Junior Software Engineer
The adoption of AI in healthcare is something that continues to evolve, with the potential to drastically transform patient care, research, and healthcare administration. At Reflections, we've not only recognized the multifaceted potential of AI but have also demonstrated our proficiency in harnessing its power to help numerous clients revolutionize their businesses.
This is the era of Artificial Intelligence (AI) and the reality is that it has made significant inroads into the healthcare industry by offering a range of applications and benefits. The adoption of AI in healthcare is something that continues to evolve, with the potential to drastically transform patient care, research, and healthcare administration.
In the previous blog, we discussed the way AI has made its presence in Healthcare Analytics, Medical Diagnostics, Telehealth, Medical Robots, and Hospital Management. In this blog, we are analyzing the impact of AI in Clinical Decision Support, Clinical Trials, Public Health Management, Cybersecurity, and Personalized Healthcare.
AI-based clinical decision support systems (CDSS) minimize the time to identify high-risk patients and predict the possibility of diseases. 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 Natural Language Processing (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.
For example, a US-based startup has developed a cloud-based contouring software application for radiation treatment planning. It uses deep learning models to identify cancerous tumors from CT and MRI scans and demarcates at-risk organs for radiotherapy. Moreover, the centralized platform improves communication between radiation oncologists and technicians. AI-based contouring thus significantly saves time and resources.
Another instance is an Indian startup building a medical decision support system for respiratory abnormalities. Its AI algorithm is trained on datasets from chest X-ray (CXR) machines, patient demographics, and clinical trials for precise image analysis. This enables computer-aided screening and triage for multiple complexities such as chest X-ray pathologies, tuberculosis, pediatric pneumonia, and heart attack. It provides automated clinical insights for personalized diagnostics and removes pressure from hospitals and diagnostic service providers.
At Reflections, we’ve built AI algorithms that replace traditional algorithms to optimize efficiency and increase decision-making abilities of a physician going through large heart beat records to identify heart abnormalities as elaborated in this blog. This contains AI at edge that can predict and alarm serious situations real time and also AI on cloud that will analyze patterns and forecast situations the patient might encounter in future or point out minor abnormalities that will be hard for human eyes to detect.
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 designing 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.
There is an Australian startup that has come up with a clinical trial participant recruitment and management platform, which allows clinics to create multiple trial-specific customizable landing pages, AI-generated protocol synopsis, and AI pre-screening eligibility questionnaires. Moreover, the platform enhances visibility into the patient journey and engagement as well as delivers trial reports and insights to make informed decisions. As a result, pharmaceuticals and clinical research organizations (CROs) can control costs and accelerate time to market for drug trials by automating participant management.
There is an Israeli startup that creates synthetic images for the development of healthcare AI. The startup uses Machine Learning to generate medical imaging datasets that are unbiased, verifiable, and free of privacy concerns. These ML algorithms enable de-novo synthesis of tissue images with multiscale modeling in various modalities such as MRI, CT, and ultrasound. These datasets offer clinical insights to improve patient screening for early clinical trials.
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.
A US-based startup offers a platform-as-a-service to identify population health risks and provide wellness plans. Its mobile application for AI-based predictive analytics and data-driven employee wellness tracking recommends customized lifestyle changes and nutrition advice. The app also tracks activities and sends reminders to employees while enabling progress tracking. This enables employers to introduce wellness initiatives and ensure good employee health and wellness. The app further provides care coordination plans for clinics, hospitals, independent delivery networks, payers, and insurers.
A Bruneian startup has developed EVYDENCE, an AI-based population health management platform. It leverages proprietary ML models to convert raw, unstructured data into standardized and legible data to derive public health insights. Its public health solution includes EVYDSurveillance and EVYDResponse modules for tracking infectious disease spread and improving epidemic management. Health authorities, policymakers, regulators, hospital administrators, doctors, and researchers use EVYDENCE for real-time health data analysis, outbreak prevention, and policy making.
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 control to monitor and mitigate unauthorized access to healthcare data and systems.
There is an AI-graph platform that simplifies real-world-data (RWD) licensing by incentivizing patients developed by a Swiss startup that utilizes blockchain and AI to encrypt patient data and provides a marketplace for licensing consented data to healthcare organizations and pharmaceuticals. This RWD thus accelerates drug discovery pipelines.
A German startup increases patient data privacy through its encryption technology and medical network hub. The startup’s self-controlled healthcare data records, XHR Records, applies multiple encryption layers on EHRs to enable homomorphic encryption and enhance security. Its AI-powered applications, XETAX Pro and XETAX App, for healthcare providers and patients improve patient management security and remote surgical planning.
