Applications of AI in Healthcare and Finance

Leverage Generative AI to foster equity in Healthcare & Life-sciences- A look at the possibilities: As the industry works on technical feasibility vs. value recognized the market of Gen AI in #HCLS is projected to grow by 85 % from its current valuation of $1B to $22B by end of 2027 (source: https://www.bcg.com) #genai to soon emerge as a key tool for healthcare & hospital systems to boost productivity, free critical resources from mundane tasks and reduce administrative burdens of a whopping $1 trillion. (source: https://lnkd.in/eXcq4Ksz) These burdens affect patients ranging from their reduced access to health care to subsequent effects of their mental wellbeing. Citi Global Insights (CGI) predicts 25-30% of admin cost savings with AI adoption. This can benefit the staff and they will pass it as improved patient experience. Other areas where AI will benefit is diagnostics, critical assessment of populations, optimizing drug development and evaluation. The recent FDA acceptance to algorithmic drug development pilot tool expedites availability of safe and effective treatments at scale. (Refer: https://lnkd.in/e2Tpwykq) Opportunities of maximum value realization Commercial and Medical Affairs 1. Patient & Physician customer journey orchestration 2. HCP Engagement through content classification 3. Patient Doctor Case summarization 4. Automated Customer Service 5. Real world Evidence Generation 6. Voice Assistants, Human Robots & Conversational AI 7. Localizations & Translations Research & Development 1. Discovery of new compounds, Protein & Molecule Generation for Drug Development 2. Synthetic generation of Healthcare assets 3. Self Service Data Analytics tools & services Pharma Manufacturing & Supply Chain 1. Resource / Material forecasting 2. Inventory Management & Intelligent Tracking 3. Automated Logistics Management 4. Operations Excellence Clinical Trials and Operations 1. Patient Outreach & Recruitment 2. Federated knowledge sharing 3. Self-serve onboarding 4. Protocol Generation & Optimization 5. Patient Support Services Quality Control & Surveillance 1. Deviations Management 2. Product Quality Reporting & Analysis 3. Staff productivity & Quality Review optimization 4. Standard operating procedures review management This interesting correlation between increased equity & diversity in the care continuum and adoption of Generative AI across the value chain will be fast tracked with patients, care givers and clinicians asking for more value for less cost & effort. #healthcareequity #aiadoption #clinicaldecisionsupport #patientcentricity #customerexperience

AI in Healthcare Sepsis infection is one of the largest causes of deaths in hospitals, estimated 11 m deaths/year. AI can help. After a patient checks into the emergency ward of a hospital, AI can look into 150 patient variables like lab results, vital signs, current medications, medical history, demographics to predict risk profile for possible sepsis. Staying vigilant has brought down sepsis incidence in hospitals ! I just gave you one example of how AI can help in healthcare. Few more … DIAGNOSIS – GE is using gen AI for multi modal integration from sources like imaging, genomics, pathology to help a clinician in diagnosis. Another ex is ischemic stroke where the image has to be read by a radiologist quickly to identify the clot in the brain. This can be done by AI when radiologists are busy or limited in number. This speed in diagnosis can save lives. REMOTE PATIENT CARE – We are know that there is a demand & supply mismatch in doctors and nurses. Monitoring devices with AI can send a notification to the healthcare professionals to visit the patient as and when needed saving time. Such efficient remote care limits the number of days patient has to spend in the hospital thereby reducing cost of stay which is very helpful for patients and insurance companies. AI-trained Chatbots have shown the potential to answer patient questions when doctors are not available. DRUG DISCOVERY – With millions of people waiting for the approval of new medicines, bringing a drug to market still takes on average more than 10 years and costs over 1.9 billion Euros on average. Merck has launched a drug discovery software that identifies compounds from over 60 billion possibilities based on key properties like non toxicity, solubility and stability in the body. Insilico Medicine, a biotech company out of Hong Kong is the first company where an AI discovered drug has entered phase II clinical trials in US and China. CLINICAL TRIALS - AI can help in trials through patient recruitment (through analysing patient health records and identifying most suitable candidates thereby reducing recruitment time), patient monitoring (by identifying adverse events or complications real time), protocol design, trial site selection, predict enrolment rates, data analysis (AI can often spot patterns and correlations that might be missed by humans) and cost efficiency by automating a lot of the admin paperwork involved in trials. MANUFACTURING– AI can predict machine failure and schedule equipment maintenance before breakdown occurs. It can inspect products and detect defects more accurately than humans, it also ensures timely delivery of raw materials through analysis and prediction of typical delays due to logistics, weather, shortages etc. Way ahead - I have only skimmed the surface & covered a few areas above. There is no doubt that AI can transform healthcare in many way however the challenges of data privacy and related ethics, prohibitive costs and unclear regulations remain.

