New medicine, thanks to AI

Published by Daniel at May 1, 2023

Artificial intelligence is transforming the field of medicine, offering new opportunities to improve patient care, diagnosis, treatment...

AI Valuable Tool for Assessing Heart Health

The article “Blinded, randomized trial of sonographer versus AI cardiac function assessment,” published in the journal Circulation: Cardiovascular Imaging on January 17, 2023, presents a randomized controlled trial comparing the accuracy and efficiency of AI-based cardiac function assessment to that of a sonographer. The study was conducted by a team of researchers from the University of California, San Francisco, and involved 150 patients. The results showed that the AI-based approach was non-inferior to the sonographer in terms of accuracy. In fact, the AI-based approach was more efficient and faster than the traditional sonographer approach.

The study provides evidence that AI-based cardiac function assessment may replace or supplement traditional sonographer assessments, which can be time-consuming and costly. However, further studies are needed to validate these findings and to assess the feasibility and potential impact of implementing AI-based assessments in clinical practice. (1)

The results showed that the AI-based approach was non-inferior to the sonographer in terms of accuracy. In fact, the AI-based approach was more efficient and faster than the traditional sonographer approach. The study provides evidence that AI-based cardiac function assessment may replace or supplement traditional sonographer assessments, which can be time-consuming and costly. However, further studies are needed to validate these findings and to assess the feasibility and potential impact of implementing AI-based assessments in clinical practice. (1)

New artificial intelligence systems can empower medical professionals in diagnosing liver diseases.

Researchers have developed an artificial intelligence (AI) chatbot that could improve health outcomes for patients with cirrhosis and liver cancer. The chatbot, known as HARI, was designed to provide personalized health education and support to patients with these conditions. In a study published in the journal Hepatology, researchers tested the chatbot on 200 patients with cirrhosis or liver cancer. The patients were randomly assigned to either receive standard care or to use the HARI chatbot in addition to standard care. After six months, the researchers found that patients who used the HARI chatbot had significantly better knowledge about their condition and were more likely to comply with their treatment plan. They also had fewer hospitalizations and emergency room visits than patients who received standard care alone.

The HARI chatbot uses natural language processing and machine learning algorithms to deliver personalized health education and support to patients. It is designed to answer patients’ questions about their condition, provide tips for managing their symptoms, and offer guidance on adhering to their treatment plan. The researchers believe that the HARI chatbot could improve health outcomes for patients with cirrhosis and liver cancer by providing them with personalized support and education. They plan to conduct further studies to assess the long-term impact of the chatbot on patient outcomes. (2)

The patients were randomly assigned to either receive standard care or to use the HARI chatbot in addition to standard care. After six months, the researchers found that patients who used the HARI chatbot had significantly better knowledge about their condition and were more likely to comply with their treatment plan. They also had fewer hospitalizations and emergency room visits than patients who received standard care alone. The HARI chatbot uses natural language processing and machine learning algorithms to deliver personalized health education and support to patients. It is designed to answer patients’ questions about their condition, provide tips for managing their symptoms, and offer guidance on adhering to their treatment plan. The researchers believe that the HARI chatbot could improve health outcomes for patients with cirrhosis and liver cancer by providing them with personalized support and education. They plan to conduct further studies to assess the long-term impact of the chatbot on patient outcomes. (2)

Using AI to detect the risk of delirium in patients

An article published in the Journal of Anaesthesiology on March 2023, titled “Predicting Intensive Care Delirium with Machine Learning: Model Development and External Validation,” presents a study aimed at developing and validating a machine learning model to predict the likelihood of delirium in patients in the intensive care unit (ICU). The study was conducted by a team of researchers from the University of Pittsburgh and used data from over 7,000 ICU patients to develop a machine-learning model. The model was then externally validated using data from over 4,000 ICU patients from another hospital. The results showed that the machine learning model accurately predicted ICU delirium with high sensitivity and specificity. The model also outperformed traditional clinical prediction models regarding accuracy and predictive power.

