A third of U.S. physicians are already using artificial intelligence (AI) in their practices, and many believe there is ample reason to think this advanced technology can help address diagnostic errors—the largest cause of malpractice claims. However, there are still unresolved questions about the risks.
Startup companies using artificial intelligence (AI) for healthcare raised a record amount of funding in the second quarter of 2019, raising $864 million through 75 deals.1 This indicates strong confidence in the industry that healthcare organizations will adopt AI more broadly in the near future. AI in healthcare is poised to change physicians’ practices and patients’ experiences in the most fundamental ways and holds great promise for improving patient safety.
The potential for meaningful use is not yet realized. Artificial Intelligence is evolving. Computers are not yet intelligence. Today's view of AI relies on huge stored databases, images and algorithms to make decisions. AI does not yet mean computers can think like humans. Comparing the brain to a computer is incorrect. The brain is not binary. It uses a completely different biochemical and electrical system to retain memories and learn. This is still a subject we know little about. Our current primitive measurement systems include functional magnetic resonance imaging, PET scans, electrophysiologic memories, and crude measurements of neurotransmitter hormones. We are far from a USB port at the base of our brain (neck). Visions of the matrix will have to wait.
For now, we have to rely on natural language processing, image analysis, (facial recognition)
At the moment, the sweeping benefits of health technology are still emerging: Improved accuracy of diagnosis, precision medicine, early detection, personalized medicine, and cheaply reproducible drugs are just a few of the ways the healthcare community can expect AI to make the practice of medicine safer and more efficient.
Comments from the Doctor's Company, a medical malpractice insurance company
"As the nation’s largest physician-owned medical malpractice insurer, we advance the practice of good medicine by reducing risk. To this end, we support the integration of AI for its potential in reducing litigation risk, particularly from misdiagnosis—the leading allegation in medical malpractice suits.
Our analysis of more than 25,000 claims and suits revealed that in cases with incorrect diagnoses, inadequate assessments were the most common factor. A tool like AI could support physicians with a second opinion or a deeper layer of understanding. To give just one example, machine learning—a subset of AI concerned with pattern recognition—can help accurately identify the key indicators of a particular illness or injury. This could include symptoms that may otherwise be missed or diagnosed much farther down the line. AI systems are already capable of detecting minute anomalies that would be imperceptible for even the most experienced physicians.
If AI tools can consistently deliver these levels of precision, they could have the additional advantage of reducing the practice of defensive medicine—medical responses are undertaken primarily to avoid liability. In a near-future where misdiagnoses and associated malpractice suits are markedly reduced, physicians should be less inclined to order superfluous tests, procedures, or visits that their judgment deems unnecessary (but which the legal climate often requires)."
A third of U.S. physicians are already using AI in their practices, and many believe there is ample reason to think this advanced technology can help address diagnostic errors, the largest cause of malpractice claims.3 However, there are still unresolved questions about the risks. AI technology is still in the early stages of deployment in clinical practice throughout the United States, but the number of users is likely to rise in coming years.4 Leading healthcare institutions see AI as the front-runner in new technologies for reducing risk.5
53% of physicians surveyed are optimistic about the prospects of AI in medicine.
The foreseeable benefits from healthcare AI include:
Assistance with case triage
Enhanced image scanning and segmentation
Improved detection (speed and accuracy)
Supported decision making
Integration and improvement of workflow
Personalized care
Automatic tumor tracking
Disease development prediction
Disease risk prediction
Patient appointment and treatment tracking
Easing workload to prevent physician burnout and distractions that compromise doctor-led diagnosis
Making healthcare delivery more accessible, humane, and equitable
Increasing physician competency to enable patient-physician trust 6
The foreseeable risks include:
False positives/negatives
Systems error
Overreliance
Unexplainable results
Unclear lines of accountability
New skill requirements
Network systems vulnerable to malicious attack
Seeing things that don’t exist (AI hallucination)
Augmenting biased or unorthodox behavior
Initial Wins from Healthcare AI
“AI will impact almost every area of healthcare. The most promising areas are where machines can automate the processing of large volumes of data when it is not practical for people. Examples include reading an entire patient record and surfacing the relevant data in context, prereading and auditing images for radiologists, automatic identification of gaps in care, risk stratification of patient cohorts, and automation of prior authorization and claims processing based on understanding accepted treatment pathways relative to a specific patient's condition.”