Earlier this year the Luminary Labs team spoke with 50+ experts about applied AI and conducted a preliminary analysis of market size, growth potential, and AI readiness. What is applied AI, you ask? Applied AI is the practical implementation of AI to solve real-world problems and achieve specific goals. This is in contrast to foundational AI (large-scale, adaptable models that apply to any task.) And while both have a role in transforming life and work, applied AI is where value will be created. To give you an idea of what this looks like in practice, Janna Gilbert, Rebecca Meyer, Ben Alsdurf, Jessica Hibbard & I share the potential for applied AI in 3 industries that have an outsized impact on America’s economy and society. Here's a sneak peak: 🏥 Health: America’s population is aging: The birth rate is at its lowest point in a century, and people 85 and older are the fastest-growing segment of the population. As the aging population continues to grow, the U.S. will need a larger, more efficient workforce that can better serve patients and caregivers. Automation reduce administrative burden, allowing existing staff to focus on direct patient care.  In life sciences, AI has enabled growth of in silico drug development models; as a result, biotech companies can use fewer resources to advance promising therapeutics. 🏦 Finance: The financial services industry is a bedrock of the U.S. economy, accounting for 6.7 million jobs and more than 7% of gross domestic product. However, traditional banking institutions are facing competitive pressure. Across the industry, market volatility, global uncertainty, and cybersecurity threats are growing concerns. Applied AI presents transformative opportunities across the financial sector, which relies heavily on large sets of data. With machine learning and applied AI, those massive data troves could be used to create innovative new offerings for customers. Financial institutions are already implementing AI for fraud prevention and security, legal services, trading and portfolio optimization, and enhanced customer interactions through AI agents. 📺 Media & Culture: The $649 billion U.S. media and entertainment market is the largest in the world, and the industry employs 2 million people. In addition, advertising, public relations, and related services employ ~500k Americans. AI tools can enhance creative workflows, personalize content at scale, and unlock new business models. On the other hand, creative roles face unprecedented pressure, with generative AI directly threatening traditionally billable services such as copywriting, graphic design, photography, and video production. Any effective applied AI strategy in creative media must carefully navigate job displacement, bias and inaccuracy risks, intellectual property infringement, and data privacy, among others. Read the full article in this week's Lab Report 👇 https://lnkd.in/eQ7M7JbN

Dossier by Deloitte AI Institute highlights business-ready AI use cases and emerging applications expected to impact the industry. 𝐒𝐢𝐱 𝐦𝐚𝐣𝐨𝐫 𝐰𝐚𝐲𝐬 AI creates business value: cost reduction, speed to execution, reduced complexity, transformed engagement, fueled innovation, and fortified trust. 𝐂𝐮𝐫𝐫𝐞𝐧𝐭 𝐒𝐭𝐚𝐭𝐞 𝐨𝐟 𝐀𝐈 𝐀𝐝𝐨𝐩𝐭𝐢𝐨𝐧- More than 60% of life sciences companies spent over $20 million on AI initiatives in 2019- Over 50% expected to increase AI investments in 2020- Top outcomes companies seek: enhancing existing products (28%), creating new products/services (27%), making processes more efficient (22%) - 𝐌𝐚𝐢𝐧 𝐜𝐡𝐚𝐥𝐥𝐞𝐧𝐠𝐞𝐬: identifying high-value business cases (30%), data challenges (28%), integration into organization (28%) 𝐓𝐨𝐩 𝐀𝐈 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 𝐢𝐧 𝐋𝐢𝐟𝐞 𝐒𝐜𝐢𝐞𝐧𝐜𝐞𝐬 & 𝐇𝐞𝐚𝐥𝐭𝐡𝐜𝐚𝐫𝐞 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐓𝐫𝐢𝐚𝐥𝐬 𝐚𝐧𝐝 𝐌𝐚𝐧𝐮𝐟𝐚𝐜𝐭𝐮𝐫𝐢𝐧𝐠:- Digital Data Flow for Clinical Trials: Automate trial data integration, populate standardized digital elements, and generate trial artifacts - Drug Manufacturing Intelligence: Use algorithmic models and sensor data to predict manufacturing deviations and suggest corrective actions Marketing and Customer Engagement:- Drug Marketing Omnichannel Engagement: Predict best ways to engage patients and healthcare professionals while optimizing marketing spend - Voice of the Patient Insight: Analyze social media feedback, complaints, and adverse events to improve product design 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞 𝐚𝐧𝐝 𝐀𝐝𝐦𝐢𝐧𝐢𝐬𝐭𝐫𝐚𝐭𝐢𝐨𝐧:- Proactive Risk and Compliance: Automate analysis and aggregation of data for identifying risk and compliance issues - Patient Engagement: Improve all aspects of patient interaction from scheduling to care coordination Healthcare Revenue Cycle Optimization: Automate claims submission and payment processes 𝐂𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬:- Computer Assisted Diagnosis: Use AI to diagnose medical conditions more efficiently and accurately - Precision Medicine & Personalized Health: Use predictive insights for proactive diagnosis, prevention, and treatment based on individual characteristics - Hospital Management: Predict patient volume peaks and valleys to optimize staffing and resource allocation 𝐄𝐦𝐞𝐫𝐠𝐢𝐧𝐠 𝐀𝐈 𝐔𝐬𝐞 𝐂𝐚𝐬𝐞𝐬 Drug Discovery and Development: - Biomarker Discovery - Synthetic Biology - Virtualized Drug Discovery Lab - Predictive Behavioral Model Healthcare Delivery: - Digital Healthcare Providers - Digital Pathology - Patient Vitals Monitoring - Medication Compliance & Remote Patient Monitoring - Diagnostic Image Enhancement in Radiology 𝐒𝐮𝐩𝐩𝐥𝐲 𝐂𝐡𝐚𝐢𝐧: - Self-healing Supply Chains Key Success Factors - Data is the foundation - Start small but think big - IT infrastructure, talent and skill sets, and strategic alliances/ecosystems - Focus on patient experience Nitin Mittal | Irfan Saif | Beena Ammanath | Asif Dhar | Daniel Ressler