.svg)
How AI Has been Revolutionising Healthcare
Artificial Intelligence has benefited many fields, solved many problems, and made life more convenient than ever. One such field where AI models that can learn, unlearn, relearn, understand, predict, and act on their own without the need of any programming have proven to be meaningful is the healthcare industry.
As per a report published by Accenture, by 2026, AI is expected to help the US healthcare industry save up to $150 billion annually through efficiency gains in clinical trials, early diagnosis, and enhanced treatment plans. In fact, an AI system developed by Google Health demonstrated a 94.5% accuracy rate in detecting breast cancer from mammograms, compared to 88% by human radiologists. From patient data management, where we have seen 60% of healthcare providers adopting AI, to drug discovery, where the time taken is reduced by up to 70%, AI has truly transformed the industry.
This article highlights some of the areas within healthcare that AI has had an impact upon.
Analyzing Pulmonary Diseases with AI
Chest X-rays and CT (computed tomography) scans are essential tools for diagnosing pulmonary conditions, offering critical insights that improve patient outcomes. However, interpreting these images accurately requires significant expertise, especially in high-volume and urgent scenarios. AI is emerging as a key support tool in this area.
- AI and COVID-19 Screening:
Recent advancements in AI have led to the development of sophisticated systems for screening COVID-19 patients using lung X-rays. These systems utilize techniques such as principal component analysis (PCA) and clustering to differentiate between severe and mild cases. Such tools can guide healthcare providers in determining appropriate treatments, such as whether mechanical ventilation is necessary or potentially harmful.
In addition, another AI system, based on Microsoft CustomVision, has been designed to classify various pneumonia types, including those caused by COVID-19. This system has demonstrated a high sensitivity of 92.9% in accurately identifying different pneumonia conditions.
- AI in Lung Cancer Detection:
AI has also shown promise in detecting lung cancers that may be missed by traditional methods. A study from Seoul National University Hospital found that a commercial AI application outperformed a team of four radiologists in both initial and follow-up readings. The AI achieved better specificity in initial readings and helped radiologists interpret results more effectively.
Despite these advancements, challenges remain in integrating AI into clinical practice. Issues such as updating software systems and improving data management practices are critical for ensuring the effective and secure use of AI in healthcare settings. Continued development and refinement of AI technologies hold the potential to further enhance the accuracy and efficiency of pulmonary disease diagnosis.
Analyzing Skin Diseases with AI
Skin cancer is one of the most prevalent cancers globally, with melanoma being a particularly dangerous subtype. Although melanoma is less common than other skin cancers, in 2023, about 8000 people died in the United States. If, however, melanoma is diagnosed at an early stage, there is a significant change of improvement in treatment outcomes, with up to 86% of melanoma cases being preventable. But identifying early-stage melanoma can be challenging, as it often resembles benign moles or other skin conditions. To address this, AI technology is being explored to provide additional decision support for dermatologists.
Recent advancements have seen the development of annotated datasets of skin lesions, driven by initiatives such as the International Skin Imaging Collaboration (ISIC). These datasets allow for the training of AI models to identify melanoma with increased accuracy. The accessibility of cloud computing and the sharing of expertise among machine learning professionals have accelerated progress in this area.
One notable study from 2019 employed convolutional neural networks (CNNs) based on InceptionV3 and ResNet50 architectures to analyze dermoscopic and close-up skin images. The AI system achieved accuracy levels on par with 95 dermatologists, surpassing the performance of less experienced practitioners. This indicates that AI can effectively match and even exceed human expertise in certain scenarios. Further research using the InceptionV4 model, which is approved for medical use in the EU, demonstrated promising results. The AI was tested against dermatologists who reviewed 100 cases, including both benign and malignant lesions. The AI model slightly outperformed dermatologists in sensitivity, achieving 95% compared to the human average of 94.1%. Despite this, experienced dermatologists generally performed better than the AI in many instances.
Analyzing CT and MRI Scans with AI
Radiology, a field that has long been at the forefront of technological advancements in healthcare, is now only being further revolutionized with AI. Its integration is not only enhancing diagnostic capabilities but also streamlining workflows, allowing medical professionals to focus more on patient care rather than image analysis.
One of the key benefits of AI in radiology is its ability to analyze medical images with high precision. Modern AI tools can extract over 1,500 data points from CT, MRI, and PET scans, improving diagnostic accuracy. As medical imaging annotation becomes more accessible and of higher quality, AI technologies continue to evolve, offering more sophisticated tools for radiologists.
Recent developments in AI applications for radiology are making notable impacts. For instance, researchers in China have created an AI system to analyze CT scans for early signs of COVID-19 pneumonia. This tool can reduce detection time by approximately 30% to 40%, enabling quicker identification, isolation, and treatment of affected patients. In another advancement, a team from Spain has developed a deep learning algorithm designed to enhance the resolution of MRI brain scans. This improvement allows for more accurate identification of complex brain conditions, such as cancers, speech disorders, and trauma. Higher resolution images provide critical insights that can lead to better patient outcomes.
