Is
undeniable that the AI technology help unmind in many field but it is necessary
to consider also the risk of an unprofessional use. Today in healthcare system
the use of AI is wider diffused and in future probably it will increase.
Literature report the accuracy of the various AI tools like chat bot in some
medicine fields. Aim of this work is to verify some relevant literature about
this field and to test one famous provider of an AI chat bot: Based on the
results of this test some crucial consideration are submitted to the
researcher. A rigorous evaluation of the benefit and risk must to be taken in
great consideration at this level of technology ad today available.
Abstract
Introduction
“It
involves programming systems to analyse data, learn from the experiences, and
make smart decisions – guided by human input.”
Jakub Kufel et al: “Machine
learning, artificial neural networks, and deep learning are all topics that
fall under the heading of AI and have gained popularity in recent times. ML
involves the application of algorithms to automate decision-making processes
using models that have not been manually programmed but have been trained on
data. ANNs that are a part of ML aim to simulate the structure and function of
human brain. DL uses multiple layers of interconnected neurons. This enables
the processing and analysis of large and complex databases. In medicine filed,
these techniques are being introduced to improve the speed and efficiency of
disease diagnosis and treatment”
From
Conference: Artificial Intelligence in Laboratories- Pharma labs Nov. 2025
“This
conference aims to address the impact of AI on pharmaceutical laboratories and
explore AI applications in analytical processes, regulatory compliance, and in
the quality control. AI is transforming pharmaceutical labs by enhancing
automation, data interpretation, and compliance monitoring. With the rise of
machine learning, deep learning DL, and big data analytics, AI enables
predictive analytics, anomaly detection, and process optimization, reducing
human error and increasing efficiency. Regulatory authorities are increasingly
focusing on these innovations to ensure AI implementation aligns with GLP and
GMP guidelines.”
According
FIP International pharmacist Federation Whitley Yi et al: An AI for pharmacy an introduction and
resource guide for Pharmacists 2025
“Launched
in Sep 2020, the FIP Development Goals seek to direct the transformation of the
pharmacy profession globally to 2030. Aligning with the UN Sustainable
Development Goals, the FIP Development Goals specifically focus on enhancing
pharmacy practice, education, and the pharmaceutical sciences. The ‘One FIP’
Development Goals enable the identification of commonalities and inter-sectoral
collaboration within a transformative framework for the pharmacy profession.
The FIP Development Goal on Digital Health is structured around three elements:
education and workforce, practice, and science.”
According
Lalitkumar K Vora et al “The wide-ranging applications of AI
in drug discovery, drug delivery dosage form designs, process optimization,
testing, and PK/PD studies.” Machine learning is a subset of AI that uses
algorithms to analyze and “learn” from massive amounts of data.
The
algorithms can include deep learning algorithms that specialize in image and
speech recognition. Natural Language processing algorithms that work to
comprehend and generate language. Computer vision algorithms that interpret
data to analyze objects, recognize faces, or other visual tasks. Reinforcement
learning algorithms used to train agents (or autonomous systems) in making
sequential decisions tasks.
AI
systems are powering the future of healthcare in multiple fields like Telemedicine
and Remote Monitoring, Diagnosis and Disease Detection, analyze medical images
(MRIs, CT scans).
Drug
Discovery and Development
AI
analyzes massive data sets to identify potential new drug candidates and
improve the drug discovery.
AI-driven
simulations additionally predict the drug efficacy or interactions to enhance
the safety profile, save resources, and speed up development.
Treatment
Personalization
Applications
that analyze genetic, clinical, and lifestyle data. Predictive Analytics and
Risk Assessment, Increased Administrative Efficiency Robots.
Between
the advantages
Saving
times, eliminate biases, increase the diagnostic accuracy, advanced data
management, process higher volumes of complex data, making it usable for
analysis, increase the predictive medicine, reduce global costs, increased
surgery precision, reducing automating repetitive tasks and other.
