Table 2 Selected examples demonstrating AI successfully impacting clinical outcomes, underscoring real-world applicability
Author/Organization | Year | Tumor Type | Methods Used | Results | How AI Changes the Practice |
|---|---|---|---|---|---|
Liu et al. 191 | 2017 | Breast Cancer | CNN-based tumor detection and localization in gigapixel pathology images using Inception (V3) architecture with multi-scale inputs. | Detected 92.4% of tumors at 8 FP/image, exceeding previous methods (82.7%) and human pathologists (73.2%). | Improved sensitivity and reduced false negatives in breast cancer metastasis detection, aiding pathologists in diagnosis. |
Esteva et al. 192 | 2017 | Skin Cancer | CNN trained on dermoscopic images for lesion classification. | Matched performance of 21 board-certified dermatologists in identifying malignant vs benign lesions. | Enabled wider access to dermatological expertise via AI-powered applications. |
Lay N et al. 193 | 2017 | Prostate Cancer | Random forest classifiers leveraging spatial, intensity, and texture features from MRI sequences (T2W, ADC, B2000). | Achieved an AUC of 0.93, outperforming the SVM-based CAD approach (AUC of 0.86) on the same test data. | Improved prostate cancer detection accuracy by leveraging instance-level weighting and enhanced feature extraction. |
Zhang C et al. 194 | 2019 | Lung Cancer (Pulmonary Nodules) | 3D CNN; CT Imaging; Open-source and multicenter datasets | Sensitivity: 84.4%; specificity: 83%; superior to manual assessment; effective for nodules <10 mm and 10–30 mm | AI assisted radiologists by providing objective, accurate, and timely detection/classification of nodules, improving diagnostic efficiency. |
McKinney et al. 195 | 2020 | Breast Cancer | CNN models trained on large datasets of mammograms to detect malignancies, leveraging transfer learning. | Reduced false positives by 5.7% and false negatives by 9.4% compared to radiologists | Improved early detection, reducing unnecessary biopsies, and missed diagnoses. |
Exscientia196 | 2021 | General (Drug Discovery) | AI-driven molecular design using generative algorithms and reinforcement learning to identify potential cancer therapeutics. | Reduced drug design timeline significantly, with the first AI-designed drug entering clinical trials in record time. | Accelerated the drug development process and personalized therapy options by identifying targeted molecular candidates. |
Zhu S et al. 197 | 2021 | Breast, Lung, Prostate, Thyroid, Bone Cancers | Analysis of FDA-cleared AI/ML devices, primarily using rule-based algorithms for diagnostics and DL for radiation planning. | 52 out of 343 devices (15.2%) were oncology specific. Since 2016, 94.2% of these were approved. The majority (96.2%) cleared by 510(k) pathway. Diagnostic devices mainly targeted breast (66.7%), lung (14.3%), prostate (6.3%), thyroid (6.3%), and bone (6.3%) cancers. Therapeutic devices (30.8%) were primarily for radiation planning. | Enhanced diagnostic accuracy and efficiency in detecting oncologic pathologies such as breast cancer. Improved treatment precision with AI-driven radiation planning, automating organ segmentation for radiotherapy. |
GI Genius198 | 2021 | Colon Cancer | GI Genius: AI device for real-time lesion detection during colonoscopy using deep learning algorithms. | FDA-authorized; improved adenoma detection rates during colonoscopy. | Assisted clinicians in real-time detection of colorectal polyps, enhancing early cancer detection. |
Optellum199 | 2021 | Lung Cancer | AI-driven software leveraging probabilistic ML for early lung cancer detection from CT scans. | Received FDA clearance for software aiding in the early detection and optimal treatment of lung cancer. | Enhanced early diagnosis, improving patient outcomes through timely intervention. |
Paige.AI200 | 2021 | Prostate Cancer | The Paige Prostate system employs a deep learning algorithm to analyze whole slide images of prostate needle biopsy slides, providing binary classifications and localizing the highest-probability cancer regions using annotated datasets for training and validation. | The system improved diagnostic sensitivity by 7.3% for cancer cases, increased specificity by 1.1%, and enabled pathologists to identify overlooked cancer areas, enhancing overall diagnostic accuracy. | AI complements pathologists by highlighting suspicious regions, improving detection of subtle cancers, reducing diagnostic variability, and optimizing focus areas for review, leading to more accurate and efficient pathology workflows. |
