Ken Nelson’s Post

Exciting advancements in AI are reshaping healthcare and I am also quite excited to see how it evolves – can you tell? 😝 As an advocate for equitable access to healthcare, one of the key aspects I am keen to understand is the impact of AI in low-middle income countries? How can AI help strengthen healthcare systems in resource-constrained settings to enhance patient outcomes, optimize resource allocation, and revolutionize disease surveillance? This comprehensive review highlights AI's impact across various medical specialties, including radiology, cardiology, oncology, critical care, and surgery. For example, it illustrates how AI was used to improve diagnosis of diabetic retinopathy (DR) in Zambia. This helped overcome the shortage of ophthalmologists as well as improving diagnostic precision and overall diagnostic rates. Despite these promising strides, challenges like low adoption rates, population bias and infrastructure limitations persist. As we delve deeper into AI's potential, it's crucial to address these barriers to unlock its full benefits and make sure we continue to work towards equitable access to healthcare. #AI #healthcare #equitableaccess #accesstocare #emergingmarkets

Economic evaluation for medical artificial intelligence: accuracy vs. cost-effectiveness in a diabetic retinopathy screening case - npj Digital Medicine #Introduction Healthcare IT professionals are increasingly interested in the economic evaluation of medical artificial intelligence systems, particularly in the context of diabetic retinopathy screening. #Accuracy vs. Cost-Effectiveness The blog post discusses the trade-off between accuracy and cost-effectiveness when implementing AI systems for diabetic retinopathy screening. #Study Design Researchers conducted a study to evaluate the economic implications of using AI for diabetic retinopathy screening, comparing the accuracy and cost-effectiveness of different approaches. #Results The study found that while AI systems can improve accuracy in diabetic retinopathy screening, the cost-effectiveness of these systems varies depending on the specific implementation. #Discussion The blog post explores the implications of these findings for healthcare IT professionals, highlighting ai.mediformatica.com #health #effectivene #diabeticretinopathy #china #model #intelligence #artificialintelligence #analysis #diabetes #digital #medical #medicine #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/4bFRHmd)

Treating a patient at the moment is like driving a car while looking only in the rear-view mirror. That is a scary and maybe oversimplified analogy for today’s #healthcare. But there is some truth to it. Essentially, treatments are based on what worked in similar patients in the past. The vision of #precisionmedicine goes beyond that and tries to find the best treatment for the individual directly. One of the promising areas of #digitalhealth that can help with this concept are digital twins. Replicating some organs or even entire bodies can help simulate treatments, surgeries, progression, and many other parameters that we currently passively observe. The article below has some good insights on where the technology of digital twins are already being heavily explored in cardiovascular care, oncology, and women’s health. Even in #patientexperience of the healthcare system. “Imagine having a digital twin that gets ill, and can be experimented on to identify the best possible treatment, without you having to go near a pill or a surgeon’s knife. Scientists believe that within five to 10 years, “in silico” trials – in which hundreds of virtual organs are used to assess the safety and efficacy of drugs – could become routine, while patient-specific organ models could be used to personalise treatment and avoid medical complications.”

AI is transforming cardiology by streamlining workflows, improving image interpretation, and potentially reducing burnout among cardiologists. This translates to enhanced diagnostics and further improved patient treatment accuracy. However, the adoption and practical use of AI tools also have other implications, and the need for a deeper understanding and evaluation of AI’s clinical value is essential before any decisions are made. In this new white paper, we explore the exciting possibilities and challenges of AI in cardiology. Read now: https://lnkd.in/g_spttgT   #cardiology #AI #enterpriseimaging #ACC24

Digital twins, combining patient-specific data with computational models, offer a new horizon in medical diagnostics and treatment plans. 👥 In short, these virtual representations of physical objects can be used: 👥 To conduct in silico (computer-simulated) trials: In the next 5-10 years, virtual trials using digital organs could become standard for testing drug safety and efficacy, drastically reducing the need for physical drug trials. 👥 To generate patient-specific models: By integrating detailed data on genes, proteins, and bodily systems, these models create precise simulations of individual organs, aiding in the customisation of treatments and preventing medical complications. 👥 Advance the cardiology field: Cardiology is leading the way, with patient-specific heart models already being used to design medical devices and test drugs. 👥 Optimise cancer treatment and research: Collaboration between AI experts and cancer researchers is enabling the creation of digital replicas of tumours, enhancing the understanding of individual responses to drug treatments. 👥 Improve outcomes in obstetrics: Digital twins are also being developed for pregnancy, aiming to improve outcomes for conditions like pre-eclampsia and placental insufficiency. 👥 Optimise healthcare systems: Beyond individual treatment, digital twins of hospitals are being created to enhance overall healthcare efficiency and patient care. This innovative approach marks a significant shift from traditional methods, offering a future where treatments are no longer based on the 'average patient' but tailored to each individual's unique biological makeup. It promises a more efficient, effective, and personalised healthcare system. #digitaltwins #healthcareinnovation #healthcare