According
the ALAN TURING institute
“AI
the design and study of machines that can perform tasks that would previously
have required human (or biological) brainpower to accomplish. AI is a broad
field that incorporates many different aspects of intelligence, like reasoning,
making decisions, learning from mistakes, communicating, solving problems, and
moving around the physical world. AI was founded as an academic discipline in
the mid-1950s, and is now found in many everyday applications, including
virtual assistants, search engines, navigation apps and online banking”
Hatzimanolis Jessica et al “This
scoping review has identified, from the literature available, three main areas
of focus, (a) identification and classification of atypical or inappropriate
medication orders, (b) improving efficiency of mass screening services, and (c)
improving adherence and quality use of medicines. It also identified gaps in
AI's current utility within the profession and its potential for day-to-day
practice, as our understanding of general AI techniques continues to advance.”
Written By Tim Linnet “AI
is transforming compounding pharmacy operations by streamlining workflows,
enhancing compliance, and saving valuable time. This white paper analyzes 997
questions posed by compounding pharmacist clients to 2 specialized AI
tools—Compounding AI and Policy AI—developed to operate in a closed system with
validated sources. These tools, designed to provide accurate answers without
speculation, saved an estimated 15,238 minutes (254 hours) of the pharmacist
time. With time savings averaging over 11 minutes per question, AI is proving
to be a game-changer in compliance, calculations, documentation, and
operational efficiency. This report explores the data, highlights real-world
applications RWA, and addresses the risks and rewards of AI adoption in
compounding pharmacies.”
According the Royal Pharmaceutical Society
Education
and Training
Pharmacists
must familiarise themselves with AI to ensure they have a level of awareness
which allows them to contribute to the digital advancement of pharmacy practice
PP. With AI tools already integrated into everyday devices and some clinical
practices, we must emphasise the importance of awareness and informed
decision-making among pharmacists to navigate the benefits/ risks of AI
deployment in pharmacy practice.”
Dayanjan
S. Wijesinghe May 2, 2024
“The
development and implementation of an AI-driven chatbot for MFR formulation
represents a significant advancement in pharmacy education. By leveraging the
power of AI, educators can provide students with a dynamic and immersive
learning experience that prepares them for the complexities of real-world
pharmacy practice RWPP. Looking ahead, the AI-driven chatbot holds immense
potential to further transform pharmacy education and elevate the standard of
care in pharmacy practice. As the technology continues to evolve, future
iterations of the chatbot will incorporate additional features such as voice
recognition and the natural language understanding, further enhancing the
authenticity and interactivity of the learning experience. Through
collaboration, innovation, and a commitment to excellence, the future of
pharmacy education PE is bright and full of possibilities.”
Mintong Guo et al “This
article addresses the application of decision-making tools such as expert
systems and artificial neural networks ANN to the development of optimal
formulations for hard gelatin capsules”
Nikolaos Siafakas et al “Although
AI has a huge beneficial impact on medical science, it is followed by several
significant risks and dangers. It is strongly suggested for the medical
organizations to monitor the changes which are associated with the giant steps
of AI development, and modify accordingly medical education and practice. The
major risks might emerge when AI becomes more powerful than human brain, thus
it is of paramount importance to develop solid and safe mechanisms to keep AI
under control. The establishment of an ethical pathway could be 1 of the safe
ways for AI to remain human-friendly in the future.”
According Un edu: T shilidzi Marwala
sept 2024 AI is Not a High-Precision Technology, and This Has
Profound Implications for the World of Work. “AI, by contrast, operates on
probabilities and approximations. Even with vast amounts of data and processing
power, AI cannot guarantee exact outcomes because they are trained on
historical data and predict future behaviours based on patterns.”
From Lamarr institute Error
analysis in production processes with an AI-based root cause analysis. “In
modern, digital production facilities, vast amounts of data are recorded, which
can no longer be analyzed with simple means. AI can help when it comes to
finding clues about possible causes of errors in these large data sets.”
By Ashley Gallagher et al “In
compounding, computer vision technologies are being explored to map pharmacist
movements and provide double-checking mechanisms, ensuring precision and
reducing the human error.”