Ye M et al. 201 | 2022 | Lung Cancer | AI-enhanced classifier combining DL for imaging and liquid biopsy for early diagnosis. | Improved diagnostic accuracy in early-stage lung cancer detection, enhancing sensitivity and specificity compared to traditional methods. | Combined AI and liquid biopsy approach offers a non-invasive, accurate method for early lung cancer diagnosis, potentially improving patient outcomes. |
Esteva A et al. 63 | 2022 | Prostate Cancer | Multi-modal DL model integrating clinical data and digital histopathology from prostate biopsies. Model: trained, validated using data from 5 Phase III trials (median follow-up, 11.4 years) | Demonstrated superior discriminatory performance compared to the NCCN risk stratification tools; relative improvement (range, 9.2% to 14.6%) in predicting long-term, clinically relevant outcomes. | Enhanced prognostication by allowing oncologists to computationally predict patient-specific outcomes, facilitating personalized therapy decisions in prostate cancer treatment. This AI-based tool is scalable and can be implemented globally in clinics equipped with digital scanners and internet access. |
ProstatID202 | 2022 | Prostate Cancer | ProstatID: AI software using ML on prostate MRI data for cancer detection. | Received FDA clearance for improving accuracy and speed of prostate cancer detection. | Enhanced MRI diagnostics, leading to earlier and more accurate prostate cancer detection. |
NIH Algorithm-Based Tool203 | 2022 | Prostate Cancer | AI-driven software trained on annotated pathology datasets for cancer detection | FDA cleared; demonstrated improved detection accuracy. | Automated and enhanced prostate cancer diagnosis in clinical settings. |
Liu M et al. 204 | 2023 | Lung Cancer | Meta-analysis of multiple AI techniques, including supervised learning (CNNs, random forests) for CT scan analysis. | AI systems demonstrated high accuracy in diagnosing lung cancer, with pooled sensitivity and specificity rates indicating reliable performance. | Enhanced early detection and diagnostic accuracy in lung cancer, potentially leading to improved patient outcomes. |
Spratt DE et al. 205 | 2023 | Prostate Cancer | AI ensemble model using digital pathology images combined with clinical trial data to predict distant metastasis risk. | In the validation cohort (14.9 years follow-up), ADT significantly improved time to distant metastasis for model-positive patients (34%). No benefit for model-negative patients. | AI enabled patient-specific predictions, improving decision-making for targeted use of ADT in prostate cancer treatment. |
Thirona206 | 2024 | Lung Cancer | AI-based software analyzing lung CT images to detect cancer and other lung diseases. | Received FDA clearance for lung analysis software to assist in diagnosing lung diseases, including cancer. | Improves diagnostic accuracy and efficiency in lung cancer detection. |
Avenda Health207 | 2024 | Prostate Cancer | Unfold AI: AI tool using deep learning on prostate MRI scans for cancer detection. | Achieved 84% accuracy in detecting prostate cancer, outperforming doctors’ 67% accuracy. | Enhances diagnostic precision, leading to more targeted and effective treatments. |
Guardant Health208 | 2024 | CRC | AI-driven blood-based screening test (Shield) for detecting cancer biomarkers. | FDA-approved; detected 83% of colorectal cancers in clinical studies. | Provides a less invasive and more accessible screening option, potentially increasing screening rates. |
ArteraAI209 | 2024 | Prostate Cancer | AI prognostic tool leveraging genomic data to predict outcomes in localized prostate cancer. | Included in NCCN Clinical Practice Guidelines as a predictive test. | Assisted in personalized treatment planning by predicting patient outcomes. |
2024 | Melanoma, BCC and SCC | AI-powered device using light reflectance for non-invasive skin cancer diagnosis. | FDA-approved device for detection of all three major skin cancers. | Expanded access to accurate skin cancer screening in dermatology and primary care settings. |