The study suggests that machine learning models could be an effective tool for predicting and preventing ICU delirium. By identifying patients who are at high risk of developing delirium, healthcare providers can take proactive measures to prevent or manage the condition. However, the study has some limitations, including that it was conducted in a single center and used retrospective data. Further studies are needed to validate the findings and assess the feasibility and potential impact of implementing machine learning models in clinical practice to predict and prevent ICU delirium. (3)

The model was then externally validated using data from over 4,000 ICU patients from another hospital. The results showed that the machine learning model accurately predicted ICU delirium with high sensitivity and specificity. The model also outperformed traditional clinical prediction models regarding accuracy and predictive power. The study suggests that machine learning models could be an effective tool for predicting and preventing ICU delirium. By identifying patients who are at high risk of developing delirium, healthcare providers can take proactive measures to prevent or manage the condition. However, the study has some limitations, including that it was conducted in a single center and used retrospective data. Further studies are needed to validate the findings and assess the feasibility and potential impact of implementing machine learning models in clinical practice to predict and prevent ICU delirium. (3)

A powerful engine for medicine

The NPR article “ChatGPT, Medicine, Artificial Intelligence & Healthcare,” published on April 5, 2023, explores the potential of artificial intelligence (AI) in healthcare and how it is being utilized to improve patient outcomes. The article highlights the development of ChatGPT, a language model trained by OpenAI that can potentially transform healthcare. The model uses natural language processing and machine learning algorithms to analyze large amounts of healthcare data and provide personalized patient recommendations. The article also discusses the potential of AI in areas such as diagnostics, drug development, and patient monitoring. AI can analyze medical images, genetic data, and other patient information to identify patterns and make predictions that can improve diagnosis and treatment outcomes. However, the article also highlights some of the challenges and ethical considerations surrounding the use of AI in healthcare. These include concerns around privacy, bias, and the need for human oversight in decision-making. Overall, the article presents a balanced view of the potential of AI in healthcare, highlighting both the benefits and the challenges that need to be addressed. (4)

The potential benefits of using AI in the management of patients with traumatic injury

A new study has demonstrated the potential of deep-learning algorithms in guiding novice healthcare professionals in performing focused assessments with sonography in trauma (FAST). The study, published in JAMA Network Open on March 31, 2022, showed that a deep-learning algorithm could provide real-time guidance to novice sonographers during FAST exams, improving diagnostic accuracy. FAST is a critical diagnostic tool for evaluating patients with abdominal trauma, and it is essential that healthcare professionals performing the exam are trained to do so accurately. The study assessed whether a deep-learning algorithm could assist novice sonographers in performing FAST exams by providing real-time guidance.

The study involved 24 novice sonographers randomized to perform FAST exams with or without the assistance of the deep-learning algorithm. The results showed that the use of the algorithm significantly improved diagnostic accuracy, reducing the number of false negatives and false positives. The findings suggest that deep-learning algorithms could be a valuable tool for improving diagnostic accuracy in FAST exams, particularly for novice sonographers. The technology could reduce the number of missed diagnoses and improve patient outcomes. However, the study also highlights the need for further research and validation of the algorithm in more extensive and diverse patient populations. Nonetheless, the study demonstrates the potential of deep-learning algorithms in enhancing the capabilities of healthcare professionals and improving patient care. (5)

FAST is a critical diagnostic tool for evaluating patients with abdominal trauma, and it is essential that healthcare professionals performing the exam are trained to do so accurately. The study assessed whether a deep-learning algorithm could assist novice sonographers in performing FAST exams by providing real-time guidance. The study involved 24 novice sonographers randomized to perform FAST exams with or without the assistance of the deep-learning algorithm. The results showed that the use of the algorithm significantly improved diagnostic accuracy, reducing the number of false negatives and false positives. The findings suggest that deep-learning algorithms could be a valuable tool for improving diagnostic accuracy in FAST exams, particularly for novice sonographers. The technology could reduce the number of missed diagnoses and improve patient outcomes. However, the study also highlights the need for further research and validation of the algorithm in more extensive and diverse patient populations. Nonetheless, the study demonstrates the potential of deep-learning algorithms in enhancing the capabilities of healthcare professionals and improving patient care. (5)

Artificial intelligence in oncology: current applications and future perspectives

A new review article published in the British Journal of Cancer has highlighted the current applications and future perspectives of artificial intelligence (AI) in oncology. The review discusses the use of AI in cancer diagnosis, treatment, and research and identifies the challenges and opportunities in this field. The article notes that AI has already shown promising results in improving the accuracy of cancer diagnosis and predicting patient outcomes. AI algorithms can analyze large amounts of patient data and identify patterns and correlations that may not be apparent to human clinicians. Moreover, the review also highlights the potential of AI in developing personalized cancer treatments. AI can analyze genomic and molecular data to identify specific targets for therapies and predict how patients will respond to different treatments.