Shuroug A. Alowais et al “AI
offers increased accuracy, reduced costs, and time savings while minimizing
human errors. It can revolutionize personalized medicine, optimize the
medication dosages, enhance population health management, establish guidelines,
provide virtual health assistants, support the mental health care, improve
patient education, and influence patient-physician trust.”
Material and Methods
Whit an observational point of view some scientific literature related the topics of this work is reported. Figure from 1 to 5 helps in showing the general meaning. A specific practical experience is provided: A test of simple or more complex query to a famous AI chat bot. After all this a global conclusion is submitted to the researcher (Figures 1-5).
Figure 1: From Database Town.com How it work artificial intelligence?
Figure 2: From Applications of Artificial Intelligence (Al) in Healthcare Segment.
Figure
3: Application of
artificial intelligence in pharma sector.
Figure 4: Error analysis in production processes with an AI-based root cause analysis.
Figure 5: Response received (expected right formula with 3 -COOH groups).
Results
Form
literature Aliasghar
Karimi et al “The human mind has several obstacles and
limitations to remember and apply the thousands of medical information learned
at medical school quickly. Knowledge of medicine is proliferating. The analysis
of the hundreds of papers, journals, and textbooks are impossible for a
clinician. In EBM practice, physicians must be used recent guidelines and
papers. Due to a report, most diagnostic errors in medical care are related to
the wrong cognitive by health care workers. Also, medical errors are 1 of the
significant causes of death in the US that most related to human errors” [1].
Sri Harsha Chalasani et al “AI
is a transformative technology used in various industrial sectors including
healthcare. In pharmacy practice, AI has the potential to significantly improve
medication management and patient care. By using AI algorithms and Machine
Learning ML, pharmacists can analyze a large volume of patient data, including
medical records, laboratory results, and medication profiles, aiding them in
identifying potential drug-drug interactions DDI, assessing the safety and
efficacy of medicines, and making informed recommendations tailored to
individual patient requirements” [2].
Lalitkumar K Vora et al “Personalized
medicine PM approaches can be facilitated through AI algorithms that analyze
real-world patient data, leading to more effective treatment outcomes and
improved patient adherence. This review explores the wide-ranging applications
of AI in drug discovery, drug delivery dosage form designs, process
optimization, testing, and PK/PD studies” [3].
Kelsee Tignor et al “AI
technology for the pharmacy field, otherwise known as pharmacy intelligence,
can help streamline processes for clinical pharmacists, including making more
accurate and evidence-based EB clinical decisions through analyzing a large
amount of patient data, medical records, laboratories, and medication profiles”
[4].
Rayn Oswalt “Pharmacists
are highly concerned about patient safety and AI may help in this area. The
integration of AI technologies in pharmacy practice can help detect and prevent
medication errors, such as incorrect dosages or potential drug interactions DI,
thereby minimizing AEs and hospital readmissions” [5].
Praveen Halagali et al “The
review article also discusses the AI concepts and their applications,
particularly in developing solid dosage forms. Advanced algorithms optimize
formulation processes, predict PK profiles, and assess drug toxicity profiles,
facilitating a more efficient pathway from pilot study to market. This review
highlights the advancements in 3D printing technologies of dosage forms that
have the ability to provide personalized treatment PT to different individuals”
[6].
Ashutosh Kumar et al “Excipient
compatibility assessment using AI offers tremendous promise and potential for
enhancing the pharmaceutical development and manufacturing procedures” [7].
Mahroza Kanwal Khan et al “The
use of AI in predicting drug toxicity offers several advantages. This enables
the analysis of large data sets, allowing for a more complete understanding of
the complex interactions between the drugs and biological systems” [8].
Negar Mottaghi et al Drug
Formulation, Design, and Development. “AI algorithms evaluate data to predict
the stability and compatibility of pharmaceutical ingredients PI. This
technology can improve formulations for controlled release, optimize
bioavailability, and minimize side effects, enhancing the entire lifecycle of
pharmaceutical products” [9].
