Artificial intelligence (AI) in the healthcare industry reduces the workload of healthcare providers, enables faster diagnosis, and brings efficiency to therapeutics and management. Healthcare analytics, diagnostics, medical robotics, and telehealth are the top areas where AI adoption is prominent. AI further accelerates clinical trials and public health management. This report explores the top 10 AI trends in healthcare. Read more to explore how they impact your business.
Innovation Map outlines the Top 10 AI Trends in Healthcare.
For this in-depth study on the Top AI Trends & Startups in Healthcare, we examined a sample including 3 103 worldwide startups & scaleups. This research produces data-driven innovation intelligence that, by providing an overview of developing technologies and healthcare industry startups, enhances strategic decision-making. Working with our IndeedSEO, driven by Big Data and artificial intelligence, these ideas come from 4.7M+ startups and scaleups worldwide. As the world’s largest resource for data on emerging companies, the SaaS platform enables you to identify relevant startups, emerging technologies & future industry trends quickly & exhaustively.
The Innovation Map highlights the Top 10 AI Trends in Healthcare, showcasing how healthcare SEO services can enhance visibility and patient engagement. Moreover, the AI in Healthcare Innovation Map reveals 20 hand-picked startups, all working on emerging technologies that advance their field.
Top 10 AI Trends in Healthcare
- Healthcare Analytics
- Medical Diagnostics
- Telehealth
- Medical Robots
- Hospital Management
- Clinical Decision Support
- Clinical Trials
- Public Health Management
- Cybersecurity
- Personalized Healthcare
Healthcare Analytics
Healthcare data analytics utilize ML algorithms to rapidly discern relevant information from large amounts of medical data. By use of data analysis from EHRs, medical devices, and wearables, helps early disease identification and quick response. Starting from patient datasets for the prevention of stroke, cardiovascular disease (CVD), neurodegeneration, and other consequences among at-risk populations, startups create predictive models. Further, it forecasts hospitalization risks for chronic and critical patients to lower readmission rates. AI-powered big data further improves the overall accuracy and quality of care while speeding up drug discovery and development.
Medical Diagnostics
AI improves patient outcomes by providing faster diagnoses to support clinical decisions and reduce the risks of human error. Startups use a range of data processing methods, like ML and deep learning, to increase the capacity and streamline the workflows of radiologists. Examples include medical analysis of computed tomography (CT) scans and retinal images as well as electrocardiogram (ECG) waveforms and genetic testing. AI flags abnormalities and assists in the early diagnosis of rare medical conditions such as cancer and neurodegeneration. Moreover, it minimizes the need for unnecessary tests and procedures, consequently decreasing the burden on the healthcare infrastructure.
Telehealth
Routine patient health and recovery monitoring allow medical professionals to focus on complex cases and emergencies. AI-driven virtual consultations and remote patient monitoring enable them to assist patients worldwide and provide data-driven care. E-health or mobile health (m-health) platforms range from simple recommendation engines and refer to specialist solutions to platforms that track and analyze patient health. Such solutions collect data from heterogeneous sources including electronic medical records (EMRs), medical images, wearables, medical devices, and smartphones. AI ingests this data to provide recommendations and alerts based on medical history, symptoms, and other concerns. Similarly, NLP-powered virtual health assistants and chatbots automate disease diagnosis, health monitoring, and triaging.
Medical Robots
Medical robots developed by startups serve to execute complicated operations with more accuracy or supply healthcare institutions in far-off locations. Surgical robots help doctors lower their risk of problems and raise the success rate of operations. Likewise, ML-based rehabilitation robots help individuals recuperating from injuries or operations perform physical therapy. Telepresence robots also utilize AI algorithms to simulate physical presence and provide care to patients in underserved areas. This improves patient monitoring for patients with infectious diseases and prescribed contact isolation. Lastly, startups use AI routing in medical transport robots and drones for the autonomous delivery of medical supplies and equipment.
Hospital Management
In hospitals, AI automates routine administrative tasks such as appointment scheduling, insurance claim processing, and medical inventory optimization. AI automation further tackles worker shortage and staff allocation while handling large patient volumes and keeping consistency in the quality of care. Additionally, startups utilize AI-based predictive modeling and patient flow management to optimize resource utilization and hospital operations. AI-driven hospital management and supply chain optimization software also offer targeted interventions and policies to improve the quality of care.
