Using integrated technology to increase patient outcomes
From the use of voice recognition devices such Alexa and Siri, tailored recommendations on streaming platforms and improved internet searches, through to higher end analytics and data management across a range of businesses, artificial intelligence (AI) already ingrained in many facets of our life. Within the healthcare sector, the creation of AI-enabled solutions presents a major area of promise.
Artificial intelligence in medical science
Including Generative AI, artificial intelligence is fast changing and has the “potential to enhance healthcare outcomes by improving clinical trials, medical diagnosis and treatment, self-management of care, and personalized care.”1 1 In many facets of healthcare, including helping doctors make educated decisions regarding patient treatment and care and reviewing MRI and x-ray produced images, artificial intelligence already acting as a co-pilot.
Innovations driven by artificial intelligence and machine learning, or ML, at a pace never seen in past times.AI is fact rather than science fiction. Head of Life Sciences and Regulatory Team Partner Alex Denoon Bristows LLP
Using artificial intelligence in daily life usually results in a not particularly noteworthy effect should things go wrong. For instance, it is quite improbable that Alexa will hurt somebody if she performs an unwelcome song or struggles with memory. But in healthcare, when AI goes awry, the effects can be really noticeable. Devices and products that employ artificial intelligence and the method the data used to train them must controlled and compliant to safeguard privacy and data, guarantee goods fit for all users, and so keep everyone safe.
For the field of medical technology, artificial intelligence offers much. Right now, the top of the iceberg is all that is visible. Gabriel Adusei is the Triune Technologies Limited founder.
Control of artificial intelligence in medical environments
Regulations can lag behind the goods and tools required to control the fast technological development. Holding back advances of life sciences firms to keep people safe and letting items and devices into the market so they may help those who need them strike a balance.
AI itself can used to ensure quality and compliance, help companies evaluate the vast volumes of data needed for verification and validation, help them be ready for regulators and auditors, and help them distribute their products to patients.
“The regulator is the last arbitrager of our safety.” Director, Regulatory and Compliance, ABHI Phil Brown
Emphasizing the “importance of establishing AI systems’ safety and efficacy,” the World Health Organization (WHO) published Regulatory considerations on artificial intelligence for health in October 2023, rapidly making appropriate systems available to those who need them and so promoting dialogue among stakeholders.
Recently, IQVIA Technologies asked a group of quality and regulatory professionals to discuss the application of artificial intelligence in healthcare. Emphasizing the legal, regulatory, and quality domains, they address utilizing a realistic, risk-based approach of artificial intelligence in healthcare, considering a mix of pragmatic solutions for now and where AI might lead us, and the regulators, tomorrow. This is especially interesting when the sector uses intelligent driven solutions to empower AI augmented experts operating with eQMS and RIM solutions to run with increased degrees of compliance, efficiency, and effectiveness.
This energetic and occasionally divisive conversation explores the advantages and drawbacks of artificial intelligence, touches on whether AI could replace a human workforce including regulators – including legislators – in healthcare and looks at how regulators might be flexible and think creatively about how they control AI products, so providing maximum benefit to patients while maintaining public safety.
Applying artificial intelligence in the medical field: lessons discovered
Drawing on our client work on uses of artificial intelligence in health care, we provide these observations:
Factor in extra time and money for early adoption; even very modest initiatives need more time and effort upfront to conduct business case validations and proof of concept.
Using open-source technologies and restricting customizing will help to lower cost and complexity.
Create solutions with capacity for longer transactions and peak volumes while yet averaging transaction length and volumes.
Engage staff members with expertise in both technology and health care who have better knowledge of end users’ wants and preferences as well as technological solution alternatives.
Choose the data carefully used for training any artificial intelligence or machine learning model: Make sure it does not improperly train and skew the model and fairly depicts the production statistics.
Since model training is a continuous process, predicted return on investment (ROI) should incorporate the duration and time frame.
AI’s advantages for medical treatment
From patient self-service to chat bots, computer-aided detection (CAD) systems for diagnosis, and image data analysis to find candidate molecules in drug discovery, AI is already at work increasing convenience and efficiency, lowering costs and errors, and generally making it easier for more patients to receive the health care they need.
Although NLP and ML are being applied in the medical field, their potential to:
raise clinician and provider output as well as quality of treatment.
Boost patient involvement in their own treatment and simplify patient access to it.
Speed up development of new pharmaceutical treatments and save expenses to help create them.
Using analytics to mine important, hitherto unexplored stores of non-codified clinical data will help to personalize medical treatments; while each AI technology can provide great value on its own, the combined potential resides in the synergies produced by using them all over the patient journey, from diagnosis, to treatment, to continuous health maintenance.
Three spheres for uses of artificial intelligence in the medical field
Applications in health care fit three general categories as artificial intelligence permeates everything from our cellphones to the supply chain1:
- AI focused on the patient
- AI with an eye toward clinicians
- Administrative- and operational-oriented artificial intelligence
Simple to sophisticated tasks ranging from answering the phone to medical record review, population health trending and analytics, therapeutic drug and device design, reading radiology images, making clinical diagnosis and treatment plans, and even patient interaction define the future of artificial intelligence in health care.
The direction artificial intelligence in health care is headed:
An artificial intelligence (AI), natural language processing (NLP), and machine learning (ML) health care-oriented review
Applications in health care both now and going forward: effects on patients, doctors, and the pharmaceutical sector
Examining how these technologies affect medical practice over the next ten years can help one to better understand the direction of artificial intelligence in health care.