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.
There is a Canadian startup that provides AI-powered personalized treatment plans for varicocele and male infertility through its smartphone application. The startup’s AI assesses patient conditions and suggests tailored treatment plans based on various identified causes. The solution also features disease assessment, patient tracking, and data-driven treatment optimization to enhance care at home. Physicians and fertility clinics use this for remote patient monitoring and offering personalized medicine.
Another startup in Latvia has built a software platform to provide personalized evidence-based care pathways and clinical decision support. The startup’s tool generates patient-specific datasets by processing and analyzing multi-omics data from the patients’ health data, medical images, and diagnostics. The tool uses this dataset to recreate patient journeys and predict diagnosis outcomes. Another solution utilizes NLP to mine scientific information relevant to patient conditions. Medical practitioners and physicians use this to provide personalized healthcare at scale for conditions such as infertility, cancer, and autoimmune and metabolic diseases.
In conclusion, it is important that we understand the advantages and disadvantages of the increased presence of AI in healthcare:
Ability to analyze data and improve diagnosis: AI-equipped technology can analyze data much faster than any human, including clinical studies, medical records and genetic information that can help medical professionals come to a diagnosis.
Automation of administrative and routine tasks: AI can automate many routine tasks, such as maintaining records, data entry and scan analysis. With less time being spent on administrative tasks, medical professionals can place more focus on patient care.
Health monitoring and digital consultations: From wearable health tech, such as the Apple Watch and Fitbit, to digital consultations via your smartphone, AI can allow people to monitor their own health, while also providing healthcare professionals with essential data.
Training complications: There is the need of extensive training in AI technology with curated data sets in order to perform as expected. However, due to privacy concerns, it can be difficult to access some of the data necessary to provide AI learning with the breadth and depth of information it needs.
Needs human surveillance: Although AI has come a long way in the medical world, human surveillance is still essential. For example, surgery robots operate logically, as opposed to empathetically. Health practitioners may notice vital behavioral observations that can help diagnose or prevent medical complications.
Change can be difficult: In any industry, change can prove challenging. Since the healthcare industry is crucial for patient care, the medical community need proof that AI will be effective, as well as a plan to show investors that it is going to be worth the cost. Everyone working alongside AI technology will need to have an understanding of this technology and how it can assist them with day-to-day tasks.
Vulnerabilities: One of the biggest risks is the potential for data breaches. As healthcare providers create, receive, store, and transmit large quantities of sensitive patient data, they become the targets of cybercriminals. Bad actors can and will attack vulnerabilities anywhere along the AI data pipeline.
Another risk is the unique privacy attacks that AI algorithms may be subject to, including membership inference, reconstruction, and property inference attacks. In these types of attacks, information about individuals, up to and including the identity of those in the AI training set, may be leaked.
There are other types of unique AI attacks as well, including data input poisoning and model extraction. In the former, an adversary may insert bad data into a training set thereby affecting the model’s output. In the latter, the adversary may extract enough information about the AI algorithm itself to create a substitute or competitive model.
Finally, there is the risk of AI being used directly for malicious purposes. For example, AI algorithms could be used to spread propaganda, or to target vulnerable populations with scams or frauds. ChatGPT, referenced above, has already been used to write highly convincing phishing emails.
AI boasts of an impressive array of applications that span multiple industries. In the realm of healthcare, AI stands poised to usher in a transformative era. By automating routine tasks, it liberates healthcare professionals, allowing them to dedicate more time to direct patient care. The availability of comprehensive data empowers healthcare practitioners to take proactive measures in disease prevention. Real-time data analysis accelerates the accuracy of diagnoses. Furthermore, AI plays a pivotal role in reducing administrative errors, thereby conserving invaluable resources. The increased participation of SMEs in AI development is making this technology more versatile and knowledge-driven.
However, it's important to acknowledge that AI in healthcare is not without its challenges. These challenges encompass the necessity for human oversight, the potential omission of social variables, gaps in population data, and an increased vulnerability to increasingly sophisticated cyberattacks. Despite these hurdles, AI holds the promise of delivering remarkable benefits to the medical sector. Whether you're a patient or a healthcare provider, AI is making a positive impact on lives across the spectrum.
At Reflections, we've not only recognized the multifaceted potential of AI but have also demonstrated our proficiency in harnessing its power to help numerous clients revolutionize their businesses. To discover more about our AI capabilities and how we can assist you, please don't hesitate to get in touch with us. We'd be delighted to explore how AI can empower your specific needs and objectives.
Pasupuleti Brahma Teja, Junior Software Engineer
Mohamed Haseel, Associate Director - Digital Practice