AI Applications in Healthcare, and Future potential.
Artificial Intelligence (AI) is rapidly transforming healthcare by enhancing diagnosis, treatment, drug discovery, and administration. This article explores various AI applications across different segments of the healthcare sector, providing examples.
Table of Contents
Applications of AI for HealthCare Providers
Operational Efficiency and resource management:
AI can help hospitals manage patient flow for registration, admissions and discharges. AI can help manage availability of beds and health facilities more efficiently.
Examples:
UiPath: Robotic Process Automation (RPA) solutions like UiPath can automate administrative tasks, including claims processing, appointment scheduling, and billing, to reduce administrative workload and minimize errors.
Qventus uses AI to predict and optimize patient flow.
Chatbots like Zocdoc’s Clara can schedule appointments for patients, reducing the workload on administrative staff.
Virtual Health Assistants:
AI based virtual health assistants can serve as an important link between healthcare providers and patients. These can generate human-like responses in real-time for patient engagement. Generative AI powers chatbots and virtual assistants can assist patients with common health questions, appointment scheduling, send medication reminders, and answer frequently asked questions.
Example:
Babylon Health’s chatbot, offer healthcare information, appointment scheduling, and medication adherence support.
Mental Health Assessment:
AI can analyze text or voice inputs to assess an individual’s mental health and provide appropriate interventions.
Example:
Woebot use AI chatbots to provide mental health support, including mood tracking and therapy.
Structured Medical Records:
Generative AI can help generate structured data and medical records from unstructured data and records.
Examples:
MModal’s Natural Language Processing (NLP) system translates physician-patient conversations into structured electronic health records (EHRs), reducing the administrative burden on healthcare providers.
Epic Systems integrates AI into Electronic Health Records (EHR) systems.
Personalized Treatment:
An AI system can generate treatment recommendations for a specific medical condition based on the latest research and patient history. AI can optimize drug dosages for individual patients based on their genetic and clinical profiles.
Example:
IBM Watson for Oncology: This AI-powered system assists oncologists in making treatment decisions by analyzing large volumes of medical literature, clinical trial data, and patient records to suggest personalized treatment options for cancer patients.
Diagnosis, Imaging and Biomarker Discovery:
AI can analyze X-rays, MRIs, and CT scans, to assist radiologists in detecting abnormalities. AI can aid pathologists in identifying abnormal cells. AI can assist in identifying disease biomarkers and improving diagnostic accuracy.
Example:
Google’s DeepMind can analyze medical images and can assist ophthalmologists in diagnosing eye diseases.
PathAI is a company that offers AI-powered pathology solutions. Path AI uses AI to assist pathologists in diagnosing diseases like cancer.
Health Management:
Enabling healthcare providers to proactively manage the health of specific patient groups and improve outcomes.
Example:
Health Catalyst’s AI tools analyze EHR data to identify at-risk patient populations.
Wearable Health Devices:
AI is integrated into wearable devices like smartwatches and fitness trackers to monitor vital signs, detect irregularities, and provide early warnings about health issues such as atrial fibrillation or sleep apnea.
AI-powered devices can collect and transmit patient data to healthcare providers, enabling real-time monitoring of chronic conditions like diabetes or hypertension.
Medical Robots:
AI-driven surgical robots assist surgeons with precision and enhance minimally invasive procedures. AI-powered robots can support physical therapy and rehabilitation for patients recovering from injuries or surgeries.
Example:
da Vinci Surgical System, aids in complex surgeries like prostatectomies and cardiac procedures.
Smart Drug Delivery Systems:
AI-controlled drug delivery devices can optimize medication dosages and timing for better treatment outcomes. For instance, insulin pumps can adjust insulin levels based on real-time glucose data.
Teleconsultations and Remote Patient Monitoring:
AI-driven systems can assist healthcare providers in diagnosing and treating patients remotely. AI solutions can be used for real time patient monitoring.
Example:
Ada Health provides an AI-based symptom checker for patients.
Medical Research and Publication:
AI can assist researchers by generating summaries, abstracts, or even full articles based on research data, saving time and helping disseminate findings more efficiently.
Example:
canSAR database, which combines genetic and clinical data from patients with information from scientific research, and uses AI to make predictions about new targets for cancer drugs.
Device Maintenance:
AI is used to analyze device data and predict maintenance needs, ensuring that medical devices are in optimal working condition.
Example:
GE Healthcare uses AI to analyze data from medical imaging equipment to predict when maintenance is needed, reducing downtime and ensuring the machines are always available for patient care.
Medical Insurance:
AI-powered algorithms can assess and process claims quickly by analyzing submitted documents and data. This reduces the time it takes to settle claims and minimizes errors.
AI can detect fraudulent claims by identifying unusual patterns and inconsistencies in claims data. It helps insurers save money by reducing fraudulent payouts.
