Risks and benefits of an AI revolution in medicine Harvard Gazette
Their work, in the field of “causal inference,” seeks to identify different sources of the statistical associations that are routinely found in the observational studies common in public health. Those studies are good at identifying factors that are linked to each other but less able to identify cause and effect. Hernandez-Diaz, a professor of epidemiology and co-director of the Chan School’s pharmacoepidemiology program, said causal inference can help interpret associations and recommend interventions. More recently, in December 2018, researchers at Massachusetts General Hospital (MGH) and Harvard’s SEAS reported a system that was as accurate as trained radiologists at diagnosing intracranial hemorrhages, which lead to strokes. And in May 2019, researchers at Google and several academic medical centers reported an AI designed to detect lung cancer that was 94 percent accurate, beating six radiologists and recording both fewer false positives and false negatives.
Lastly, AI can help optimize health care sources in the ED by predicting patient demand, optimizing therapy selection (medication, dose, route of administration, and urgency of intervention), and suggesting emergency department length of stay. By analyzing patient-specific data, AI systems can offer insights into optimal therapy selection, improving efficiency and reducing overcrowding. The integration of AI in healthcare has immense potential to revolutionize patient care and outcomes.
The synergy of AI and human intelligence in transforming health care
By analyzing a patient’s genetic makeup, medical history, and other factors, AI can craft highly personalized treatment plans. This approach is particularly promising in the field of cancer treatment, where precision medicine can match the right therapy to an individual’s specific genetic profile. The use of digital image analysis in pathology can identify and quantify specific cell types quickly and accurately and can quantitatively evaluate histological features, morphological patterns, and biologically relevant regions of interest [72,73,74]. As Balázs et al. (2020) declared, recent groundbreaking results have demonstrated that applications of machine learning methods in pathology significantly improve Ki67 scoring in breast cancer, Gleason grading in prostate cancer, and tumor-infiltrating lymphocyte (TIL) scoring in melanoma .
But they often operate in silos, relying on manual inputs across fragmented systems that may not allow for easy data sharing or synthesis. Her portfolio encompasses the design, development and launch of multiple industry self-regulation programs, including the Digital Advertising Accountability Program, the Digital Health Privacy Program, and the TeenAge Privacy Program. She is a seasoned leader focused on data privacy, AI and privacy-enhancing technology policies at the international, federal and state level.
The promise and pitfalls of large language models in clinical applications
However, challenges related to data privacy, bias, and the need for human expertise must be addressed for the responsible and effective implementation of AI in healthcare. For example, medical leaders will have to shape clinically meaningful and explainable AI that contains the insights and information to support decisions and deepen healthcare professionals’ understanding of their patients. Clinical engagement will also be required in product leadership, in order to determine the contribution of AI-based decision-support systems within broader clinical protocols.
These ideas may seem distant, but they have real potential in the near term as gen AI advances. To help bring these changes to healthcare, organizations must learn how to use gen-AI platforms, evaluate recommendations, and intervene when the inevitable errors occur. Healthcare organizations may need to provide learning resources and guidelines to upskill employees. And within hospitals and physician group settings—where burnout is already high—leaders should find ways to make gen-AI-powered applications as easy as possible for frontline staff to use, without adding to their workloads or taking time away from patients.
For millennia individuals relied on physicians to inform them about their own bodies and to some extent, this practice is still applied today. Wearable health devices (WHDs) are an upcoming technology that allow for constant measurement of certain vital signs under various conditions. The key to their early adoption and success is their application flexibility—the users are now able to track their activity while running, meditating, or when underwater. The goal is to provide individuals with a sense of power over their own health by allowing them to analyze the data and manage their own health. Personal health records have historically been physician-oriented and often have lacked patient-related functionalities.
However, the rise of computational modeling is opening up the feasibility of predicting drug toxicity, which can be instrumental in improving the drug development process . This capability is particularly vital for addressing common types of drug toxicity, such as cardiotoxicity and hepatotoxicity, which often lead to post-market withdrawal of drugs. Future applications of AI in healthcare delivery, in the approach to innovation and in how each of us thinks about our health, may be transformative. We can imagine a future in which population-level data from wearables and implants change our understanding of human biology and of how medicines work, enabling personalized and real-time treatment for all. This report focuses on what is real today and what will enable innovation and adoption tomorrow, rather than exploring the long-term future of personalized medicine. Faced with the uncertainty of the eventual scope of application of emerging technologies, some short-term opportunities are clear, as are steps that will enable health providers and systems to bring benefits from innovation in AI to the populations they serve more rapidly.
7. The artificial intelligence can see you now
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