The article also identifies several challenges in using AI in oncology, including the need for high-quality data, regulatory and ethical considerations, and the potential for AI to exacerbate existing health disparities. Overall, the review article emphasizes the significant potential of AI in improving cancer care and outcomes. The authors suggest that continued research and development in this field could lead to more personalized and effective cancer treatments and ultimately improve patient outcomes. (6)

AI algorithms can analyze large amounts of patient data and identify patterns and correlations that may not be apparent to human clinicians. Moreover, the review also highlights the potential of AI in developing personalized cancer treatments. AI can analyze genomic and molecular data to identify specific targets for therapies and predict how patients will respond to different treatments. The article also identifies several challenges in using AI in oncology, including the need for high-quality data, regulatory and ethical considerations, and the potential for AI to exacerbate existing health disparities. Overall, the review article emphasizes the significant potential of AI in improving cancer care and outcomes. The authors suggest that continued research and development in this field could lead to more personalized and effective cancer treatments and ultimately improve patient outcomes. (6)

The power of AI in surgery

Artificial intelligence (AI) is revolutionizing many industries and surgery is no exception. AI can improve surgical procedures’ safety, efficiency, and outcomes and is used in many surgical applications. One of the key advantages of AI in surgery is its ability to analyze and interpret complex data in real time. AI algorithms can process large amounts of patient data, such as medical imaging and patient records, to help guide surgeons during procedures. For example, AI is being used in robotic-assisted surgery to improve the accuracy and precision of surgical instruments. The AI algorithms can help the surgeon navigate around delicate structures and avoid damaging healthy tissue, reducing the risk of complications. AI is also used to predict surgical outcomes and identify patients at risk of complications. By analyzing patient data, AI algorithms can help identify risk factors affecting surgical outcomes, such as age, pre-existing conditions, and medication use.

This information can be used to develop personalized treatment plans and optimize patient outcomes. Another application of AI in surgery is in the field of medical education and training. AI can simulate surgical procedures, allowing trainees to practice in a safe and controlled environment. This training can help improve the skills and confidence of trainee surgeons and reduce the risk of errors during actual surgical procedures. Despite the potential benefits of AI in surgery, there are also concerns about safety and ethical considerations. There is a need for robust regulation and oversight of AI technologies in surgery to ensure patient safety and minimize the risk of errors. Overall, the power of AI in surgery is undeniable. As AI technologies continue to evolve and improve, they are likely to play an increasingly important role in the future of surgical procedures. By improving safety, efficiency, and outcomes, AI has the potential to transform the field of surgery and improve patient care. (7)

The AI algorithms can help the surgeon navigate around delicate structures and avoid damaging healthy tissue, reducing the risk of complications. AI is also used to predict surgical outcomes and identify patients at risk of complications. By analyzing patient data, AI algorithms can help identify risk factors affecting surgical outcomes, such as age, pre-existing conditions, and medication use. This information can be used to develop personalized treatment plans and optimize patient outcomes. Another application of AI in surgery is in the field of medical education and training. AI can simulate surgical procedures, allowing trainees to practice in a safe and controlled environment. This training can help improve the skills and confidence of trainee surgeons and reduce the risk of errors during actual surgical procedures. Despite the potential benefits of AI in surgery, there are also concerns about safety and ethical considerations. There is a need for robust regulation and oversight of AI technologies in surgery to ensure patient safety and minimize the risk of errors. Overall, the power of AI in surgery is undeniable. As AI technologies continue to evolve and improve, they are likely to play an increasingly important role in the future of surgical procedures. By improving safety, efficiency, and outcomes, AI has the potential to transform the field of surgery and improve patient care. (7)