A Comprehensive Guide of 𝐀𝐈 𝐢𝐧 𝐌𝐞𝐝𝐢𝐜𝐚𝐥 𝐃𝐢𝐚𝐠𝐧𝐨𝐬𝐭𝐢𝐜𝐬 [PDF Guide] Artificial intelligence (#AI) in medical diagnostics is a powerful tool that has the potential to make healthcare more accessible and affordable by assisting healthcare providers in making accurate and quick treatment decisions for their patients. The accurate diagnosis of a disease takes years of medical training and is a time-consuming process. The use of AI in #medicaldiagnostics has proven to give accurate diagnoses, aid in clinical decisions, and enhance physicians' judgment. 𝐂𝐥𝐢𝐜𝐤 𝐡𝐞𝐫𝐞 𝐟𝐨𝐫 𝐏𝐃𝐅 𝐁𝐫𝐨𝐜𝐡𝐮𝐫𝐞 👉 https://lnkd.in/drkmitnb AI in medical diagnostics includes AI solutions that aid physicians with medical decision-making. The growth of AI in medical diagnostics market is driven by the growing need for the adoption of AI in medical diagnosis to reduce errors, the shortage of healthcare professionals, and the rising incidence rate of #chronicdiseases. In addition, the growth potential in emerging economies and the growing number of cross-industry partnerships & collaborations are expected to provide significant growth opportunities for this market. Based on modality, the #CTscan segment is expected to account for the largest share of the AI in medical diagnostics market. The large market share of this segment is attributed to factors such as the growing need for effective and early diagnosis and the rising prevalence of chronic diseases globally due to the aging population. Furthermore, the outbreak of the COVID-19 pandemic increased the demand for chest CT examinations in COVID-19 patients, contributing to the market growth of this segment. 𝐁𝐲 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐭𝐲: 1. Brain & Neurological 2. Oncology 3. Cardiac & Vascular 4. Chest & Lung 5. Obstetrics & Gynecology (OB-GYN) 6. Other Specialties 𝐑𝐞𝐚𝐝 𝐎𝐮𝐫 𝐋𝐚𝐭𝐞𝐬𝐭 𝐍𝐞𝐰𝐬 @ https://lnkd.in/dviSmKvX #machinelearning #artificialintelligence #nlp #analytics #healthcare #healthit #healthcareai #healthcareindustry #mri #ultrasound #xray #ecg #radiology #medicalequipment #medicaldevices #medicaldevicesales #medicalimaging #diagnosticimaging #invitrodiagnostics #patientcare #informationtechnology #software #growth #innovation #technology #research

Harnessing the Power of Artificial Intelligence in Cardiology Artificial intelligence (AI) is rapidly revolutionizing cardiology, offering innovative solutions to complex challenges in diagnosis, treatment, and patient care. In diagnostics, AI-driven algorithms analyze vast amounts of cardiac data, including electrocardiograms (ECGs) and imaging studies, to detect abnormalities and identify patterns indicative of cardiovascular disease. These advanced analytics enable early detection and timely intervention, improving patient outcomes. Moreover, AI predicts the risk of cardiovascular events by leveraging patient data, including medical history and lifestyle factors, enabling targeted interventions and preventive measures. In image interpretation, AI-powered technologies enhance the analysis of cardiac imaging studies, aiding cardiologists in identifying structural abnormalities and assessing disease progression more accurately. This facilitates personalized treatment planning and improves diagnostic accuracy. Additionally, AI-driven decision support systems help clinicians select optimal treatment strategies by synthesizing clinical guidelines and patient data, leading to more effective and personalized patient care. Despite the promising potential of AI in cardiology, challenges such as data standardization, algorithm validation, and regulatory compliance must be addressed to ensure safe and effective implementation in clinical practice. Ongoing research is needed to refine AI algorithms, validate their performance, and assess their impact on clinical outcomes. Nevertheless, AI holds immense promise for the future of cardiology, empowering clinicians with advanced tools and insights to improve diagnosis, treatment, and patient care, ultimately shaping the landscape of cardiovascular medicine in profound and impactful ways. #ai #cardiology #healthcare #medicine #artificialintelligence #healthtech #cardiovascular #digitalhealth #medicalresearch #deeplearning #technology #aiforgood

Artificial intelligence is revolutionizing the field of cardiology by enhancing diagnostic accuracy, predicting outcomes, and personalizing treatment plans for patients. AI algorithms analyze large amounts of data from various sources, such as medical records, imaging studies, and genetic information, to assist clinicians in making more informed decisions. In cardiology, AI is used for interpreting medical imaging studies, such as echocardiograms and MRIs, to detect abnormalities and diagnose conditions like heart disease and arrhythmias. AI also helps predict the risk of cardiovascular events by analyzing patient data and monitoring devices. It can personalize treatment plans by analyzing genetic information and medical history, enabling clinicians to choose the most effective medications and interventions for each individual. While AI shows great promise in cardiology, further research and validation are needed to ensure its reliability and safety in clinical practice.