Example:
Aetna, a subsidiary of CVS Health, uses AI to analyze claims data and other health information.
Applications of AI in Pharmaceuticals and Biotech Industry
Drug Discovery:
AI can be used for drug discovery by generating new molecules with potential therapeutic properties. AI can predict drug molecules and target interaction.
Example:
IBM’s Watson for Drug Discovery uses AI to design new molecules with desired characteristics.
Atomwise uses AI for virtual screening. AI algorithms can predict how molecules will interact with biological targets.
Drug Repurposing:
AI can analyze existing drug data to find new therapeutic uses. AI models can predict interactions between existing drugs and potential new applications for drugs.
Example:
Benevolent Ai’s platform uses deep learning to discover new therapeutic uses for existing drugs. BenevolentAI has identified baricitinib, an arthritis drug, as a potential treatment for COVID-19.
Clinical Trial Optimization:
AI can identify suitable patients for clinical trials based on medical records and criteria. AI could also help in predicting patient recruitment and dropout rates, leading to more efficient trials.
Examples:
Deep 6 AI efficiently helps identify patients who meet the desired criteria for a clinical trial.
Tempus offers AI-driven predictive analytics for clinical trials.
Biological Data Analysis:
AI models can help analyze and interpret biological data, including genomics, proteomics, and metabolomics data.
Example:
Deep Genomics uses deep learning to analyze the functional impact of genetic variations.
Drug Manufacturing and Quality Control:
AI can drive quality control and process optimization in pharmaceutical manufacturing.
Example:
Merck has implemented AI for process optimization in pharmaceutical production.
Novartis employs AI for quality control in pharmaceutical manufacturing.
Drug Safety and Pharmacovigilance:
AI systems can analyze large datasets to detect adverse drug reactions and safety concerns.
Example:
Pfizer uses AI for pharmacovigilance to identify potential safety issues related to their products.
Drug Pricing and Marketing:
AI can help pharmaceutical companies develop pricing and market access strategies by analyzing various factors. AI can predict drug sales by analyzing various data sources.
Examples:
IQVIA offers AI solutions for pharmaceutical sales forecasting.
Companies like Artemis Health and Concerto HealthAI use AI to optimize drug pricing.
Supply Chain Management:
AI can optimize the pharmaceutical supply chain, ensuring timely delivery and reducing waste.
Example:
Companies like Genpact use AI for supply chain management.
Applications of AI For Public Health
AI-Powered Predictive Analytics:
AI can analyze healthcare data to predict disease outbreaks, patient readmissions, and more.
Examples:
Health Catalyst offers data analytics solutions for healthcare organizations.
BlueDot uses AI to predict the spread of infectious diseases like COVID-19 by analyzing travel data and news reports.
Environmental Health:
AI can analyze sensor data and satellite imagery to predict air quality issues and their impact on public health.
Example:
BreezoMeter provides real-time air quality information to the public.
Logistics and Supply Chain Optimization:
AI can optimize distribution by considering factors like temperature requirements, population density, and transportation options.
Example:
This is crucial for large-scale vaccination campaigns, as seen during the COVID-19 pandemic.
Suicide Prevention:
AI can analyze social media posts and public data to identify potential suicide risks and alert authorities or support organizations. Results across multiple studies indicate that AI consistently outperforms doctors at predicting suicide completion and suicide attempts. 1
Example:
MIT researchers built an AI model able to identify a depressed individual based on speaking patterns.
Future Potential of AI in HealthCare
The future potential of AI in the healthcare and pharmaceutical industry is vast and holds promise for significant advancements in patient care, drug development, and operational efficiency. Here are some key areas where AI is expected to have a transformative impact.
The future potential of AI in the healthcare and pharmaceutical industry is vast and holds promise for significant advancements in patient care, drug development, and operational efficiency. Here are some key areas where AI is expected to have a transformative impact.
Healthcare Resource Allocation and Management:
AI based solutions will see widespread adoption for resources management. Ai will be used to optimize resource allocation within healthcare institutions, helping to reduce wait times, improve bed management, and enhance overall operational efficiency 2
Clinical Decision Support and Medication Management:
The Agency for Healthcare Research and Quality (AHRQ) commissioned a systematic review of diagnostic errors in the emergency department (ED). “With 130 million U.S. ED visits, estimated rates for diagnostic error (5.7%), misdiagnosis-related harms (2.0%), and serious misdiagnosis-related harms (0.3%) could translate to more than 7 million errors, 2.5 million harms, and 350 000 patients suffering potentially preventable permanent disability or death.”3
AI based CDS softwares could assist healthcare professionals in making more accurate and timely clinical decisions by providing evidence-based recommendations and analyzing patient data. AI systems will offer decision support for medication selection, dosages, and monitoring.
