The health-care industry has rapidly integrated machine learning, advanced algorithms, and artificial intelligence to pursue precision medicine in recent years. Advanced technology scans and analyzes big data to retrieve relevant diagnoses and detect various diseases.
AI image sensing improves the prediction capabilities of patient outcomes through big-data analytics. It converts acquired images into data before analyzing the information to detect abnormalities. These results allow radiologists to identify early signs of disease and develop a personalized treatment plan.
The rapid adoption of AI-based imaging devices (in use cases like X-rays, MRIs, and CT scans) and rising investment in advanced technologies for health care have resulted in an uptick in adoption and usage. Investments began in 2016 when AI showed great promise, and since then, it has continued to play a pivotal role in medicine. In fact, market reports predict that AI image sensing can generate a CAGR of 30.4% by 2026 (Mordor Intelligence, 2021).
In this article, we’ll highlight the growth of AI image sensing in disease detection and prevention and discuss the role of radiologists during this evolution and what the future of AI entails.
Leveraging AI image sensing for diagnosing diseases early
Over the years, AI has started dominating the health-care market due to its higher specificity and lower recall rates. These aspects increase productivity levels and performance efficiency in diagnosing various diseases. Advanced diagnostic imaging enables radiologists to detect early signs of cancer, tumors, and other diseases.
Real-time intervention can improve the survival rate and help practitioners develop a personalized treatment plan according to the acquired data. The Journal of the American College of Radiology highlights the future of AI image sensing in health care by listing its diverse applications and their market demand. A recent report from the Journal of the American College of Radiology by Dr. Alan Alexander estimated that currently, the usage of AI-based imaging devices range from CT scans (90%), MRIs (60%), and X-rays (56%).
Health-care professionals are eager to use AI-based imaging devices instead of biopsy procedures. Its noninvasive approach to diagnosis and detection helps evade undesirable complications that doctors experience during a biopsy. Outcomes are relatively more accurate and precise than conventional diagnostic methods. For instance, CT scans can identify gene mutations and lesions early on with the assistance of advanced technology.
Moreover, it can measure small radiographic abnormalities that would have remained undetectable before. AI uses prerequisite machine learning and algorithms to detect tissue-related patterns and abnormalities. Its high sensitivity enables these devices to detect inconsistent patterns at a level that radiologists cannot identify during the initial consultation or surface-level readings.
Radiologists and AI: Will AI replace physicians?
One may wonder if the steady growth of AI-based devices in health care might result in the replacement of radiologists. While this seems like an inevitable result of AI adoption, medical professionals know that their presence has equal importance.
For starters, AI image sensing is currently in its development phase. One should also remember that decision-making requires aptitude and medical knowledge beyond algorithm-based results.
Therefore, even if AI imaging outperforms traditional diagnostic techniques in quantitative tasks, it can’t match the human-level accuracy in medical decision-making. For now, you should consider machine-learning integration as a supplementary diagnostic solution for certain diseases.
It can help interested radiologists boost productivity by reducing clinical time and enhancing patient experiences.
A recent article in the Lancet also observes that radiologists can optimize patient outcomes if they consider specific AI-related setbacks. For instance, an early diagnosis might be challenging to prove if the results point toward anomalies that don’t have extensive scientific support. Likewise, “overdiagnosis as a result of minor changes” can generate false positives. Radiologists must use their expertise to compare symptoms and present the correct diagnosis. In short, AI image sensing still requires human support to fulfill its potential.
The future of AI imaging
The confidence in medical imaging has increased substantially in the last decade. As machine-learning networks improve, medical image detection and interpretation will enhance. Due to this, many big corporations and research programs invest in AI image sensors.
For instance, Google Health aims to “reduce treatment planning and improve the efficiency of radiotherapy” by collaborating with Mayo Clinic to configure an algorithm that segments healthy tissues and organs from tumors. The tech firm has also invested in AI-centric imaging solutions for detecting eye diseases, breast cancer, and lung cancer.
Moreover, the success of AI image sensing has given rise to augmented reality 3D medical imaging development. Currently, researchers are testing prototypes that allow physicians to use AR headsets to view affected organs and tissues. Alternatively, 3D imaging can also be used to observe blockages, scarred tissues, and tumors from various angles. Surgeons can use this information to focus solely on target sites during the operation.
Supplementing advanced diagnostics via medical imaging can lead to life-changing insights into various diseases, injuries (including fractures and musculoskeletal injuries), and conditions. Health-care providers can generate data that they might be unable to identify without a visual aid. The high sensitivity and accuracy rates lead to successful treatment plans.
In the end, increased interest in AI image sensing will make diagnostic technology more accessible and practical.