Andreea-Alexandra Mocrii et al “The
aim is to assist paediatricians in determining appropriate treatment doses for
children based on various parameters like age, weight, and other significant
factors” [10].
Muhammad Ahmer Raza et al “AI
involves the combination of human knowledge and resources with AI. As research
into AI continues, with many interesting applications of it in progress, one
may consider it a necessary evil even for those that see it as an enemy. It is
strongly recommended that pharmacists should acquire the relevant hard skills
that promote AI augmentation. Education about and exposure to AI is necessary
throughout all domains of pharmacy practice PP. Pharmacy students should be
introduced to the essentials of data science and fundamentals of AI through a
health informatics curriculum during their PharmD education. Pharmacists must
also be allowed to develop an understanding of AI through continuing education
CE. Data science courses or pharmacy residencies with a focus on AI topics
should be made available for pharmacists seeking more hands-on involvement in
AI development, governance, and use. As these technologies rapidly evolve, the
pharmacy education system PES must remain agile to ensure our profession is
equipped to steward these transformations of care” [11].
“The
literature search yielded 8796 articles. After removing duplicates and applying
the inclusion and exclusion criteria, 44 studies were included in the
qualitative synthesis. This review highlights the significant promise that AI
holds in health care, like as enhancing health care delivery by providing more
accurate diagnoses, personalized treatment plans, and efficient resource
allocation, persistent concerns remain, including biases ingrained in AI
algorithms, a lack of transparency in decision-making, potential compromises of
patient data privacy, and safety risks associated with AI implementation in the
clinical settings” [12].
Michela Ferrara et al “The results of the
present study highlighted the usefulness of AI not only for risk prevention in
clinical practice, but also in improving the use of an essential risk
identification tool, which is incident reporting IR” [13].
Nicole Kleinstreuer et al “Used
judiciously, AI has immense potential to advance toxicology into a more
predictive, mechanism-based, and evidence-integrated scientific discipline to
better safeguard human and environmental wellbeing across the diverse
populations” [14].
Mateusz LASKA et al “1
of the main risks associated with AI in the chemical industry CI is the possibility of human error. As AI
systems become increasingly sophisticated, they can become more difficult to
understand and operate, increasing the risk of errors and accidents. AI systems may also malfunction, leading to
unexpected results and a potential hazards” [15].
Mitul Harishbhai Tilala et al “the
multifaceted ethical considerations surrounding the use of AI and ML in health
care, including privacy and data security, algorithmic bias AB , transparency,
clinical validation, and professional responsibility. By critically examining
these ethical dimensions, stakeholders can navigate the ethical complexities of
AI and ML integration in health care, while safeguarding patient welfare and
upholding ethical principles” [16].
Timothy Tracy et al “3D
printing technology is very versatile in that a wide range of release profiles
can be created by controlling tablet structure. Customized appearance, size,
dose, and other characteristics of the dosage forms can be achieved by 3D
printing, resulting in patient centric designs. In early-stage development, 3D
printing technology can accelerate formulation development for pre-clinical
studies PCS and allows the production of small batches, including flexible
dose-adjustment, to facilitate pilot clinical studies” [17].
Cinzia Barberini et al “The
application is based on the interconnection of prescription-related aspects
(patients' and prescriber's details and prescription information PI). The
prescription name is linked to the list of substances, which allows to monitor
the stock levels. Inserting the daily dosage into the system, our personnel can
calculate the monthly supply of medicine. Each prescription contains specific
warnings on printable labels. A printed sheet, inclusive of label and checks on
final preparation, is produced for each prescription” [18].
Sasanka Sekhar Chanda et al “AI
systems can fail (a) if there are problems with its inputs comprising various
representations of data, sensor hardware, etc. and/or (b) if the processing
logic is deficient in some way and/or (c) if the repertoire of actions
available to the AI system is inadequate, i.e. if the output is inappropriate.
These problems/deficiencies/inadequacies originate from 2 kinds of
errors—commission and omission errors —in the design, development and
deployment of an AI system. These errors are: Error of commission: doing
something that should not have been done.