Reading Diagnostic Images
Of all medical specialties, initial applications of AI are likely to affect radiology most directly. Diagnosis-related claims accounted for 67 percent of all diagnostic radiology claims in a study of closed claims between 2013 and 2018 conducted by The Doctors Company. In interventional radiology claims, the second-highest case type was “improper management of treatment course.” Many of those cases were related to primary care physicians’ management of treatment.
In diagnosis-related radiology claims, patient assessment was a contributing factor in 85 percent of the claims, including misinterpretation of diagnostic studies and failure to appreciate and reconcile relevant signs, symptoms, and test results. The top injury in diagnosis-related cases was undiagnosed malignancy, occurring in 35 percent. AI may offer a way to significantly reduce the incidence of failure-to-diagnose and the misinterpretation of diagnostic studies.
The advent of systems that can quickly and accurately read diagnostic images will undoubtedly redefine the work of radiologists and assist in the prevention of misdiagnoses. The majority of AI healthcare applications use machine learning algorithms that train on historical patient data to recognize the patterns and indicators that point to a particular condition. Although the best machine learning systems are possibly only on a par with humans for accuracy in making medical diagnoses based on images,7 experts are confident that this will improve over time as developers train AI systems on millions-strong databanks of labeled images showing fractures, embolisms, tumors, etc. Eventually these systems will be able to recognize the most subtle of abnormalities in patient image data (even when indiscernible for the human eye).
Radiologists have sought ways to help primary care physicians provide the best care, with strategies such as placing the most important findings first in the report and calling attending physicians with serious or confusing findings. AI can be an additional tool to help attending physicians better understand the findings and follow through with recommended tests or referrals.
Though there are legitimate concerns about radiologists being replaced by AI, they should not distract from the undeniable potential of these tools to assist physicians in identifying patients for screening examinations, prioritizing patients for immediate interpretation, standardizing reporting, and characterizing diseases.8
Initial research of AI applications in radiology shows success in:
Performing automatic segmentation of various structures on CT or MR images, potentially providing higher accuracy and reproducibility.9
Automatically detecting polyps during colonoscopy, which assists in increasing adenoma detection, especially diminutive adenomas and hyperplastic polyps.10 Investigators found that the AI system significantly increased the adenoma detection rate (ADR) (29.1 percent vs. 20.3 percent; P < .001), as well as the mean number of adenomas detected per patient (0.53 vs. 0.31; P < .001).
Making better diagnostic decisions through the use of a radiologist-trained tool that provides a summary view of patient information in the electronic health record (EHR) so radiologists can easily uncover relevant underlying issues.11
Prioritizing interpretation of critical findings that a radiologist might otherwise be unaware of until the study is opened. Such solutions allow for faster reading of cases that have a high suspicion for significant abnormalities.
Case Study12
A stroke protocol patient is brought in from the emergency department (ED). The CT scanner has a brain hemorrhage detector built into its display software and is able to immediately notify the team that there is hemorrhage. At that point, the radiologist confers with the ED physician and other clinical team members so that CT angiography can be performed while the patient is still on the table, enhancing workflow and efficiency for the patient.
Case Study13
A 60-year-old male with no prior imaging is admitted to the ED for shortness of breath. A chest radiograph is obtained as part of the initial workup. The algorithm evaluates the image and determines whether the patient’s heart is enlarged. The radiologist is informed of this categorization at the time of interpretation. Additionally, the algorithm is able to evaluate for enlargement of the left atrium (or other specific chambers).
Case Study (from email correspondence with Bradley N. Delman, MD, MS, August 2019)
A female patient is scheduled for a scan to investigate a right-sided rib lesion, but existing imaging data shows that the lesion is on the left. A new data integrity system called CREWS (Clinical Radiology Early Warning System) is being developed at Mount Sinai to detect numerous classes of discordant data and advise a patient's physician before scanning to ensure imaging addresses the correct clinical scenario.
“Even the most straightforward of diagnoses require a clinician’s time to understand and manage. AI algorithms working in the background, monitoring patient data, could minimize many diagnostic delays we have historically considered acceptable.
Here is a real-world example:
Whereas the diagnosis of subarachnoid hemorrhage on a head CT has historically required a radiologist’s eye, convolutional neural networks can now detect many instances of hemorrhage with reasonable enough accuracy to
prioritize in the radiologist’s queue for a formal interpretation. As a result, cases with the highest urgency can be elevated for more prompt attention. Everyone will benefit from a more streamlined diagnosis.”
—Bradley N. Delman, MD, MS
How AI and ML Support Cognitive Collaboration
The Algorithm Will See You Now: How AI’s Healthcare Potential Outweighs Its Risk | The Doctors Company
