019m a \u003Ca href=\u0022https:\/\/scholar.google.com\/citations?hl=en\u0026amp;user=BY9oaaoAAAAJ\u0026amp;view_op=list_works\u0026amp;sortby=pubdate\u0022\u003Eprofessor and researcher\u003C\/a\u003E who studies AI and health care analytics. I\u2019ll try to explain why AI\u2019s growth will be gradual, and how technical limitations and ethical concerns stand in the way of AI\u2019s widespread adoption by the medical industry.\u003C\/p\u003E\u003Ch2\u003EInaccurate Diagnoses, Racial Bias\u003C\/h2\u003E\u003Cp\u003EArtificial intelligence excels at finding patterns in large sets of data. In medicine, these patterns could signal early signs of disease that a human physician might overlook \u2013 or indicate the best treatment option, based on how other patients with similar symptoms and backgrounds responded. Ultimately, this will lead to faster, more accurate diagnoses and more personalized care.\u003C\/p\u003E\u003Cp\u003EAI can also \u003Ca href=\u0022https:\/\/doi.org\/10.3390\/bioengineering11040337\u0022\u003Ehelp hospitals run more efficiently\u003C\/a\u003E by analyzing workflows, predicting staffing needs and scheduling surgeries so that precious resources, such as operating rooms, are used most effectively. By streamlining tasks that take hours of human effort, AI can let health care professionals focus more on direct patient care.\u003C\/p\u003E\u003Cp\u003EBut for all its power, AI \u003Ca href=\u0022https:\/\/hai.stanford.edu\/news\/whos-fault-when-ai-fails-health-care\u0022\u003Ecan make mistakes\u003C\/a\u003E. Although these systems are trained on data from real patients, they can struggle when encountering something unusual, or when data doesn\u2019t perfectly match the patient in front of them.\u003C\/p\u003E\u003Cp\u003EAs a result, AI doesn\u2019t always give an accurate diagnosis. This problem is called \u003Ca href=\u0022https:\/\/doi.org\/10.1038\/s41467-024-46142-w\u0022\u003Ealgorithmic drift\u003C\/a\u003E \u2013 when AI systems perform well in controlled settings but lose accuracy in real-world situations.\u003C\/p\u003E\u003Cp\u003ERacial and ethnic bias is another issue. If \u003Ca href=\u0022https:\/\/theconversation.com\/noise-in-the-machine-human-differences-in-judgment-lead-to-problems-for-ai-228984\u0022\u003Edata includes bias\u003C\/a\u003E because it doesn\u2019t include enough patients of certain racial or ethnic groups, then AI might give inaccurate recommendations for them, leading to misdiagnoses. Some evidence suggests \u003Ca href=\u0022https:\/\/doi.org\/10.1007\/s40615-024-02237-0\u0022\u003Ethis has already happened\u003C\/a\u003E.\u003C\/p\u003E\u003Cfigure\u003E\u003Cp\u003E\u003Ciframe width=\u0022440\u0022 height=\u0022260\u0022 src=\u0022https:\/\/www.youtube.com\/embed\/qetKUFDDF4A?wmode=transparent\u0026amp;start=0\u0022 frameborder=\u00220\u0022 allowfullscreen=\u0022\u0022\u003E\u003C\/iframe\u003E\u003C\/p\u003E\u003Cfigcaption\u003E\u003Cspan class=\u0022caption\u0022\u003EHumans and AI are beginning to work together at this Florida hospital.\u003C\/span\u003E\u003C\/figcaption\u003E\u003C\/figure\u003E\u003Ch2\u003EData-Sharing Concerns, Unrealistic Expectations\u003C\/h2\u003E\u003Cp\u003EHealth care systems are labyrinthian in their complexity. The prospect of integrating artificial intelligence \u003Ca href=\u0022https:\/\/doi.org\/10.7759\/cureus.46454\u0022\u003Einto existing workflows is daunting\u003C\/a\u003E; introducing a new technology like AI disrupts daily routines. Staff will need extra training to use AI tools effectively. Many hospitals, clinics and doctor\u2019s offices simply don\u2019t have the time, personnel, money or will to implement AI.