Predictive Analytics for Patient Outcomes:
In near future AI-powered predictive analytics will help in forecasting patient outcomes, reducing hospital readmissions, and proactively managing high-risk patients4
Medical Imaging and Diagnostic Support:
AI algorithms will improve the accuracy and speed of medical image analysis, aiding radiologists and pathologists in diagnosing various conditions. Example: In an study conducted in South Korea, AI diagnoses of breast cancer versus radiologists was compared. The AI-utilized diagnosis was more sensitive to diagnose breast cancer. AI was better at detecting early breast cancer (91%) than radiologists 74%. 5
AI can accelerate the drug discovery process by identifying potential candidates, predicting their efficacy, and simulating their interactions within the human body. AI-driven drug discovery platforms are becoming increasingly prevalent. 6
AI-Enabled Robotic Assisted Surgery:
The use of AI in robotic-assisted surgery is expected to become more sophisticated, offering surgeons greater precision and capabilities for minimally invasive procedures. However, there is an urgent need for studies on large datasets and external validation of the AI algorithms used.7
Genomic Data Analysis and Public Health:
AI applied for genotype analysis holds immense promise in the realms of disease surveillance, prediction, and personalized medicine. Genomic data provides valuable insights into genetic markers associated with increased susceptibility to specific diseases. AI will be used to effectively monitor the emerging disease threats (such as COVID-19).8
Personalized treatment and precision Medicine:
AI will aid in the interpretation of genomic data and the development of personalized treatment plans, based on individual’s genetic profile. In medulloblastoma, the emergence of discrete molecular subgroups of the disease following AI-mediated analysis of hundreds of exomes, has facilitated the administration of the right treatment, at the right dosage, to the right cohort of paediatric patients.9 AI applications for precision medicine is the future of Personalized Health Care.
Telehealth and Remote Monitoring:
AI will play a crucial role in telehealth by enabling remote patient monitoring, facilitating virtual consultations, and enhancing the accuracy of telehealth diagnostics.10
Patient Engagement and Education:
AI-driven virtual health assistants will become more sophisticated in providing real-time patient engagement, answering healthcare questions, and offering educational content.11
These references offer insights into the research and developments in AI for clinical settings. As AI continues to advance, it is expected to significantly enhance patient care, clinical decision-making, and healthcare management within clinical environments.
References:
- https://pmc.ncbi.nlm.nih.gov/articles/PMC7549453/ ↩︎
- van Rossum, G. et al., 2017. AI in health: state of the art, challenges, and future directions. Yearbook of Medical Informatics, 26(1), 152-161 ↩︎
- Edlow JA, Pronovost PJ. Misdiagnosis in the Emergency Department: Time for a System Solution. JAMA. 2023;329(8):631–632) https://doi.org/10.1001/jama.2023.1678 ↩︎
- Golas, S.B. et al., 2019. Predictive Analytics in Healthcare: How Can We Know It Works? Journal of Healthcare Management, 64(2), 110-123 ↩︎
- Kim H-E, Kim HH, Han B-K, Kim KH, Han K, Nam H, et al. Changes in cancer detection and false-positive recall in mammography using Artificial Intelligence: a retrospective, Multireader Study. Lancet Digit Health. 2020;2(3). https://doi.org/10.1016/s2589-7500(20)30003-0 ↩︎
- Stokes, J.M., Yang, K. et al., 2020. A Deep Learning Approach to Antibiotic Discovery. Cell, 181(2), 475-483 ↩︎
- Moglia, Andreaa,*; Georgiou, Konstantinosb; Georgiou, Evangelosc; Satava, Richard M.d; Cuschieri, Alfrede,f. A systematic review on artificial intelligence in robot-assisted surgery. International Journal of Surgery 95():p 106151, November 2021 https://doi.org/10.1016/j.ijsu.2021.106151 ↩︎
- Abubaker Bagabir S, Ibrahim NK, Abubaker Bagabir H, Hashem Ateeq R. Covid-19 and Artificial Intelligence: genome sequencing, drug development and vaccine discovery. J Infect Public Health. 2022;15(2):289–96. https://doi.org/10.1016/j.jiph.2022.01.011.) ↩︎
- Johnson KB, Wei WQ, Weeraratne D, Frisse ME, Misulis K, Rhee K, et al. Precision Medicine, AI, and the future of Personalized Health Care. Clin Transl Sci. 2021;14(1):86–93. https://doi.org/10.1111/cts.12884.) ↩︎
- Wosik, J. et al., 2020. Telehealth transformation: COVID-19 and the rise of virtual care. Journal of the American Medical Informatics Association, 27(6), 957-962 ↩︎
- Bickmore, T.W. et al., 2010. Patient and consumer safety risks when using conversational assistants for medical information: an observational study of Siri, Alexa, and Google Assistant. Journal of Medical Internet Research, 22(10), e22086) ↩︎