Published on 23 Feb 2024
min read
Artificial Intelligence (AI) is redefining the global healthcare landscape. The expanding body of medical data, gathered from electronic medical records, picture archiving and communication systems, hospital management information systems, claims records, and patient surveys, is helping enable the rapid formulation of effective AI models.
Text data from electronic health records, IoT devices and medical literature provides valuable information for AI-assisted diagnostics, decision support, and research. Medical imaging systems have a similar application. Videos from surgeries or from patient monitoring can also be analyzed for skill assessments, training surgical bots, and refining existing quality paradigms. Data from voice recordings can be leveraged for diagnosing speech related anomalies, enabling automated documenting procedures and patient interaction analysis. AI on human gestures can be analyzed to enable hands-free control of medical devices or images while performing sterile procedures or for assessing the motor functions of patients under physical therapy. The possibilities are truly endless.
Apart from optimizing clinical practices, AI finds multi-level applications in the healthcare industry. AI can help streamline operational workflows for better healthcare management and optimize clinical trial processes by assisting in patient recruitment through enhanced identification of suitable candidates. The applicability of AI in healthcare today therefore cuts across the product development lifecycle – right from new product development, manufacturing, managing operations, and providing post market support, including, training users, compliance handling, and predictive asset maintenance.
Here are the top seven healthcare areas that have promising future for infusion with AI-enabled capabilities.
AI-Powered Diagnosis
AI-enabled Electronic Health Record (EHRs)are already supporting clinicians by providing them with valuable insights on suggestive diagnosis, disease risk, and progression pathways based on the patient’s signs, lab reports, medical history, family history, and data from sensors.
In the medical imaging domain, AI is enabling radiologists to make more accurate and faster diagnosis by contributing towards smart image segmentation, quality enhancement, early disease detection, image triaging, review, and reporting.
AI can also speed up the interpretation of pathological slides or in pap smear screening, helping reduce the time to result, the need for skilled personnel, and overcoming the bias owing to subjective factors. In disease diagnosis, AI can enable early-stage disease identification, leading to reduced mortality, morbidity, and overall cost of care.
Precision Medicine
The FDA defines precision medicine as “an innovative approach to tailoring disease prevention and treatment that takes into account differences in people's genes, environments, and lifestyles.” Using AI, more specific root causes can be identified and personalized intervention plans can be tailored based on the individual’s own lifestyle, medical history, family history, genomics, and other factors.
One such example is targeting the heterogenous nature of cancer. Vast biological datasets that capture molecular tumor characteristics, genomics, proteomic and transcriptomics, among many other key variables, are analyzed by AI models to identify patient-specific mutations. The results derived are then used for devising patient-specific therapies.
This shift from a ‘one-size-fits-all’ to a ‘targeted’ approach thus has the potential to minimize trial-and-error, improve treatment efficiency, and improve health outcomes.
Patient Engagement and Treatment Adherence
AI powered-virtual assistant tools are being leveraged to engage with patients and address their queries, schedule appointments, and provide health promotional information.
Adherence to medication is crucial for successful health outcomes and for preventing anti-microbial resistance. AI, along with computer vision powered smart phone cameras, can help validate the timely and correct ingestion of drugs by patients. These real-time monitoring methods have shown better results than self-reporting, checking for pill counts, and straight-forward reminder notifications.
Administration and Regulatory Affairs
AI can be leveraged for appointment scheduling, billing processes, and insurance claims processing. Predictive analytics helps in resource allocation and demand forecasting, improving overall operational efficiency. AI aids in managing medical records by automating tasks such as data extraction, data retrieval, medical coding and ensuring data accuracy. The technology can also be used for the continuous monitoring of data to identify potential regulatory risks and deviations. Subsequently, audit reports can be generated with minimal manual efforts for facilitating quicker response to queries.
Population Health Management
Public health and community medicine professionals continuously work towards preventing diseases. AI can be leveraged to categorize the population into cohorts based on demographics and medical characteristics to identify disease risk, suggest data-driven public health initiatives, and enable targeted community outreach by creating personalized educational materials for patients, outlining their medical conditions and treatment options including overcoming language barrier by translating medical information.
Predictive Maintenance of Medical Equipment
The concept of predictive maintenance of assets can be leveraged to improve the efficiency and reliability of various healthcare equipment. Historic data from sensors can be analyzed to identify patterns, detect anomalies, and predict potential future failures which can be addressed proactively before they impact patient care. Maintenance tasks can be accordingly prioritized, helping minimize downtime, optimizing equipment performance, reducing costs, and enhancing patient safety.
Drug Discovery
AI can be used to analyze large datasets from clinical trials and other real-world sources to identify new molecules and chemical structures that are likely to be effective on a specific target. Virtual compounds can be generated by AI algorithms and tested in silico, i.e., a computer simulation rather than a laboratory, helping accelerate drug development journeys while reducing cost.
Healthcare data streams today can be characterized by their significant volume, variety, and velocity. This is both an opportunity and challenge, especially in ensuring effective management and analysis. As AI continues to evolve and becomes more integrated in healthcare workflows, it will be essential to address issues related to data privacy, security, and regulatory compliance. Ensuring that the algorithms are safe, reliable, and compliant with healthcare standards is of critical importance.
We feel that AI would play a key role in transforming the future of global healthcare, enabling improved patient care, enhanced diagnostics, and a more efficient healthcare delivery paradigm. The intersection of the technology with medicine has the potential to transform the way healthcare is delivered, making it more personalized, accessible, and effective for patients and caregivers across the globe.
Let’s #engineerthechange in healthcare, together!