Additionally, a collaborative project between Facebook AI and NYU Langone Health has introduced fastMRI, an AI-driven approach that accelerates the production of MRI images. This technology can make the scanning process up to four times faster while maintaining the same quality as traditional MRI scans. Radiologists have reported that AI-generated images are indistinguishable from those produced by conventional methods.
The Food and Drug Administration (FDA) has already approved numerous deep learning methods for use in radiology, and many more AI projects are transitioning from research labs to clinical settings. As AI continues to advance, its integration into radiology promises to enhance diagnostic accuracy, improve efficiency, and ultimately lead to better patient care.
Detecting Breast Cancer with AI
Regular breast cancer screenings are crucial in improving survival rates, as early detection and timely treatment have been shown to be highly effective. However, the widespread implementation of these screening programs has increased the workload for radiologists, creating a need for more efficient diagnostic tools. Artificial intelligence (AI) is emerging as a vital solution to support radiologists and manage the growing demand for breast cancer detection.
In the European Union, guidelines aim to screen 70% to 75% of eligible women, which has led to a significant rise in patient volumes. To cope with this increase, AI technology is being integrated into clinical practice to provide much-needed decision support. For instance, Radboud University Medical Center has implemented a tomosynthesis tool that generates 3D breast images, enhancing diagnostic precision. However, this advancement comes with the challenge of longer reading times, prompting the use of AI to accelerate image analysis and support radiologists in making faster decisions.
AI tools are proving their value by processing mammography images more quickly and efficiently. For example, a recent collaboration between Google Health and medical professionals has produced an AI system that matches the accuracy of dual readings by two doctors. This system also offers a significant advantage in terms of speed, reducing the workload of the second reader by 88%, which can alleviate some of the pressure on radiologists.
Despite these advancements, the shortage of radiologists remains a critical issue. The UK, for instance, faces a shortage of 27% to 37% of radiologists, with an estimated 42,000 vacancies expected in the U.S. by 2033. In developing regions, the shortage of professionals and equipment is even more pronounced. AI technologies can help mitigate these challenges by easing the strain on healthcare systems and supporting the expansion of breast cancer screening and detection programs.
Overall, AI is making progress in enhancing breast cancer detection, offering both speed and accuracy improvements that are essential in managing the increasing demands placed on radiologists. As AI technology continues to evolve, it promises to play a crucial role in addressing the global shortage of radiologists and improving patient outcomes.
Digital Pathology with Artificial Intelligence
Digital pathology is revolutionizing the field of microscopy by transforming traditional glass slides into digital formats for more comprehensive analysis. This innovation enables pathologists to perform more detailed examinations of tissue samples and enhances diagnostic accuracy through advanced image analysis. With the onset of AI, we now see improvements in pathology practices.
AI algorithms are increasingly being utilized to assist pathologists in several key areas: analyzing and interpreting images, inspecting sample tissues in detail, matching pathology types to historical cases, and improving both diagnostic accuracy and early detection. These AI tools are particularly valuable in managing complex data from various types of cancer.
Recent research has demonstrated the effectiveness of AI in digital pathology through an extensive study involving a public database of whole-slide images (WSIs) from 11,000 cancer patients, covering 32 different cancer subtypes. The AI algorithm used in this study achieved remarkable accuracy in identifying various types of pathology. For instance, it correctly identified bladder urothelial carcinoma with 93% accuracy, kidney renal clear cell carcinoma with 97%, and ovarian serous cystadenocarcinoma with 99%. The algorithm also showed impressive performance with histopathology slides, accurately diagnosing prostate adenocarcinoma at 98%, skin cutaneous melanoma at 99%, and thymoma with a perfect 100% accuracy.
These advancements in AI-powered digital pathology not only support clinical decision-making but also enhance the diagnosis of less commonly studied pathologies and early-stage variations. The ability of AI to analyze vast amounts of data with high precision is a significant step forward in the field, promising better diagnostic outcomes and more efficient pathology practices. As AI technology continues to evolve, its integration into digital pathology is set to improve both the speed and accuracy of cancer diagnoses, ultimately benefiting patient care and treatment.
Enhancing Healthcare Efficiency with AI and Natural Language Processing
Another area where AI has made its mark is in streamlining administrative tasks. One of the key applications of AI in this domain is in automating clinical documentation, decision support systems, and electronic health records (EHRs). The integration of AI technologies, particularly Optical Character Recognition (OCR) and Natural Language Processing (NLP), is proving to be a game-changer in reducing operational costs and enhancing productivity in healthcare settings.