Error of omission: not doing something that should have been done” [19].
Karim Lekadir et al “This
study identified and clarifies seven main risks of AI in medicine and
healthcare: a) patient harm due to AI errors, b) the misuse of medical AI
tools, c) bias in AI and the perpetuation of existing Inequities, d) lack of
transparency, e) privacy and security issues, f) gaps in accountability, and g)
Obstacles in implementation. Each section, as summarised below, not only
describes the risk at hand, but also proposes potential mitigation measures”
[20].
Stefanie Beck, Manuel Kuhner et al “This
study work evaluated the suitability of Chat-GPT versions 3.5 and 4 for
healthcare professionals seeking up-to-date evidence and recommendations for
resuscitation by comparing the key messages of the resuscitation guidelines,
which methodically set the gold standard of current evidence / recommendations,
with the statements of the AI chatbots on this topic. In response to inquiries
about the 5 chapters, ChatGPT-3.5 generated a total of 60 statements, whereas
ChatGPT-4 produced 32 statements. ChatGPT-3.5 did not address 123 key messages,
and ChatGPT-4 did not address 132 of the 172 key messages of the ERC guideline
chapters. A total of 77% of the ChatGPT-3.5 statements and 84% of the ChatGPT-4
statements were fully in line with the ERC guidelines. The main reason for
nonconformity NC was superficial and incorrect AI statements” [21].
Meron W Shiferaw et al “Occasionally,
ChatGPT provided 2 completely different responses to the same question.
Overall, ChatGPT provided more accurate responses (8 out of 12) to the
"what" questions with less reliable performance to the
"why" and the "how"
questions. We identified errors in calculation, unit of measurement, and misuse
of protocols by ChatGPT. Some of these errors could result in clinical
decisions leading to harm. We also identified citations and references shown by
ChatGPT that did not exist in literature” [22].
Ronald Chow et al “A total of 600 consecutive questions were inputted into ChatGPT. ChatGPT 4o answered 72.2% questions correctly, whereas 3.5 answered 53.8% questions correctly. There was a significant difference in performance by question category (P < .01). ChatGPT performed poorer with respect to knowledge of landmark studies and treatment recommendations and planning. ChatGPT is a promising technology, with the latest version showing a marked improvement. Although it still has limitations, with further evolution, it may be considered a reliable resource for the medical training and decision making in the oncology space” (Table 1).
Tables 1: Summary (Galenic Lab Semi-Automatic Mixer).
Experimental
project
In this section various query (simple or more complex) was submitted to a famous AI tools (Chat bot) available free on the web: the response are then reported:
1. Digoxin
is water soluble? Response: it is poorly
soluble in water, more soluble in alchool.
2. What
is the molecular weight of NACL?
response : 58,44 g/ mol
3. The
Colliria must to be sterile : response yes it is fundamental to avoid eye infections
4. It
is compatible PROPRANOLOL with Cellulose microcrystalline? response yes
5. Is
omeprazole gastro sensible acid label? Response yes, it is inactivated in the
gastric (acid) environment.
6. What
is the chemical structure of the acid citric monohydrate? response:
7. Question:
lidocaine cloridrate is considered a poison by Italian pharmacopeia n. 3 tab? response
received :yes
8. Aceton
in inflammable? response: yes
9. What
is the galenic use of NIPAGIN? Response
: preservative
10. What
is the water solubility of amoxicillin? Response: the water solubility is not
so extremely high so it is needed specific formulation to improve body
absorption.
11. How
increase the solubility of a water insoluble active principles in galenic oral
drops? Response: Various strategies
like: to be used solvents like ethanolo, glycerin, propylene glycol or
surfactants, or cyclodextrin complexation, solubility enharcers (PEG), PH
adjustments, formulation of suspension.
12. How
to increase solubilisation of a solute in a solution? : response : increase
temperature, use a solvent, agitation, increase the surface area of the solute,
use surfactants, change the PH of the solution, use co-solvents, aplly pressure
(for gases), use complexants .
