We analyzed the World Health Organization report "Ethics and governance of artificial intelligence for health Guidance on large multi-modal models" dated January 18, 2024 and found that Great Leveler's developments in Asilisc Secure® fully meet WHO requirements and physicians' needs.
"LMMs could be used in routine diagnosis, can be done quickly, because an LMM can scan a patient’s full medical record much more quickly than can doctors.
Read messages from patients and to draft responses from doctors to reduce the time that medical staff spend in replying to patient queries. This practice is intended to decrease the burn-out of health-care workers, who field thousands of messages daily, and to enable them to focus on their clinical duties (“keyboard liberation”). Thus, when a patient message is received, the LMM displays a draft reply based on both information from the patient and a version of their electronic medical history.
Answering standardized “curb-side consult” questions and providing information and responses on the initial presentation of a patient or to summarize laboratory test results.
Patients already take significant responsibility for their own care, including taking medicines, improving their nutrition and diet, engaging in physical activity, caring for wounds or delivering injections.
LMMs accelerate the trend towards use of AI by patients and laypeople for medical purposes. Individuals have used Internet searches to obtain medical information for two decades. Therefore, LMMs could play a central role in providing information to patients and laypeople, integrating them to searches.
Large language model- powered chatbots could replace search engines for seeking information , including for self-diagnosis and before visiting a medical provider.
LMM-powered chatbots, with increasingly diverse forms of data, serve as highly personalized, broadly focused virtual health assistant.
Specific LMM-powered chatbots could provide treatment in, for example, mental health.
Medicine exposes physicians and other health-care professionals in many settings to ever-growing paperwork for numerous obligations for recording patient information and data in electronic health records, billing in private, insurance or public health-care systems and other administrative tasks.
While many such obligations, such as completing electronic health records, were intended to “liberate” health- care professionals, most are now a major cause of physician and health-worker burn-out.
Documentation constituted one fourth to one half of a doctor’s time and one fifth of a nurse’s time.
LMMs have been singled out as a means of returning to health-care professionals their most valuable commodity – time – both to reduce burn-out, to allocate more time to providing care to each patient or to see more patients.
One physician reported that “AI has allowed me, as a physician, to be 100 percent present for my patients” and that the software had freed up to 2 h daily.
Examples of current and anticipated uses of LMMs include:
• better communication to assist in translation or improving clinician–patient communication by simplifying medical jargon and making communication more “patient-friendly”;
• to fill in missing information in electronic health records ; and
• with other forms of AI, to draft clinical notes after each patient visit (virtual or in
person).
• Use of LMMs is also expected to include pre-emptive writing of automated prescriptions and appointments, billing codes, scheduling of tests, pre-authorization by insurance companies, procedure notes and discharge summaries."