\u003C\/p\u003E\u003Cp\u003EAlso, many cutting-edge AI systems operate as opaque \u201cblack boxes.\u201d They churn out recommendations, but even its developers might struggle to fully explain how. This opacity clashes with the needs of medicine, where decisions demand justification.\u003C\/p\u003E\u003Cp\u003EBut developers are often reluctant to \u003Ca href=\u0022https:\/\/doi.org\/10.3389\/fhumd.2024.1421273\u0022\u003Edisclose their proprietary algorithms or data sources\u003C\/a\u003E, both to protect intellectual property and because the complexity can be hard to distill. The lack of transparency feeds skepticism among practitioners, which then slows regulatory approval and erodes trust in AI outputs. Many experts argue that transparency is not just an ethical nicety but \u003Ca href=\u0022https:\/\/doi.org\/10.3389\/fdgth.2024.1267290\u0022\u003Ea practical necessity for adoption\u003C\/a\u003E in health care settings.\u003C\/p\u003E\u003Cp\u003EThere are also \u003Ca href=\u0022https:\/\/doi.org\/10.3390\/healthcare10101878\u0022\u003Eprivacy concerns\u003C\/a\u003E; data sharing could \u003Ca href=\u0022https:\/\/hbr.org\/2019\/10\/adopting-ai-in-health-care-will-be-slow-and-difficult\u0022\u003Ethreaten patient confidentiality\u003C\/a\u003E. To train algorithms or make predictions, medical AI systems often require huge amounts of patient data. If not handled properly, AI could expose sensitive health information, whether through data breaches or unintended use of patient records.\u003C\/p\u003E\u003Cp\u003EFor instance, a clinician using a cloud-based AI assistant to draft a note must ensure no unauthorized party can access that patient\u2019s data. U.S. regulations \u003Ca href=\u0022https:\/\/www.hhs.gov\/hipaa\/for-professionals\/privacy\/laws-regulations\/index.html\u0022\u003Esuch as the HIPAA law\u003C\/a\u003E impose strict rules on health data sharing, which means AI developers need robust safeguards.\u003C\/p\u003E\u003Cp\u003EPrivacy concerns also extend to patients\u2019 trust: If people fear their medical data might be misused by an algorithm, they may be less forthcoming or even refuse AI-guided care.\u003C\/p\u003E\u003Cp\u003EThe grand promise of AI is \u003Ca href=\u0022https:\/\/doi.org\/10.1016\/j.socscimed.2023.116442\u0022\u003Ea formidable barrier in itself\u003C\/a\u003E. Expectations are tremendous. AI is often portrayed as a magical solution that can diagnose any disease and revolutionize the health care industry overnight. Unrealistic assumptions like that often lead to disappointment. AI may not immediately deliver on its promises.\u003C\/p\u003E\u003Cp\u003EFinally, developing an AI system that works well involves a lot of trial and error. AI systems must go through rigorous testing to \u003Ca href=\u0022https:\/\/time.com\/6958868\/artificial-intelligence-safety-evaluations-risks\/\u0022\u003Emake certain they\u2019re safe and effective\u003C\/a\u003E. This takes years, and even after a system is approved, adjustments may be needed as it encounters new types of data and real-world situations.