{"id":40226,"date":"2024-12-12T10:35:29","date_gmt":"2024-12-12T16:35:29","guid":{"rendered":"https:\/\/sites.imsa.edu\/acronym\/?p=40226"},"modified":"2024-12-12T10:35:29","modified_gmt":"2024-12-12T16:35:29","slug":"the-doctor-will-ai-you-now","status":"publish","type":"post","link":"https:\/\/sites.imsa.edu\/acronym\/2024\/12\/12\/the-doctor-will-ai-you-now\/","title":{"rendered":"The Doctor Will AI You Now"},"content":{"rendered":"

Have you ever thought how cool it would be not to have to sift through and fill out tons of paperwork during a doctor’s visit or simply enter your symptoms on a mobile app that instantly diagnoses and recommends further testing or writes a prescription? Or imagine your doctor consulting an AI assistant that predicts possible conditions and disease prognoses you haven\u2019t considered. <\/span><\/p>\n

This isn\u2019t science fiction\u2014the future of healthcare is already happening. Artificial intelligence (AI) is revolutionizing the medical field in ways that were unimaginable just a few years ago, impacting everything from research to diagnosis, treatment, and patient monitoring. Before deep diving into AI applications in healthcare, let\u2019s understand why AI is such a unique tool.  <\/span><\/p>\n

AI essentially leverages machine learning and deep learning algorithms that allow it to analyze and learn from vast datasets. Unlike traditional software, which follows predefined rules, AI is unique because it learns from experience and can self-improve to recognize patterns accurately over time. Additionally, some of the latest AI models can process unstructured data \u2014such as images, audio, sizeable genetic information, and even natural languages from a doctor\u2019s notes. AI\u2019s ability to identify information from varied sources and process it together sets it apart from traditional software tools. In a way, AI mimics the human brain with one notable difference (and an advantage): it can process a significant amount of data in seconds. These features enable it to tackle complex tasks like research, diagnosis, personalized treatment, and predictive analytics with a level of speed and accuracy that was previously unattainable.<\/span><\/p>\n

Transforming Medical Research<\/b><\/p>\n

As many IMSA upperclassmen begin their SIRs in various labs, they would be well advised to know how AI can have far-reaching implications in research. The Acronym <\/span>interviewed<\/span><\/a> Dr. Dong last year on this very topic, and he believes in the significant benefits AI brings by automating routine tasks, thereby freeing researchers to focus on more complex and creative aspects of their work. <\/span><\/p>\n

\u201cThere\u2019s so much time wasted and duplicated effort that happens at the lab right now. If we could get rid of that, that would be really great. So I hope that\u2019s the direction. You know, it\u2019s the same thing as automation and mechanization. We learned and figured that out, and in the long run, that was a huge gain for society, for humanity, to be able to mechanize stuff. So in the same way, I hope it works out.\u201d<\/span><\/i><\/p>\n

 – Dr. Dong<\/span><\/i><\/p>\n

Much medical research has been sifting through vast data to uncover trends, find cures, and develop treatments. Innovations in AI are making that process faster and more efficient than ever. One of the most prominent examples is the creation of <\/span>AlphaFold<\/span><\/a>, an AI system developed by DeepMind, that has revolutionized biological research by predicting the 3D structures of proteins based on their amino acid sequences. It recently solved the decades-old protein folding problem\u2014 significantly enhancing drug discovery, disease understanding, and biotechnology. <\/span><\/p>\n

Revolutionizing Disease Diagnosis<\/b><\/p>\n

Perhaps the most life-changing aspect of AI\u2019s influence on medicine is its role in diagnosing diseases. AI-powered predictive tools can forecast patient outcomes, helping clinicians to intervene early. One such example is the deterioration index predictive model used by <\/span>Novant Health Facility<\/span><\/a> to analyze patient data enabling them to identify hospitalized patients who were at risk of deterioration more quickly. This led to a 22% reduction in mortality rate and saved about 153 lives in about a year.<\/span><\/p>\n

AI can analyze medical images and detect patterns that may be too subtle for the human eye, improving the accuracy and speed of diagnoses. There are already AI-based models available to diagnose diseases such as diabetes and skin cancer. This kind of technology could be deployed in remote areas where access to specialists is limited or to provide early diagnoses before visiting a doctor, ensuring patients receive timely and accurate diagnoses. As an example, <\/span>Google DeepMind <\/span><\/a>created an algorithm that can diagnose over 50 different eye conditions from retinal scans, often faster and more accurately than human doctors. Early detection of these diseases can prevent blindness.<\/span><\/p>\n

Personalized Treatment<\/b><\/p>\n

AI can also step in to help design the most effective treatment plan for individual patients. As the world moves towards personalized healthcare, AI can analyze a patient\u2019s genetic makeup, lifestyle, and medical history to recommend treatments tailored specifically to them. For example, <\/span>IBM Watson\u2019s Oncology program<\/span><\/a> helps oncologists develop personalized cancer treatment plans by comparing the patient\u2019s data to vast databases of medical research and clinical trials.<\/span><\/p>\n

AI and IoT technologies, combined, are also making significant strides in complex healthcare procedures such as surgeries. These systems benefit from high levels of precision that humans cannot achieve, allowing doctors to perform procedures that are associated with reduced recovery times and fewer complications.  <\/span><\/p>\n

Improved Patient Monitoring and Aftercare<\/b><\/p>\n

Another area where AI has significant applications is patient monitoring during and aftercare. Wearable devices such as fitness trackers and smartwatches collect data on heart rate, activity levels, and sleep patterns continuously. AI can then analyze this data to detect potential health issues before they become serious. Similarly, cancer researchers in some of the top research hospitals are using AI models to predict relapse after <\/span>CAR T-cell therapies<\/span><\/a>. These machine-learning models allow doctors and researchers to make informed decisions when managing such difficult-to-tackle complications.  <\/span><\/p>\n

 <\/span><\/p>\n","protected":false},"excerpt":{"rendered":"

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