KCU Libraries: Introduction to AI: AI in Medicine

Resources and Training

AMA Ed Hub

Artificial and Augmented Intelligence in Health Care

Artificial and Augmented Intelligence are rapidly changing technologies with wide-reaching medical implications. Earn CME credits while learning at your own pace.

Augmented intelligence in medicine

Articles on AI in Medicine

Artificial Intelligence and Machine Learning in Clinical Medicine, 2023
Artificial intelligence and deep learning in ophthalmology Abstract Artificial intelligence (AI) based on deep learning (DL) has sparked tremendous global interest in recent years. DL has been widely adopted in image recognition, speech recognition and natural language processing, but is only beginning to impact on healthcare. In ophthalmology, DL has been applied to fundus photographs, optical coherence tomography and visual fields, achieving robust classification performance in the detection of diabetic retinopathy and retinopathy of prematurity, the glaucoma-like disc, macular oedema and age-related macular degeneration. DL in ocular imaging may be used in conjunction with telemedicine as a possible solution to screen, diagnose and monitor major eye diseases for patients in primary care and community settings. Nonetheless, there are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms. DL could potentially revolutionise how ophthalmology is practised in the future. This review provides a summary of the state-of-the-art DL systems described for ophthalmic applications, potential challenges in clinical deployment and the path forward. Keywords: imaging, retina, glaucoma, telemedicine, public health
Emerging role of deep learning-based artificial intelligence in tumor pathology Abstract The development of digital pathology and progression of state-of-the-art algorithms for computer vision have led to increasing interest in the use of artificial intelligence (AI), especially deep learning (DL)-based AI, in tumor pathology. The DL-based algorithms have been developed to conduct all kinds of work involved in tumor pathology, including tumor diagnosis, subtyping, grading, staging, and prognostic prediction, as well as the identification of pathological features, biomarkers and genetic changes. The applications of AI in pathology not only contribute to improve diagnostic accuracy and objectivity but also reduce the workload of pathologists and subsequently enable them to spend additional time on high-level decision-making tasks. In addition, AI is useful for pathologists to meet the requirements of precision oncology. However, there are still some challenges relating to the implementation of AI, including the issues of algorithm validation and interpretability, computing systems, the unbelieving attitude of pathologists, clinicians and patients, as well as regulators and reimbursements. Herein, we present an overview on how AI-based approaches could be integrated into the workflow of pathologists and discuss the challenges and perspectives of the implementation of AI in tumor pathology. Keywords: artificial intelligence; artificial intelligence-assisted bioinformatic analysis; deep learning; pathology; tumor.
Artificial intelligence and machine learning | applications in musculoskeletal physiotherapy Abstract Introduction: Artificial intelligence (AI) is a field of mathematical engineering which has potential to enhance healthcare through new care delivery strategies, informed decision making and facilitation of patient engagement. Machine learning (ML) is a form of narrow artificial intelligence which can be used to automate decision making and make predictions based upon patient data. Purpose: This review outlines key applications of supervised and unsupervised machine learning in musculoskeletal medicine; such as diagnostic imaging, patient measurement data, and clinical decision support. The current literature base is examined to identify areas where ML performs equal to or more accurately than human levels. Implications: Potential is apparent for intelligent machines to enhance various areas of physiotherapy practice through automization of tasks which involve data analysis, classification and prediction. Changes to service provision through applications of ML, should encourage physiotherapists to increase their awareness of and experiences with emerging technologies. Data literacy should be a component of professional development plans to assist physiotherapists in the application of ML and the preparation of information technology systems to use these techniques. Keywords: Artificial intelligence; Low back pain; Machine learning; Physiotherapy.
The promise of artificial intelligence: a review of the opportunities and challenges of artificial intelligence in healthcare Abstract Introduction: Artificial intelligence (AI) and machine learning (ML) are rapidly evolving fields in various sectors, including healthcare. This article reviews AI's present applications in healthcare, including its benefits, limitations and future scope. Sources of data: A review of the English literature was conducted with search terms 'AI' or 'ML' or 'deep learning' and 'healthcare' or 'medicine' using PubMED and Google Scholar from 2000-2021. Areas of agreement: AI could transform physician workflow and patient care through its applications, from assisting physicians and replacing administrative tasks to augmenting medical knowledge. Areas of controversy: From challenges training ML systems to unclear accountability, AI's implementation is difficult and incremental at best. Physicians also lack understanding of what AI implementation could represent. Growing points: AI can ultimately prove beneficial in healthcare, but requires meticulous governance similar to the governance of physician conduct. Areas timely for developing research: Regulatory guidelines are needed on how to safely implement and assess AI technology, alongside further research into the specific capabilities and limitations of its medical use. Keywords: artificial intelligence; healthcare; machine learning.
Use of artificial intelligence and deep learning in fetal ultrasound imaging Abstract Deep learning is considered the leading artificial intelligence tool in image analysis in general. Deep-learning algorithms excel at image recognition, which makes them valuable in medical imaging. Obstetric ultrasound has become the gold standard imaging modality for detection and diagnosis of fetal malformations. However, ultrasound relies heavily on the operator's experience, making it unreliable in inexperienced hands. Several studies have proposed the use of deep-learning models as a tool to support sonographers, in an attempt to overcome these problems inherent to ultrasound. Deep learning has many clinical applications in the field of fetal imaging, including identification of normal and abnormal fetal anatomy and measurement of fetal biometry. In this Review, we provide a comprehensive explanation of the fundamentals of deep learning in fetal imaging, with particular focus on its clinical applicability. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.
Generative AI in Medical Imaging: Applications, Challenges, and Ethics Abstract Medical imaging is playing an important role in diagnosis and treatment of diseases. Generative artificial intelligence (AI) have shown great potential in enhancing medical imaging tasks such as data augmentation, image synthesis, image-to-image translation, and radiology report generation. This commentary aims to provide an overview of generative AI in medical imaging, discussing applications, challenges, and ethical considerations, while highlighting future research directions in this rapidly evolving field. Keywords: Generative artificial intelligence; Large language models; Medical imaging; Radiology; Synthetic medical data.

Examples of AI in Clinical Settings

Diagnosis and Treatment:

Imaging analysis: AI algorithms can analyze medical images (X-rays, MRIs, etc.) with high accuracy, aiding in earlier disease detection and more precise diagnoses. This applies to conditions like cancer, heart disease, and lung abnormalities.
Chatbots and virtual assistants: AI-powered chatbots can answer patient questions, offer self-diagnosis guidance, and even triage symptoms to prioritize urgent cases. They can also act as virtual assistants for healthcare professionals, summarizing patient data and suggesting treatment options.
Predictive analytics: AI can analyze patient data to predict potential health risks and personalize treatment plans. This helps in preventive care and early intervention for chronic diseases like diabetes and cardiovascular issues.

Drug Discovery and Development:

Robot-assisted drug discovery: AI-powered robots can automate tasks in drug labs, accelerating the process of discovering new medications. They can analyze vast amounts of data and suggest promising drug candidates for further testing.
Personalized medicine: AI can analyze individual patient data (genetic makeup, medical history, etc.) to predict how they might respond to specific drugs, facilitating personalized treatment approaches.

Administrative tasks and operations:

Electronic health record (EHR) analysis: AI can analyze vast amounts of EHR data to identify trends, improve administrative efficiency, and flag potential issues like medication errors or coding inconsistencies.
Claims processing and fraud detection: AI can automate claims processing tasks, reducing administrative burden and helping detect fraudulent activities within healthcare systems.
Scheduling and appointment management: AI-powered chatbots can handle appointment scheduling and reminders, freeing up staff time for other tasks.