Contributed: Top 10 Use Cases for AI in Healthcare

A typical clinical study can produce vast datasets containing thousands of images, leading to incredible amounts of data in need of review. Using AI algorithms, studies from across the healthcare industry can be analyzed for patterns and hidden relationships, which can help imaging professionals find critical information fast. AI is being studied within the field of radiology to detect and diagnose diseases through Computerized Tomography and Magnetic Resonance Imaging.

• The British company has leveraged machine learning with data from over 15,000 patients, allowing the algorithm to spot eye disease from optical coherence tomography .
• Some EHR software vendors are beginning to build limited healthcare analytics functions with AI into their product offerings, but are in the elementary stages.
• Physicians do not have the bandwidth to process all this data manually, and AI can leverage this data to assist physicians in treating their patients.
• Speech and text recognition are already employed for tasks like patient communication and capture of clinical notes, and their usage will increase.
• AI in healthcare can enhance preventive care and quality of life, produce more accurate diagnoses and treatment plans, and lead to better patient outcomes overall.
• More than 475 hospitals and 8,000 outpatient facilities across the United States have used the AKASA platform.

The Healthcare AI market is projected to surge from $2.1 to $36.1 billion by 2025 because healthcare data has grown 20x in the past 7 years. Intel’s venture capital arm Intel Capital recently invested in startup Lumiata which uses AI to identify at-risk patients and develop care options. The trend of large health companies merging allows for greater health data accessibility. Endoscopic exams such as esophagogastroduodenoscopies and colonoscopies rely on rapid detection of abnormal tissue. By enhancing these endoscopic procedures with AI, clinicians can more rapidly identify diseases, determine their severity, and visualize blind spots. Early trials in using AI detection systems of early gastric cancer have shown sensitivity close to expert endoscopists.

The exam of the future has arrived

Finally, substantial changes will be required in medical regulation and health insurance for automated image analysis to take off. There has been considerable attention to the concern that AI will lead to automation of jobs and substantial displacement of the workforce. A Deloitte collaboration with the Oxford Martin Institute26 suggested that 35% of UK jobs could be automated out of existence by AI over the next 10 to 20 years.

Perhaps least controversially, clear rules on who is liable if something goes wrong would likely increase adoption.22 If we believe AI adoption will improve health care productivity, then reducing these regulatory barriers will have value. To analyze this hypothesis in the context of AI adoption in health care, we focused on 1,840,784 job postings by 4,556 different hospitals. The Lab for AI in Medicine at TU Munich develops algorithms and models to improve medicine for patients and healthcare professionals. A user-friendly platform is also targeted in order to support clinicians in their treatment decisions that would improve the quality of life for Veterans suffering from AKI. Expert systems usually entail human experts and engineers to build an extensive series of rules in a certain knowledge area. But as the number of rules grows too large, usually exceeding several thousand, the rules can begin to conflict with each other and fall apart.

Drug discovery

If a patient is informed that an image has led to a diagnosis of cancer, he or she will likely want to know why. Deep learning algorithms, and even physicians who are generally familiar with their operation, may be unable to provide an explanation. But whether rules-based or algorithmic in nature, AI-based diagnosis and treatment recommendations are sometimes challenging to embed in clinical workflows and EHR systems. Some EHR vendors have begun to embed limited AI functions (beyond rule-based clinical decision support) into their offerings,20 but these are in the early stages. Providers will either have to undertake substantial integration projects themselves or wait until EHR vendors add more AI capabilities.

While most EU Member States are taking measures towards establishing strategies around the use of AI in healthcare, most initiatives focus on the research and innovation area. The start-up ecosystem varies across EU Member States and is mostly driven by private initiatives and support networks. Learn how artificial intelligence can ease the clinical documentation burden for your care teams.

Imagen OsteoDetect – Wrist fracture detection in adult patients

Harness your organization’s full potential with AI data solutions that remove bottlenecks at the edge, core, and cloud while maximizing protections. Give your care teams the benefit of capturing all the necessary patient information and documentation details—right at the point of care. Nuance’s next-generation Computer-Assisted Physician Documentation uses artificial intelligence to provide real‑time clinical documentation improvement guidance within their natural workflow. This cloud‑based solution ensures consistent recommendations and drives everything from appropriate reimbursement to compliance with regulatory requirements to improved quality outcomes—all while reducing distracting retrospective queries. The number one concern, according to a survey carried out in the United States, surrounding the increased usage of AI in healthcare was threats to security and privacy. Other ethical concerns included safety issues and the potential of the AI being taken over by malicious entities.