\u003C\/p\u003E\u003Cfigure\u003E\u003Cp\u003E\u003Ciframe width=\u0022440\u0022 height=\u0022260\u0022 src=\u0022https:\/\/www.youtube.com\/embed\/f7SIwZJwmzE?wmode=transparent\u0026amp;start=0\u0022 frameborder=\u00220\u0022 allowfullscreen=\u0022\u0022\u003E\u003C\/iframe\u003E\u003C\/p\u003E\u003Cfigcaption\u003E\u003Cspan class=\u0022caption\u0022\u003EAI could rapidly accelerate the discovery of new medications.\u003C\/span\u003E\u003C\/figcaption\u003E\u003C\/figure\u003E\u003Ch2\u003EIncremental Change\u003C\/h2\u003E\u003Cp\u003EToday, hospitals are rapidly adopting AI scribes that listen during patient visits and automatically draft clinical notes, reducing paperwork and letting physicians spend more time with patients. Surveys show over 20% of physicians now use AI for \u003Ca href=\u0022https:\/\/www.ama-assn.org\/press-center\/ama-press-releases\/ama-physician-enthusiasm-grows-health-care-ai#:%7E\u0022\u003Ewriting progress notes or discharge summaries\u003C\/a\u003E. AI is also becoming a quiet force in administrative work. Hospitals deploy AI chatbots to handle appointment scheduling, triage common patient questions and translate languages in real time.\u003C\/p\u003E\u003Cp\u003EClinical uses of AI exist but are more limited. At some hospitals, AI is a second eye for radiologists \u003Ca href=\u0022https:\/\/doi.org\/10.1016\/j.jacr.2019.05.036\u0022\u003Elooking for early signs of disease\u003C\/a\u003E. But physicians are still reluctant to hand decisions over to machines; only about 12% of them currently \u003Ca href=\u0022https:\/\/www.ama-assn.org\/practice-management\/digital-health\/2-3-physicians-are-using-health-ai-78-2023\u0022\u003Erely on AI for diagnostic help\u003C\/a\u003E.\u003C\/p\u003E\u003Cp\u003ESuffice to say that health care\u2019s transition to AI will be incremental. Emerging technologies need time to mature, and the short-term needs of health care still outweigh long-term gains. In the meantime, AI\u2019s potential to treat millions and save trillions awaits.\u003C!-- Below is The Conversation\u0027s page counter tag. Please DO NOT REMOVE. --\u003E\u003Cimg style=\u0022border-color:!important;border-style:none;box-shadow:none !important;margin:0 !important;max-height:1px !important;max-width:1px !important;min-height:1px !important;min-width:1px !important;opacity:0 !important;outline:none !important;padding:0 !important;\u0022 src=\u0022https:\/\/counter.theconversation.com\/content\/241551\/count.gif?distributor=republish-lightbox-basic\u0022 alt=\u0022The Conversation\u0022 width=\u00221\u0022 height=\u00221\u0022 referrerpolicy=\u0022no-referrer-when-downgrade\u0022\u003E\u003C!-- End of code. If you don\u0027t see any code above, please get new code from the Advanced tab after you click the republish button. The page counter does not collect any personal data. More info: https:\/\/theconversation.com\/republishing-guidelines --\u003E\u003C\/p\u003E\u003Cp\u003E\u0026nbsp;\u003C\/p\u003E\u003Cp\u003E\u003Cem\u003EThis article is republished from \u003C\/em\u003E\u003Ca href=\u0022https:\/\/theconversation.com\u0022\u003E\u003Cem\u003EThe Conversation\u003C\/em\u003E\u003C\/a\u003E\u003Cem\u003E under a Creative Commons license. Read the \u003C\/em\u003E\u003Ca href=\u0022https:\/\/theconversation.com\/ai-in-health-care-could-save-lives-and-money-but-change-wont-happen-overnight-241551\u0022\u003E\u003Cem\u003Eoriginal article\u003C\/em\u003E\u003C\/a\u003E\u003Cem\u003E.