13. How
much grammes of KOH are to be weighted to prepare 100 ml solution at 30%?
response to be dissolved 30 gr intotal volume of 100 ml of solution
14. How
sub-ministrate drugs in children with difficulty in swallowing? Response: liquid
suspension, chewable tablets, dissolvable forms, powders or orally
disintegrating tablets, other liquid forms or suppository.
15. What
is he time needed to adequately mix Apis and excipients in galenic field using
a semi-automatic powder mixer to prepare capsules?: response:
Results on the 15 query 14 was considered as
acceptable. (6,7 % not acceptable in this test )
Discussion
At
today many are the application of AI that can be used in galenic field: from
the robots for oncologic lab to the software for the management of the
laboratory, the algorithm to verify incompatibility or the posology or toxicity
but there are many other topics of interests. As in other discipline like
medicine or technology the AI tools will be introduced to helps humans and the healthcare
professionals (also like in the pharmaceutical fields). Because in the pharma
worlds it is needed for regulatory and safety rules to follow strictly
requirements it is crucial to observe the kind of results that can be obtained
by the various AI instruments (robots, software chat bot and other available). But
In the pharma world is needed CERTANITY for the drugs production and use. AI
operates on probabilities and approximations. Even with vast amounts of data
and processing power, AI models cannot guarantee exact outcomes because they
are trained on historical data and predict future behaviours based on the
patterns. For this reason is needed to know the algorithm used or followed and
the kind (and %) of possible errors of this new technology. In the healthcare
fields some concepts are fundamental: continuous updating activity, digital
competencies and innovations, accuracy of the information’s.
According
Fip, the integration of AI in pharmacy require to the pharmacist to understand
not only the capacity of the new technology but also the limits, the quality of
the data, the normative conformity, the ethical consideration and the
infrastructural investments needed. The FIP guide on AI use in pharmacy
practice contribute to hold responsible the pharmacists to provide to the
patients safe assistance and taylored without compromise their critical
thinking or professional judgement. The response of the AI used in the
experimental project reported in this work provided a unique response and not
as the classic Searching engine: various response and from various source
reporting various point of view.
Related the experimental project: Between
15 scientific technical questions 14 response was substantially acceptable, one
with some peculiarity -error: in the chemical structure acid citric monohydrate
the AI instrument not provided a formula with 3 Carboxylic acid.
Between
dismantles of AI is possible to see
Lack
of AI Transparency and Explainability: AI and deep learning models can be
difficult to understand, even for those who work directly with this new
technology. Bias and fairness concerns in training data that may produce to
unequal treatment, misdiagnosis, or underdiagnoses of certain kind of
demographic groups. New regulatory and legal challenges that require navigating
complex regulatory frameworks. Possibility of Manipulation through AI
Algorithms, increased control systems (face recognizemend). Lack of data
privacy (due by explicit law that protect this), racial biases.
Loss
of human influence. Interoperability problems between existing healthcare
systems and the emerging data platforms.
Accountability concerns: To
identify what or who is responsible in the event of an error.
Resistance
to adoption by the healthcare professionals
Lack
of trust in AI-generated recommendations.
High
costs of development and implementation of AI
Lack
of emotions and creativity
Possibility
that this technology can reduce the critical thinking and judgment of
healthcare professionals.
Ethical concerns:
AI decisions that may conflict with the patient or family preferences.
Data
quality problems related to incomplete or inaccurate data. Potential
cybersecurity risks: ransomware, malware, data breaches, or privacy violations
and related malfunctions.
Conclusion
As
results of this work AI tools can be really useful in orientating also in
galenic practice but the findings of the query using AI chat bot must to be strictly verify under
a specific pharmaceutical requirement. Related the practical experience
performed in this work on the 15 query only 14 response was finded acceptable:
a results of 6,7% not acceptable is a sinificative percentage. This because the
safety and efficacy of the galenic product must to follow strictly normative
rules and for the health need of the patients. The uman verify of the results
obtained from a chat bot at today is mandatory for field like galenic activity.
Conflicts of Interest
The
author declare no conflicts of interest.
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