Clinical Decision Support
Clinical decision support systems (CDSS) based on artificial intelligence (AI) reduce the time to find high-risk patients and project illness occurrence. This enables healthcare providers to take preventative actions at an earlier stage for disease management and improve patient outcomes. AI algorithms also provide diagnostic decision support and test recommendations using evidence-based guidelines. Startups leverage NLP to analyze patient data from EHRs and other clinical data sources for treatment recommendations. This assists physicians in providing patient-specific care and advances personalized medicine. It also allows healthcare institutions to identify patterns of drug interactions and support clinical trials and drug development.
Clinical Trials
Clinical trials require processing large amounts of medical data from various sources, often compiled as manual records. AI increases the efficiency of clinical trials by evaluating this data and predicting outcomes such as treatment efficacy, device safety, etc. It further aids researchers in optimizing clinical trial design and identifying promising interventions and drugs. Additionally, startups use NLP to evaluate the data to generate a comprehensive view of patient health. AI thus enables researchers to promptly identify patterns and adverse events that allow them to minimize risks to participants and stratify potential candidates.
Public Health Management
Disease surveillance, management, and outbreak prediction are the primary use cases of AI in population health management. For instance, startups develop AI models that evaluate data from heterogeneous sources, including social media, to monitor trends and infectious disease spread. This information is further utilized to develop targeted interventions and policies to protect public health. Additionally, AI forecasts particular environmental factors that contribute to health risks and enables public health officials to take preventative actions. Moreover, ML and NLP models enable better utilization of public health data. In case of public health emergencies, AI-based chatbots reduce the workload of human responders and public healthcare infrastructure. Startups develop wellness programs with in-built AI that tailors these programs to individual populations.
Cybersecurity
Digitization of healthcare puts sensitive patient medical and private information at risk. Therefore, startups offer AI solutions for anomaly prediction and fraud detection to keep health tech networks running. Artificial intelligence in cybersecurity analyzes network traffic to predict patterns that may indicate the presence of cyber threats. This provides insights to prevent malicious attacks by discovering the origin of attacks, accurate threat detection, and continuous threat monitoring. Similarly, AI accelerates risk assessment and modeling by simulating cyberattacks. This assists in identifying system vulnerabilities and enables the development of risk mitigation policies. Furthermore, startups build AI-powered access controls to monitor and mitigate unauthorized access to healthcare data and systems.
Personalized Healthcare
AI enables faster and more effective utilization of data collected from genetic testing, health records, medical facilities, clinical trials, and research. Startups develop ML and deep learning models to extract insights such as differences in genetic makeup, lifestyle, and medical history. This enables healthcare institutions to deliver targeted and personalized care. Startups also provide testing and diagnostics platforms for early detection and risk prediction of diseases as well as to determine treatment efficacy. AI in personalized healthcare further aids in faster and more targeted drug discovery. This reduces patient-specific complications and facilitates treatment development for rare diseases. Moreover, AI algorithms utilize data from wearables, implants, and, other medical devices to identify patterns that are relevant to an individual’s overall health and wellness.
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Discover all AI Trends, Technologies & Startups in Healthcare
AI application in healthcare is evolving through continuous training with RWD and medical experts, adding value to complex tasks. Startups are automating administration tasks and patient interactions to provide relief during emergencies and improve care delivery. Innovations in AI are increasingly cutting down research time to find optimal interventions and therapies as well as discover new cures. In essence, AI is supplementing the healthcare industry by making it less prone to errors and more efficient. Explore the latest AI trends, technologies, startups in healthcare, and effective healthcare SEO strategies to enhance online visibility and engagement.
The AI Trends in Healthcare outlined in this report only scratch the surface of trends that we identified during our data-driven innovation & startup scouting process. Among others, NLP, disease diagnostics technologies, and medical robots will transform the sector as we know it today. Identifying new opportunities & emerging technologies to implement into your business goes a long way in gaining a competitive advantage. Get in touch to easily & exhaustively scout startups, technologies & trends that matter to you!