\u003C\/em\u003E\u003C\/p\u003E\u003C\/div\u003E","summary":"","format":"full_html"}],"field_subtitle":"","field_summary":[{"value":"\u003Cp\u003EThough artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low.\u003C\/p\u003E","format":"limited_html"}],"field_summary_sentence":[{"value":"Though artificial intelligence has become nearly ubiquitous, from smartphones to chatbots to self-driving cars, its impact on health care so far has been relatively low."}],"uid":"27469","created_gmt":"2025-07-11 15:36:58","changed_gmt":"2026-03-19 13:12:48","author":"Kristen Bailey","boilerplate_text":"","field_publication":"","field_article_url":"","location":"Atlanta, GA","dateline":{"date":"2025-07-11T00:00:00-04:00","iso_date":"2025-07-11T00:00:00-04:00","tz":"America\/New_York"},"extras":[],"hg_media":{"677407":{"id":"677407","type":"image","title":" AI will help human physicians by analyzing patient data prior to surgery. Boy_Anupong\/Moment via Getty Images","body":"\u003Cdiv\u003E\u003Cp\u003EAI will help human physicians by analyzing patient data prior to surgery. \u003Ca href=\u0022https:\/\/www.gettyimages.com\/detail\/photo\/artificial-intelligence-robot-while-analyzing-royalty-free-image\/2153167997?phrase=AI%20in%20hospital%20setting\u0026amp;searchscope=image%2Cfilm\u0026amp;adppopup=true\u0022\u003EBoy_Anupong\/Moment via Getty Images\u003C\/a\u003E\u003C\/p\u003E\u003C\/div\u003E","created":"1752508399","gmt_created":"2025-07-14 15:53:19","changed":"1752508399","gmt_changed":"2025-07-14 15:53:19","alt":" AI will help human physicians by analyzing patient data prior to surgery. Boy_Anupong\/Moment via Getty Images","file":{"fid":"261302","name":"file-20250603-68-b488qp.jpg","image_path":"\/sites\/default\/files\/2025\/07\/14\/file-20250603-68-b488qp.jpg","image_full_path":"http:\/\/hg.gatech.edu\/\/sites\/default\/files\/2025\/07\/14\/file-20250603-68-b488qp.jpg","mime":"image\/jpeg","size":204171,"path_740":"http:\/\/hg.gatech.edu\/sites\/default\/files\/styles\/740xx_scale\/public\/2025\/07\/14\/file-20250603-68-b488qp.jpg?itok=vW2nFiFp"}}},"media_ids":["677407"],"related_links":[{"url":"https:\/\/theconversation.com\/ai-in-health-care-could-save-lives-and-money-but-change-wont-happen-overnight-241551","title":"Read This Article on The Conversation"}],"groups":[{"id":"1237","name":"College of Engineering"},{"id":"658168","name":"Experts"},{"id":"57458","name":"ISyE External News"},{"id":"1214","name":"News Room"},{"id":"1188","name":"Research Horizons"}],"categories":[],"keywords":[{"id":"187915","name":"go-researchnews"}],"core_research_areas":[],"news_room_topics":[{"id":"71881","name":"Science and Technology"}],"event_categories":[],"invited_audience":[],"affiliations":[],"classification":[],"areas_of_expertise":[],"news_and_recent_appearances":[],"phone":[],"contact":[{"value":"\u003Ch5\u003EAuthor:\u003C\/h5\u003E\u003Cp\u003E\u003Ca href=\u0022https:\/\/theconversation.com\/profiles\/turgay-ayer-2237122\u0022\u003ETurgay Ayer\u003C\/a\u003E, professor of Industrial and Systems Engineering, Georgia Institute of Technology\u003C\/p\u003E\u003Ch5\u003EMedia Contact:\u003C\/h5\u003E\u003Cp\u003EShelley Wunder-Smith\u003Cbr\u003E\u003Ca href=\u0022mailto:shelley.wunder-smith@research.gatech.edu\u0022\u003Eshelley.wunder-smith@research.gatech.edu\u003C\/a\u003E\u003C\/p\u003E","format":"limited_html"}],"email":[],"slides":[],"orientation":[],"userdata":""}}}