AI-driven automation has the potential to cut down healthcare administrative costs significantly. For instance, the application of OCR to handle tasks such as prior authorizations, healthcare claims management, patient records management, and appointment scheduling can potentially save healthcare providers up to $16.3 billion. Early adopters of these AI technologies have already reported savings of over $122 billion, showcasing the financial impact of process automation.
Natural Language Processing (NLP) is particularly impactful in managing the vast amounts of structured and unstructured data encountered in healthcare. Traditional text mining algorithms struggle with the fact that up to 80% of health data is unstructured, which can lead to inefficient data utilization and negatively affect patient outcomes. NLP systems, however, can process and interpret this data more effectively. They can capture and analyze social determinants of health (SDOH), which provide non-clinical insights into patients’ well-being and care needs. Dr. Elizabeth Marshall highlights that understanding these factors is crucial, as medical care only accounts for about 20% of healthcare outcomes.
NLP technologies are also revolutionizing the way hospitals manage documentation and patient interactions. For example, Concord Hospital has implemented Nuance’s AI-powered software suite for speech recognition and real-time clinical record processing. This system allows staff to dictate notes from any device, with the data being auto-transcribed and added to patient files. This innovation reduced phone-based transcription usage by 91% and saved the hospital $1 million.
Additionally, AI-powered virtual assistants are enhancing patient care by collecting symptom data and identifying critical cases for immediate attention. They also offer convenience for patients with mobility limitations, allowing for quick consultations and adjustments in hospital settings. Overall, AI and NLP are not only improving the efficiency of administrative processes but are also enhancing patient care and reducing costs, marking an advancement in healthcare technology.
Advancing Ophthalmology with AI
With healthcare systems facing increasing pressure from an aging population, timely access to ophthalmic care is becoming more challenging. This strain often results in delays in treatment, which can lead to serious, irreversible conditions such as visual field loss and decreased visual acuity. AI and deep learning technologies are stepping in to bridge these gaps and enhance the efficiency of ophthalmological care. For example, AI systems are now being used to expedite the diagnosis and management of several eye diseases, including diabetic retinopathy (DR), glaucoma, and cataracts.
One significant advancement is the FDA-approved IDx-DR system, which was introduced in 2018 as the first AI-powered tool for autonomously diagnosing diabetic retinopathy. The system utilizes a fundus camera to capture eye images and analyze them within a minute. Clinical trials have demonstrated its effectiveness with an 87% sensitivity and 90% specificity in detecting early signs of DR, which can significantly reduce delays in treatment.
In glaucoma detection, recent studies have highlighted the potential of AI to improve diagnostic accuracy. An algorithm trained on Optical Coherence Tomography (OCT) images and fundus photographs has shown promise in detecting glaucoma progression earlier than traditional methods. This advancement is crucial as timely diagnosis is key to preventing further deterioration.
AI is also transforming cataract management through innovative telehealth solutions. A group of researchers from China developed a smartphone-based AI assistant for remote monitoring of cataract progression. This system allows patients to perform check-ins and receive personalized predictions regarding their condition, which helps in scheduling timely visits and interventions. This approach not only facilitates early detection but also extends ophthalmic care to remote or underserved areas.
Overall, the integration of AI in ophthalmology addresses the pressing need for timely and effective eye care. By enhancing diagnostic accuracy and enabling remote monitoring, AI technologies are helping to mitigate the challenges posed by the growing demand for ophthalmic services and ensuring better patient outcomes.
The Future of AI in Healthcare
The future of artificial intelligence (AI) in healthcare is poised for transformative advancements, driven by the growing availability of annotated healthcare datasets and medical imaging. These developments serve as a foundation for emerging AI and computer vision applications that promise to revolutionize patient care and healthcare operations.
Currently, AI is proving an essential part in various aspects of healthcare, including revenue cycle management, robotic process automation, and drug development processes. Machine learning and deep learning technologies are also enhancing clinical documentation management, claims processing, and diagnostic applications. These technologies help streamline administrative tasks, accelerate drug discovery, and improve diagnostic accuracy, ultimately benefiting both healthcare providers and patients.
AI's potential extends beyond these applications. It can analyze extensive data from patient visits, prescribed medications, lab tests, and medical procedures to provide actionable insights. Early pilots and theoretical models have demonstrated promising results, instilling confidence among computer science professionals and healthcare practitioners about AI's capacity to improve patient care and optimize healthcare organizations globally.
However, for AI to achieve mainstream adoption in healthcare, significant sector-wide changes are needed. Hospitals must invest in new technology systems and focus on staff upskilling to integrate AI effectively into their practices. Successful adoption will also require comprehensive integration projects to ensure that AI technologies are seamlessly incorporated into daily clinical workflows. As AI continues to evolve and its applications expand, the healthcare industry stands on the brink of a new era. By embracing these technological advancements and addressing the necessary transformations, healthcare organizations can harness AI's full potential to enhance patient outcomes and streamline operations.