The joint center is building an infrastructure that supports research in areas such as genomics, chemical and drug discovery and population health. The collaboration employs big data medical research for the purpose of innovating patient care and approaches to public health threats. With the goal of improving patient care, Iodine Software is creating AI-powered and machine-learning solutions for mid-revenue cycle leakages, like resource optimization and increased response rates. The company’s CognitiveML product discovers client insights, completes documentation accuracy and highlights missing information. New innovations in AI healthcare technology are streamlining the patient experience, helping hospital staff process millions, if not billions of data points, faster and more efficiently.

Improved patient experience

Enable enhanced medical decision-making powered by machine learning to build the treatments of the future. Since AI makes decisions solely on the data it receives as input, it is important that this data represents accurate patient demographics. In a hospital setting, patients do not have full knowledge of how predictive algorithms are created or calibrated. Therefore, these medical establishments can unfairly code their algorithms to discriminate against minorities and prioritize profits rather than providing optimal care. IFlytek launched a service robot «Xiao Man», which integrated artificial intelligence technology to identify the registered customer and provide personalized recommendations in medical areas.

Through the use of Medical Learning Classifiers (MLC’s), Artificial Intelligence has been able to substantially aid doctors in patient diagnosis through the manipulation of mass Electronic Health Records (EHR’s). Medical conditions have grown more complex, and with a vast history of electronic medical records building, the likelihood of case duplication is high. Although someone today with a rare illness is less likely to be the only person to have had any given disease, the inability to access cases from similarly symptomatic origins is a major roadblock for physicians. The implementation of AI to not only help find similar cases and treatments, but also factor in chief symptoms and help the physicians ask the most appropriate questions helps the patient receive the most accurate diagnosis and treatment possible. Third, deep learning algorithms for image recognition require ‘labelled data’ – millions of images from patients who have received a definitive diagnosis of cancer, a broken bone or other pathology. Another growing focus in healthcare is on effectively designing the ‘choice architecture’ to nudge patient behaviour in a more anticipatory way based on real-world evidence.

Virtual assistants for healthcare are optimizing the EHR

Inundated with massive volumes of health data and growing responsibilities, clinicians are struggling to find the time to keep up with the latest medical evidence and still provide patient-centered care. By applying machine learning technologies to the latest biomedical data and electronic health records, healthcare professionals can quickly mine accurate, relevant, evidence-based information that has been curated by medical professionals. Some AI-powered clinical decision support tools feature natural language processing and domain-based training – enabling users to type questions as if they were asking a medical AI For Healthcare colleague in everyday conversation and receive fast, reliable answers. For many diseases, pathological analysis of cells and tissues is considered to be the gold standard of disease diagnosis. AI-assisted pathology tools have been developed to assist with the diagnosis of a number of diseases, including breast cancer, hepatitis B, gastric cancer, and colorectal cancer. AI is well-suited for use in low-complexity pathological analysis of large-scale screening samples, such as colorectal or breast cancer screening, thus lessening the burden on pathologists and allowing for faster turnaround of sample analysis.

Patient engagement and adherence has long been seen as the ‘last mile’ problem of healthcare – the final barrier between ineffective and good health outcomes. The more patients proactively participate in their own well-being and care, the better the outcomes – utilisation, financial outcomes and member experience. They were not substantially better than human diagnosticians, and they were poorly integrated with clinician workflows and medical record systems. The challenge is unlocking the billions of data sets often stored in disconnected digital systems and transforming data into actionable insights that modernize the health care system. Healthcare environments have enormous datasets to be leveraged, and its time to put this data to work where AI and machine learning methods that are intelligently integrated into workflows will improve healthcare delivery of all stakeholders.

CloudMedX uses machine learning to generate insights for improving patient journeys throughout the healthcare system. One Drop provides a discreet solution for managing chronic conditions like diabetes and high blood pressure, as well as weight management. The primary goal of BenevolentAI is to get the right treatment to the right patients at the right time by using AI to produce a better target selection and provide previously undiscovered insights through deep learning.